Electric vehicle charging optimization method, device and electronic equipment

Abstract

The application provides an electric vehicle charging optimization method, 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 an electric vehicle within a set period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost of the charging power curve through distributed power consumption within the set period; constructing a second objective function based on the power distribution network load curve and a historical load average curve of the power distribution network; the second objective function represents the minimum deviation of the load of the power distribution network from the historical load average of the power distribution network within the set period; solving the first objective function and the second objective function, and performing charging optimization control based on the solving results of the first objective function and the second objective function. The application solves the problems of low reliability and poor effect of the existing charging method for the power system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy consumption, in particular 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

[0002] With the rapid development of electric vehicle technology, the number of electric vehicles is also growing rapidly. Therefore, a large number of electric vehicles begin to charge in parallel. The randomness of electric vehicle charging during parallel charging will have a significant impact on the load of the power system, thereby bringing great challenges to the stable and reliable operation of the power system.

[0003] At present, the optimization means for electric vehicle charging period mainly adopts the strategy of peak-valley electricity price. However, the existing scheme still has the defects of low reliability and poor effect for the power system. SUMMARY

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

[0005] In order to achieve the above purpose, the embodiments of the present application provide an electric vehicle charging optimization method, comprising:

[0006] obtaining a charging power curve, a discharging power curve of an electric vehicle and a distribution network load curve in a set period;

[0007] constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost of the charging power curve corresponding to the distributed power consumption in the set period;

[0008] constructing a second objective function based on the distribution network load curve and the historical load average curve of the distribution network; the second objective function represents the minimum deviation of the load of the distribution network from the historical load average of the distribution network in the set period;

[0009] solving the first objective function and the second objective function, and performing charging optimization control based on the solving results of the first objective function and the second objective function.

[0010] Optionally, the distributed power source includes wind power generation, photovoltaic power generation and gas turbine power generation; the first objective function is constructed based on the charging power curve and the discharging power curve, comprising:

[0011] determining wind power, photovoltaic power and gas turbine power at each time point in the set time period based on the charging power at each time point in the charging power curve, wherein the charging power at each time point in the charging power curve is the same as the total power generated by the distributed power source; the total power generated by the distributed power source is equal to the sum of the wind power, the photovoltaic power and the gas turbine power;

[0012] calculating the electric vehicle charging cost based on the charging power curve, the discharging power curve, the charging price curve in the set time period and the discharging price curve in the set time period;

[0013] calculating the wind power cost based on the wind power, a unit operation and maintenance coefficient of the wind turbine and a unit management cost coefficient of the wind turbine;

[0014] calculating the photovoltaic power cost based on the photovoltaic power, a unit operation and maintenance coefficient of the photovoltaic turbine and a unit management cost coefficient of the photovoltaic turbine;

[0015] calculating the gas turbine power cost based on the gas turbine power, a power cost coefficient of the gas turbine and a unit operation and maintenance coefficient of the gas turbine;

[0016] constructing a first objective function based on the electric vehicle charging cost, the wind power cost, the photovoltaic power cost and the gas turbine power cost.

[0017] Optionally, the constructing the first objective function based on the electric vehicle charging cost, the wind power cost, the photovoltaic power cost and the gas turbine power cost comprises:

[0018] constructing the first objective function based on the electric vehicle charging cost, the wind power cost, the photovoltaic power cost, the gas turbine power cost, a first weight coefficient, a second weight coefficient and a third weight coefficient;

[0019] wherein the first weight coefficient represents a weight coefficient of the wind power cost; the second weight coefficient represents a weight coefficient of the photovoltaic power cost; and the third weight coefficient represents a weight coefficient of the gas turbine power cost.

[0020] Optionally, the constructing the second objective function based on the power distribution network load curve and the historical average load curve of the power distribution network comprises:

[0021] calculating the standard deviation of the power distribution network load in the set time period based on the power distribution network load curve and the historical average load curve of the power distribution network;

[0022] minimizing the standard deviation of the power grid load to construct the second objective function;

[0023] The power grid historical load average value curve comprises a power grid historical load average value at each time point in the set time period.

