CN116151865A - Charging calculation method, system, equipment and storage medium for photovoltaic energy storage charging station - Google Patents
Charging calculation method, system, equipment and storage medium for photovoltaic energy storage charging station Download PDFInfo
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Abstract
The embodiment of the application discloses a charging calculation method, a charging calculation system, charging calculation equipment and a charging calculation storage medium for a photovoltaic energy storage charging station. According to the technical scheme provided by the embodiment of the application, the historical data are input into the preset first model for data processing to obtain the charge quantity and the parking quantity, wherein the historical data comprise the historical charge charging price and the historical parking charging price; and inputting the charge quantity and the parking quantity into a preset second model, and performing data processing by taking the total storage quantity as a constraint condition to obtain a charge charging price and a parking charging price in a corresponding time period, wherein the total storage quantity comprises solar energy generated energy and night storage quantity, and the charge charging price and the parking charging price in the corresponding time period are displayed on corresponding client software so as to remind a user of paying attention, so that the problem of non-ideal comprehensive benefits of the charging station can be solved, and the comprehensive benefits of the charging station are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of photovoltaic charging stations, in particular to a charging calculation method, a charging calculation system, charging calculation equipment and a charging calculation storage medium for a photovoltaic energy storage charging station.
Background
The energy is an important material foundation for survival and development of human society, each important progress of human civilization is accompanied by important transformation of the energy, and the development of new energy is a necessary trend.
The transportation field consumes more petroleum resources and is easy to cause greenhouse gases, so that the electric automobile is widely popularized. Most of the existing electric vehicles are charged through charging stations, and the charging stations charge through municipal power grids, wherein electric energy is required to be purchased to the municipal power grids when the electric vehicles are charged through the municipal power grids.
Traditional adoption municipal administration electric wire netting charges the mode to the charging station, generally adopts fixed charge. The fixed charge is adopted, and the charging and parking time cannot be guided to change along with the change of the number of electric vehicles to be charged, so that the comprehensive benefit of the charging station is not ideal.
Disclosure of Invention
The embodiment of the application provides a charging calculation method, a charging calculation system, charging calculation equipment and a storage medium for a photovoltaic energy storage charging station, which can solve the problem of non-ideal comprehensive benefits of the charging station and improve the comprehensive benefits of the charging station.
In a first aspect, an embodiment of the present application provides a charging calculation method for a photovoltaic energy storage charging station, including:
Inputting historical data into a preset first model for data processing to obtain a charging amount and a parking quantity, wherein the historical data comprises a historical charging price and a historical parking charging price;
inputting the charge quantity and the parking quantity into a preset second model, and performing data processing by taking the total charge quantity as a constraint condition to obtain a charge charging price and a parking charging price in a corresponding time period, wherein the total charge quantity comprises solar energy power generation quantity and night charge quantity;
and displaying the charging and charging prices and the parking charging prices of the corresponding time periods on corresponding client software so as to remind the user of paying attention.
Further, the step of inputting the history data into a preset first model to perform data processing to obtain the charge amount and the parking number includes:
inputting the historical quick charge charging price and the historical quick charge parking charging price into a preset first quick charge model for data processing to obtain corresponding quick charge quantity and quick charge parking quantity;
and inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model for data processing to obtain the corresponding slow charge amount and slow charge parking quantity.
Further, the step of inputting the historical quick charge charging price and the historical quick charge parking charging price into a preset first quick charge model for data processing to obtain a corresponding quick charge amount and a corresponding quick charge parking number includes:
charging Price of historical quick charge f.c (t-1) and historical fast charging parking charging Price f. (t-1) inputting a preset first quick charge model:
(Power f.c (t),N f. (t))＝μ θ (Price f. (t),Price f. (t-1),Price f. (t),Price f.p (t-1)), wherein μ θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first quick charge model, and outputting a quick charge amount Power f.c (t) and fast charge parking quantity N f. (t)；
The method for processing the data by inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model to obtain the corresponding slow charge amount and the slow charge parking quantity comprises the following steps:
charging Price of historical slow charging s. (t-1) and historical Low charging parking charging Price s. (t-1) inputting a preset first slow charge model (Power) s. (t),N s. (t))＝μ θ (Price s. (t),Price s. (t-1),Price s. (t),Price s. (t-
1) In which μ is θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first slow charge model, and outputting a corresponding slow charge amount Power s. (t) and slow charge parking quantity N s. (t)。
Further, the inputting the charge amount and the parking number into a preset second model, and performing data processing with the total charge amount as a constraint condition to obtain a charge price and a parking charge price of a corresponding time period, including:
inputting the fast charge amount, the fast charge parking number, the slow charge amount and the slow charge parking number into a preset second model;
and in the preset second model, taking the total storage capacity as a constraint condition, and taking the total income as an optimization target to perform data processing so as to obtain the quick charge price, the slow charge price, the quick charge parking charge price and the slow charge parking charge price of the corresponding time period.