[0024] In another aspect, the embodiment of the present application also provides an electric vehicle charging optimization device, comprising:

[0025] The acquisition module is configured to acquire a charging power curve, a discharging power curve of the electric vehicle, and a power grid load curve in a set time period;

[0026] The 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 a minimized charging cost of the charging power curve corresponding to the distributed power consumption in the set time period;

[0027] The second construction module is configured to construct a second objective function based on the power grid load curve and a power grid historical load average value curve; the second objective function represents a minimized deviation of the load of the power grid from the power grid historical load average value in the set time period;

[0028] The control module is configured to solve the first objective function and the second objective function, and perform charging optimization control based on a solution result of the first objective function and a solution result of the second objective function.

[0029] Optionally, the distributed power includes wind power generation, photovoltaic power generation, and gas turbine power generation; the first objective function is constructed based on the charging power curve and the discharging power curve, comprising:

[0030] The wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power at each time point in the set time period are determined based on the charging power at each time point in the charging power curve; the charging power at each time point in the charging power curve is the same as the total distributed power generation power; the total distributed power generation power is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power;

[0031] The electric vehicle charging cost is calculated based on the charging power curve, the discharging power curve, a charging price curve in the set time period, and a discharging price curve in the set time period;

[0032] The wind power generation cost is calculated based on the wind power generation power, a wind power generator unit operation and maintenance coefficient, and a wind power generator unit management cost coefficient;

[0033] calculate the photovoltaic power generation cost based on the photovoltaic power generation power, a photovoltaic generator unit operation maintenance coefficient and a photovoltaic generator unit management cost coefficient;

[0034] calculate the gas turbine power generation cost based on the gas turbine power generation power, a gas turbine power generation cost coefficient and a gas turbine unit operation maintenance coefficient;

[0035] construct 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.

[0036] Optionally, the 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 comprises:

[0037] construct 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;

[0038] The first weight coefficient represents a weight coefficient of the wind power generation cost; the second weight coefficient represents a weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents a weight coefficient of the gas turbine power generation cost.

[0039] Optionally, the constructing the second objective function based on the power distribution network load curve and a power distribution network historical load average value curve comprises:

[0040] calculate a power distribution network load standard deviation in the set time period based on the power distribution network load curve and the power distribution network historical load average value curve;

[0041] minimize the power distribution network load standard deviation to construct the second objective function;

[0042] The power distribution network historical load average value curve comprises a power distribution network historical load average value at each time point in the set time period.

[0043] In another aspect, the present application also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the electric vehicle charging optimization method when executing the program.

[0044] In another aspect, the present application also provides a machine readable storage medium, which stores a computer program executable on a processor to implement the electric vehicle charging optimization method.

[0045] In another aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-mentioned electric vehicle charging optimization method.

[0046] By the above technical solution, the present application embodiment realizes the consideration of the accommodation of the distributed power supply in the charging cost calculation by constructing the first target function, and realizes the minimization of the deviation of the load of the distribution network from the average value of the historical load of the distribution network by constructing the second target function, thereby improving the stability of the power system, so that the present application embodiment improves the reliability and charging optimization effect of the electric vehicle charging optimization.

[0047] Other features and advantages of the present application embodiment will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings are included to provide a further understanding of the present application embodiment, and constitute a part of the specification, and are used together with the following specific implementation part to explain the present application embodiment, but do not constitute a limitation on the present application embodiment. In the drawings:

[0049] Figure 1 is a flowchart of the electric vehicle charging optimization method provided by the present application;

[0050] Figure 2 is a schematic diagram of the application scenario of the electric vehicle charging optimization method provided by the present application;

[0051] Figure 3 is a structural schematic diagram of the electric vehicle charging optimization device provided by the present application;

[0052] Figure 4 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0053] The specific implementation of the present application embodiment will be described in detail below in combination with the drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present application embodiment, and is not used to limit the present application embodiment.

[0054] Method embodiment

[0055] Please refer to Figure 1 The present application embodiment provides an electric vehicle charging optimization method, comprising:

[0056] Step 100, obtaining the charging power curve, the discharging power curve and the distribution network load curve of the electric vehicle in a set period.

[0057] Please refer to Figure 2The application scenario of the embodiment of the present application is similar to a public slow charging station near an industrial park or a residential area, and the ordered charging of an electric vehicle is optimized on this basis. A user drives an electric vehicle into the charging station and sets a predicted departure time. The charging pile power is fixed. As the control subject of this scenario, the electronic device responds to the time-of-use electricity price, integrates load prediction information and electric vehicle power information, and regulates the charging period of the charging pile. The electronic device needs to minimize the charging cost of the user and the load fluctuation of the power distribution network to the greatest extent on the basis of meeting the travel demand of the user, thereby playing the role of peak shaving and valley filling. The electric vehicle can be various vehicles using electric energy as energy, for example, the electric vehicle can be a pure electric vehicle, a range-extender hybrid vehicle and a plug-in hybrid vehicle.

[0058] The electronic device obtains the charging power curve, the discharging power curve and the power distribution network load curve of the electric vehicle in a set period. The set period can be various charging periods, for example, 8:00 to 10:00. It should be noted that the discharging power of the electric vehicle is also considered in the embodiment of the present application. The energy storage battery of the electric vehicle is connected to the power distribution network through the charging station. The charging pile in the V2G charging station is composed of two parts, generally an AC-DC circuit and a DC-DC circuit. When the grid load is high, the electric vehicle transmits the remaining power of the energy storage battery to the power distribution network through the V2G mode. The energy storage battery of the electric vehicle with charging and discharging capacity has the ability of active adjustment and flexible coordination, which can not only reduce the load peak-valley difference, but also greatly improve the power quality. The energy storage battery technology effectively reduces the energy change, so that the distributed energy can operate in the active power distribution network in a high penetration rate. At the same time, the dynamic balance of system power is coordinated to maintain the balance between power supply and demand on the power generation side and the demand side. The energy storage battery technology of the electric vehicle which can work independently in emergency can effectively improve the reliability of the power distribution network. For an industrial park, the income obtained by the user of the electric vehicle through discharging is the cost of charging the electric vehicle, plus the battery loss and maintenance cost.

[0059] Step 200, constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost of the charging power curve through distributed power consumption in the set period.

[0060] The embodiment of the present application needs to establish a multi-objective optimization function in the following form. ; . Wherein, 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 of x.

[0061] The distributed power supply in the embodiment of the present application can include at least one of wind power generation, photovoltaic power generation, and gas turbine power generation. That is, the distributed power supply in the embodiment of the present application can include any one of wind power generation, photovoltaic power generation, and gas turbine power generation; or the distributed power supply in the embodiment of the present application can include two or three of wind power generation, photovoltaic power generation, and gas turbine power generation.

[0062] In one embodiment, in order to consider the consumption of multiple new energies and improve the stability of the power distribution network, the distributed power supply in the embodiment of the present application includes wind power generation, photovoltaic power generation, and gas turbine power generation. The first objective function is constructed based on the charging power curve and the discharging power curve, including: determining the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power at each time in a set period based on the charging power at each time in the charging power curve; wherein the charging power at each time in the charging power curve is the same as the total power generation power of the distributed power supply; the total power generation power of the distributed power supply 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 price curve in the set period, and the discharging price curve in the set period; calculating the wind power generation cost based on the wind power generation power, the unit operation and maintenance coefficient of the wind turbine, and the unit management cost coefficient of the wind turbine; calculating the photovoltaic power generation cost based on the photovoltaic power generation power, the unit operation and maintenance coefficient of the photovoltaic turbine, and the unit management cost coefficient of the photovoltaic turbine; calculating the gas turbine power generation cost based on the gas turbine power generation power, the power generation cost coefficient of the gas turbine, and the unit operation and maintenance coefficient of the gas turbine; 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.

[0063] The embodiment of the present application considers the consumption of multiple new energies in the charging cost, that is, the charging power of each time in the charging power curve is equal to the sum of the wind power generation power, the photovoltaic power generation power, and the gas turbine power generation power. That is, . Wherein, represents the charging power at time t in the charging power curve, represents the wind power generation power at time t, , .

[0064] In some embodiments, the electric vehicle charging cost can be calculated based on the charging power curve, the discharging power curve, a charging price curve in the set time period, a discharging price curve in the set time period, a battery unit loss cost, and a battery unit maintenance cost. The charging price curve in the set time period includes a charging price at each time in the set time period. The discharging price curve in the set time period includes a discharging price at each time in the set time period. The battery unit loss cost represents a battery loss cost per unit power. The battery unit maintenance cost represents a battery maintenance cost per unit power. Specifically, the electric vehicle charging cost is calculated according to the following formula:

[0065] ;

[0066] wherein, is a charging price of the electric vehicle at time t in the charging price curve; is a discharging price of the electric vehicle at time t in the discharging price curve; is a charging power of the electric vehicle at time t in the charging power curve; is a discharging power of the electric vehicle at time t in the discharging power curve; is the battery unit loss cost; is the battery unit maintenance cost, and the time period from 1 to T represents the set time period; represents the charging power at time t in the charging power curve; C EV is the electric vehicle charging cost.

[0067] In some embodiments, the wind power generation cost is calculated based on the sum of a maintenance cost and a management cost. The maintenance cost of the wind power generation cost is calculated based on the wind power at each time in the set time period and a wind power generator unit operation maintenance coefficient. The management cost of the wind power generation cost is calculated based on the wind power at each time in the set time period and a wind power generator unit management cost coefficient. The wind power generator unit operation maintenance coefficient represents a wind power generator unit power operation maintenance coefficient. The wind power generator unit management cost coefficient represents a wind power generator unit power management cost coefficient. Specifically, the wind power generation cost is calculated according to the following formula: ; ; wherein is the maintenance cost of the wind power generation cost; is the wind power generator unit operation maintenance coefficient; is the management cost of the wind power generation cost; is the wind power generator unit management cost coefficient; is the wind power at time t; and the time period from 1 to T represents the set time period; CWT The wind power generation cost.

[0068] In some embodiments, the photovoltaic power generation cost is calculated based on a sum of a maintenance cost and a management cost. The maintenance cost of the photovoltaic power generation cost is calculated based on the photovoltaic power generation power at each time in a set time period and a photovoltaic power generator unit operation maintenance coefficient. The management cost of the photovoltaic power generation cost is calculated based on the wind power generation power at each time in the set time period and a photovoltaic power generator unit management cost coefficient. The photovoltaic power generator unit operation maintenance coefficient represents a photovoltaic power generator unit power operation maintenance coefficient. The photovoltaic power generator unit management cost coefficient represents a photovoltaic power generator unit power management cost coefficient. Specifically, the photovoltaic power generation cost is calculated by the following formula: . Wherein, is the maintenance cost of the photovoltaic power generation cost; is the photovoltaic power generator unit operation maintenance coefficient; is the management cost of the photovoltaic power generation cost; is the photovoltaic power generator unit management cost coefficient; is the photovoltaic power generation power at the t time; the time period of 1 to T represents the set time period; C PV The wind power generation cost.

[0069] In some embodiments, the gas turbine power generation cost is calculated based on a sum of a fuel cost and a management cost. The fuel cost of the gas turbine power generation cost is calculated based on the gas turbine power generation power and a gas turbine power generation cost coefficient. The management cost of the gas turbine power generation cost is calculated based on the gas turbine power generation power and a gas turbine unit operation maintenance coefficient. Specifically, the gas turbine power generation cost is calculated by the following formula:

[0070] . Wherein, is the gas turbine power generation power at the t time; e, f, g are the gas turbine power generation cost coefficient. is the gas turbine unit operation maintenance coefficient. The gas turbine unit operation maintenance coefficient represents a gas turbine unit power operation maintenance coefficient. C MT1 The fuel cost of the gas turbine power generation cost. C MT2 The management cost of the gas turbine power generation cost. C MT The gas turbine power generation cost. The time period of 1 to T represents the set time period.

[0071] ​​​​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, in order to allocate the cost of new energy consumption. The first objective function is constructed 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. The third weight coefficient represents the weight coefficient of the gas turbine power generation cost.

[0072] Specifically, the first objective function is represented by the following formula: ; wherein, C EV is the electric vehicle charging cost. C WT is the wind power generation cost. C PV is the photovoltaic power generation cost. C MT represents the gas turbine power generation cost. The time period of 1 to T represents a set time 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.

[0073] Therefore, the embodiment of the present application considers multiple new energy consumption in the charging cost calculation, reduces the influence of the charging behavior of the electric vehicle on the distribution network, and improves the stability of the distribution network (power system).

[0074] In addition, the first objective function also has a constraint condition. In one embodiment, the charging station needs to meet the travel demand of the user, that is, when the user leaves the charging station, the state of charge of the electric vehicle battery should be full. The embodiment of the present application constrains the charging time, the charging power, and the state of charge of the battery to avoid undercharging and overcharging during the charging process. Therefore, the constraint condition can include a charging time constraint condition, a charging power constraint condition, and a state of charge constraint condition of the electric vehicle battery. Specifically, the charging time constraint is: ; and are the arrival time and the expected departure time of the ith electric vehicle, respectively. The charging power constraint is: ; is the charging power of the ith electric vehicle at time t, is the maximum charging power of the electric vehicle. The SOC constraint of the electric vehicle battery is: ; ; . Wherein, and respectively represent the state of charge of the i-th electric vehicle at time t and t+1; is the charging efficiency of the electric vehicle; and respectively represent the initial state of charge of the i-th electric vehicle arriving at the charging station and the maximum state of charge of the electric vehicle; i represents that the electric vehicle needs to be fully charged when it leaves the charging station.

[0075] Step 300, constructing a second objective function based on the power distribution network load curve and the power distribution network historical load average value curve; the second objective function represents minimizing the deviation of the load of the power distribution network from the power distribution network historical load average value within the set period.

[0076] The electronic device can calculate the power distribution network load standard deviation or the power distribution network load difference based on the power distribution network load curve and the power distribution network historical load average value curve, so as to represent the deviation of the load of the power distribution network from the power distribution network historical load average value within the set period. In one embodiment, the second objective function is constructed based on the power distribution network load curve and the power distribution network historical load average value curve, including: calculating the power distribution network load standard deviation within the set period based on the power distribution network load curve and the power distribution network historical load average value curve; minimizing the power distribution network load standard deviation to construct the second objective function; wherein the power distribution network historical load average value curve includes the power distribution network historical load average value at each time within the set period. Specifically, the second objective function is represented by the following formula: ; wherein f 2 represents the second objective function, wherein P t is the load of the power distribution network at time t of the power distribution network historical load average value curve; P av is the power distribution network historical load average value at time t of the power distribution network historical load average value curve; wherein, P av It can be the load average value curve of the same period of the power distribution network. The time period of 1 to T represents the set period.

[0077] The embodiment of the present application minimizes the power distribution network load standard deviation in electric vehicle charging, thereby ensuring that the power distribution network load fluctuation is minimized in electric vehicle charging, thereby further improving the stability of the power distribution network (power system).

[0078] ​Solving the first objective function and the second objective function, and performing charging optimization control based on the solution result of the first objective function and the solution result of the second objective function.

[0079] By solving the first objective function and the second objective function, the wind power, the photovoltaic power, the gas turbine power at each time in the set period and the power grid load at each time are obtained, so that the charging optimization control is performed based on the above parameters. The embodiment of the application realizes the consideration of the accommodation of distributed power in the charging cost calculation by constructing the first objective function, and realizes the minimization of the deviation of the power grid load from the average value of the historical power grid load, thereby improving the stability of the power system, so that the reliability and the charging optimization effect of the electric vehicle charging optimization are improved.

[0080] Therefore, the embodiment of the application considers new energy accommodation, and provides an electric vehicle orderly charging optimization method for a power system, which is high in reliability, good in effect, objective and scientific. By building a charging scene, the charging cost of the user is minimized, and the standard deviation of the total power grid load is minimized, thereby improving the reliability and the charging optimization effect of the electric vehicle charging optimization.

[0081] Device embodiment

[0082] Please refer to Figure 3 On the other hand, the embodiment of the application further provides an electric vehicle charging optimization device, comprising:

[0083] The acquisition module 301 is configured to acquire a charging power curve, a discharging power curve and a power grid load curve of an electric vehicle in a set period.

[0084] The 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 of the charging power curve through distributed power accommodation in the set period.

[0085] The second construction module 303 is configured to construct a second objective function based on the power grid load curve and an average value curve of historical power grid load; the second objective function represents the minimization of the deviation of the power grid load from the average value of the historical power grid load in the set period.

[0086] 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 result of the first objective function and the solution result of the second objective function.

[0087] The embodiment of the present application realizes the consideration of the accommodation of the distributed power supply in the charging cost calculation by constructing a first target function, and realizes the minimization of the deviation of the load of the distribution network from the average value of the historical load of the distribution network and the improvement of the stability of the power system by constructing a second target function, thereby improving the reliability and charging optimization effect of the electric vehicle charging optimization.

[0088] Optionally, the distributed power supply includes wind power generation, photovoltaic power generation and gas turbine power generation; the first target function is constructed based on the charging power curve and the discharging power curve, including:

[0089] The wind power generation power, the photovoltaic power generation power and the gas turbine power generation power at each time in the set period are determined based on the charging power at each time in the charging power curve; the charging power at each time in the charging power curve is the same as the total power generation power of the distributed power supply; the total power generation power of the distributed power supply is equal to the sum of the wind power generation power, the photovoltaic power generation power and the gas turbine power generation power;

[0090] The electric vehicle charging cost is calculated based on the charging power curve, the discharging power curve, the charging price curve in the set period and the discharging price curve in the set period;

[0091] The wind power generation cost is calculated based on the wind power generation power, the unit operation and maintenance coefficient of the wind power generator and the unit management cost coefficient of the wind power generator;

[0092] The photovoltaic power generation cost is calculated based on the photovoltaic power generation power, the unit operation and maintenance coefficient of the photovoltaic power generator and the unit management cost coefficient of the photovoltaic power generator;

[0093] The gas turbine power generation cost is calculated based on the gas turbine power generation power, the power generation cost coefficient of the gas turbine and the unit operation and maintenance coefficient of the gas turbine;

[0094] The first target 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.

[0095] Optionally, the first target 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, including:

[0096] The first target function is constructed 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;

[0097] The first weight coefficient represents a weight coefficient of the wind power generation cost; the second weight coefficient represents a weight coefficient of the photovoltaic power generation cost; and the third weight coefficient represents a weight coefficient of the gas turbine power generation cost.

[0098] Optionally, the second objective function is constructed based on the power distribution network load curve and a historical average load curve of the power distribution network, and the constructing the second objective function includes:

[0099] The historical average load curve of the power distribution network includes a historical average load value of the power distribution network at each time point in the set time period.

[0100] The second objective function is constructed by minimizing the standard deviation of the power distribution network load.

[0101] The historical average load curve of the power distribution network includes a historical average load value of the power distribution network at each time point in the set time period.

[0102] The electric vehicle charging optimization device includes a processor and a memory, and the acquisition module 301, the first construction module 302, the second construction module 303 and the control module 304 are stored in the memory as program units, and the corresponding functions are realized by the processor executing the program units stored in the memory.

[0103] The processor includes a core, and the core calls the corresponding program units from the memory. The core can be provided with one or more than one.

[0104] The memory can include a non-permanent memory in a computer readable medium, a random access memory (RAM) and / or a non-volatile memory such as a read-only memory (ROM) or a flash memory (flash RAM), and the memory includes at least one memory chip.

[0105] Figure 4 An example of an electronic device is shown in the schematic diagram of the physical structure of the electronic device, as shown in Figure 4As shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communications bus 440. The processor 410 can invoke a logical instruction in the memory 430 to execute an electric vehicle charging optimization method, which includes: acquiring a charging power curve, a discharging power curve, and a power grid load curve of an electric vehicle within a set period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents the minimum charging cost of accommodating the charging power curve through a distributed power source within the set period; constructing a second objective function based on the power grid load curve and a historical load average curve of the power grid; the second objective function represents the minimum deviation of the load of the power grid from the historical load average of the power grid within the set period; solving the first objective function and the second objective function, and performing charging optimization control based on the solving results of the first objective function and the second objective function.

[0106] In addition, the logical instruction in the memory 430 described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0107] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a machine readable storage medium, and the computer program can be executed by a processor to enable a computer to perform an electric vehicle charging optimization method, which comprises: obtaining a charging power curve, a discharging power curve of an electric vehicle and a power grid load curve in a set period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents a minimized charging cost corresponding to the charging power curve absorbed by a distributed power source in the set period; constructing a second objective function based on the power grid load curve and a historical average load curve of the power grid; the second objective function represents a minimized deviation of the load of the power grid from the historical average load of the power grid in the set period; solving the first objective function and the second objective function, and performing charging optimization control based on the solving results of the first objective function and the second objective function.

[0108] In another aspect, the present application also provides a machine readable storage medium, which stores a computer program, and the computer program can be executed by a processor to enable a computer to perform an electric vehicle charging optimization method, which comprises: obtaining a charging power curve, a discharging power curve of an electric vehicle and a power grid load curve in a set period; constructing a first objective function based on the charging power curve and the discharging power curve; the first objective function represents a minimized charging cost corresponding to the charging power curve absorbed by a distributed power source in the set period; constructing a second objective function based on the power grid load curve and a historical average load curve of the power grid; the second objective function represents a minimized deviation of the load of the power grid from the historical average load of the power grid in the set period; solving the first objective function and the second objective function, and performing charging optimization control based on the solving results of the first objective function and the second objective function.

[0109] The apparatus embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0110] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0111] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An optimized charging method for electric vehicles, characterized in that, include: Obtain the charging power curve, discharging power curve, and power distribution network load curve of electric vehicles 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 minimum charging cost corresponding to the charging power curve being absorbed by distributed power sources within the set time period. A second objective function is constructed based on the distribution network load curve and the historical average load curve of the distribution network; the second objective function represents minimizing the deviation between the distribution network load and the historical average load of the distribution network within the set time period; Solve for 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 second objective function; The distributed power sources include wind power generation, photovoltaic power generation, and gas turbine power generation; the construction of the first objective function based on the charging power curve and the discharging power curve includes: The wind power generation, photovoltaic power generation, and gas turbine power generation at each moment within a set time period are determined 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 of the distributed power source; the total power generation of the distributed power source is equal to the sum of the wind power generation, the photovoltaic power generation, and the gas turbine power generation. The charging cost of an electric vehicle is calculated based on the charging power curve, the discharging power curve, the charging price curve within the set time period, and the discharging price curve within the set time period. Based on the wind power generation capacity, the unit operation and maintenance coefficient of the wind turbine generator, and the unit management cost coefficient of the wind turbine generator, calculate the cost of wind power generation. The photovoltaic power generation cost is calculated based on the photovoltaic power generation capacity, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient. The gas turbine power generation cost is calculated based on the gas turbine's power generation capacity, power generation cost coefficient, and unit operation and maintenance coefficient. Based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost, a first objective function is constructed; The first objective function is expressed by the following formula: ;in, C EV The cost of charging electric vehicles; C WT Cost of wind power generation; C PV Cost of photovoltaic power generation; C MT The value represents the cost of gas turbine power generation; the time period from 1 to T represents the set time period; a1 is the first weighting coefficient; a2 is the second weighting coefficient; a3 is the third weighting coefficient; and f1 is the first objective function.

2. The electric vehicle charging optimization method according to claim 1, characterized in that, The 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, including: Based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, a first objective function is constructed; Wherein, the first weighting coefficient represents the weighting coefficient of the wind power generation cost; the second weighting coefficient represents the weighting coefficient of the photovoltaic power generation cost; and the third weighting coefficient represents the weighting coefficient of the gas turbine power generation cost.

3. The electric vehicle charging optimization method according to claim 2, characterized in that, The construction of the second objective function based on the distribution network load curve and the historical average load curve of the distribution network includes: Based on the distribution network load curve and the historical average load curve of the distribution network, calculate the standard deviation of the distribution network load within the set time period; Minimize the standard deviation of the distribution network load to construct the second objective function; The historical load average curve of the distribution network includes the historical load average of the distribution network at each moment within the set time period.

4. An electric vehicle charging optimization device, characterized in that, include: The acquisition module is used to acquire the charging power curve, discharging power curve, and power distribution network load curve of electric vehicles within a set time period. The first construction module is used 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 absorbed by distributed power sources within the set time period. The second construction module is used to construct a second objective function based on the distribution network load curve and the historical average load curve of the distribution network; the second objective function characterizes minimizing the deviation between the distribution network load and the historical average load of the distribution network within the set time period; The control module is used to solve the first objective function and the second objective function, and to perform charging optimization control based on the solution results of the first objective function and the second objective function; The distributed power sources include wind power generation, photovoltaic power generation, and gas turbine power generation; the construction of the first objective function based on the charging power curve and the discharging power curve includes: The wind power generation, photovoltaic power generation, and gas turbine power generation at each moment within a set time period are determined 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 of the distributed power source; the total power generation of the distributed power source is equal to the sum of the wind power generation, the photovoltaic power generation, and the gas turbine power generation. The charging cost of an electric vehicle is calculated based on the charging power curve, the discharging power curve, the charging price curve within the set time period, and the discharging price curve within the set time period. Based on the wind power generation capacity, the unit operation and maintenance coefficient of the wind turbine generator, and the unit management cost coefficient of the wind turbine generator, calculate the cost of wind power generation. The photovoltaic power generation cost is calculated based on the photovoltaic power generation capacity, the photovoltaic generator unit operation and maintenance coefficient, and the photovoltaic generator unit management cost coefficient. The gas turbine power generation cost is calculated based on the gas turbine's power generation capacity, power generation cost coefficient, and unit operation and maintenance coefficient. Based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, and the gas turbine power generation cost, a first objective function is constructed; The first objective function is expressed by the following formula: ;in, C EV Cost of charging electric vehicles; C WT Cost of wind power generation; C PV Cost of photovoltaic power generation; C MT The value represents the cost of gas turbine power generation; the time period from 1 to T represents the set time period; a1 is the first weighting coefficient; a2 is the second weighting coefficient; a3 is the third weighting coefficient; and f1 is the first objective function.

5. The electric vehicle charging optimization device according to claim 4, characterized in that, The 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, including: Based on the electric vehicle charging cost, the wind power generation cost, the photovoltaic power generation cost, the gas turbine power generation cost, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient, a first objective function is constructed; Wherein, the first weighting coefficient represents the weighting coefficient of the wind power generation cost; the second weighting coefficient represents the weighting coefficient of the photovoltaic power generation cost; and the third weighting coefficient represents the weighting coefficient of the gas turbine power generation cost.

6. The electric vehicle charging optimization device according to claim 5, characterized in that, The construction of the second objective function based on the distribution network load curve and the historical average load curve of the distribution network includes: Based on the distribution network load curve and the historical average load curve of the distribution network, calculate the standard deviation of the distribution network load within the set time period; Minimize the standard deviation of the distribution network load to construct the second objective function; The historical load average curve of the distribution network includes the historical load average of the distribution network at each moment within the set time period.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the electric vehicle charging optimization method according to any one of claims 1 to 3.

8. A machine-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the electric vehicle charging optimization method according to any one of claims 1 to 3.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the electric vehicle charging optimization method according to any one of claims 1 to 3.

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