Further, the inputting the charge amount and the parking number into a preset second model, and performing data processing with the total charge amount as a constraint condition to obtain a charge price and a parking charge price of a corresponding time period, including:
will fill charge Power soon f.c (t), number of fast charging stops N f. (t), slow charge amount Power s. (t) and slow charge parking quantity N s. (t) inputting a preset second model
In which Income represents total Income, price f. Charging price for quick charge, power f.c Is a quick charge amount; price s. Charging price for slow charge, power s. Is a slow charge amount; price f. Stop for quick chargePrice of vehicle charge, N f. To charge the number of stops quickly, price s. Charge price for slow charge parking, N s. The number of slow charging and stopping is the number of slow charging and stopping;
to be used for
And N f. +N s. <N is a constraint condition for data processing to obtain a Price of quick charge and charge in a corresponding time period f. Price for slow charging s. Price for quick charging parking f.p And Price for slow charge parking s. Where Power represents the total charge capacity and N represents the total number of charging station parking spaces.
Further, the step of inputting the history data into a preset first model for data processing to obtain the charge amount and the parking number includes:
performing prediction processing according to a photovoltaic power generation prediction model to obtain solar power generation capacity;
and carrying out summation treatment according to the solar energy generating capacity and the night storage capacity to obtain the total storage capacity.
Further, the inputting the charge amount and the parking number into the second model, performing data processing with the total charge amount as a constraint condition, and obtaining a charge price and a parking price of the corresponding time period, includes:
updating historical data at preset time intervals;
And carrying out corresponding data processing according to the updated historical data, the preset first model, the photovoltaic power generation prediction model and the preset second model to obtain the charging price and the parking charging price of the new time period.
In a second aspect, embodiments of the present application provide a photovoltaic energy storage charging station system comprising: the intelligent charging system comprises a solar photovoltaic panel, a rectifying device, a photovoltaic storage battery, an off-peak electricity storage battery, an intelligent charging control center and a charging pile, wherein the charging pile comprises at least one fast charging pile and at least one slow charging pile;
the solar photovoltaic panel is connected with the photovoltaic storage battery and the intelligent charging control center;
the photovoltaic storage battery is connected with the off-peak electricity storage battery and the intelligent charging control center;
the off-peak electricity storage battery is connected with the rectifying device and the intelligent fast charging control center, and the rectifying device is used for being connected to a municipal power grid;
the intelligent charging control center is also connected with the charging pile and used for transmitting electric energy of the photovoltaic storage battery and the off-peak electricity storage battery to the charging pile.
In a third aspect, embodiments of the present application provide a photovoltaic energy storage charging station charging computing device, comprising: a memory and one or more processors;
The memory is used for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the photovoltaic energy storage charging station charging calculation method as described in the first aspect.
In a fourth aspect, embodiments of the present application provide a storage medium storing computer executable instructions that when executed by a computer processor are for performing the photovoltaic energy storage charging station charging calculation method as described in the first aspect.
According to the method, the charging amount and the parking quantity are obtained by inputting the historical charging price and the historical parking price into the preset first model for data processing, the charging amount and the parking quantity are input into the preset second model, the total storage capacity is used as a constraint condition for data processing, the charging price and the parking price of the corresponding time period are obtained, and the charging price and the parking price of the corresponding time period are displayed on the corresponding client software so as to remind a user of paying attention. By adopting the technical means, the obtained charging and charging prices and parking and charging prices in the corresponding time periods can be displayed on the corresponding client software to guide the user to select the corresponding charging time length, so that the problem that comprehensive benefits of the charging station are not ideal can be avoided, the parking and charging prices or charging and charging prices can be improved when the parking quantity is more, the user is guided to select shorter charging time length to save the cost, and the parking and charging prices or charging and charging prices can be improved on the basis of ensuring the total charging time length of the charging station, so that the comprehensive benefits of the charging station are improved. In addition, when the parking quantity is small, the parking charge price or the charging charge price is reduced, the user is guided to select longer charging time, the idle parking space and idle time when the parking quantity is small are reduced, and although the parking charge price or the charging charge price is reduced, the charging time of the user is prolonged, so that the total parking charge and the total charging charge are increased, and the comprehensive benefit of the charging station is further improved.
Drawings
Fig. 1 is a flowchart of a charging calculation method of a photovoltaic energy storage charging station provided in an embodiment of the present application;
fig. 2 is a flowchart of another charging calculation method of a photovoltaic energy storage charging station according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a charging calculation device of a photovoltaic energy storage charging station according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a photovoltaic energy storage charging station system according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a charging computing device of a photovoltaic energy storage charging station according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following detailed description of specific embodiments thereof is given with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the matters related to the present application are shown in the accompanying drawings. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The electric vehicle charging station has a lot of differences from the traditional fuel vehicle charging station in terms of the use mode, and the traditional fuel vehicle charging process is generally short in time consumption, so that the residence time of the fuel vehicle in the charging station is short; the charging time of the electric vehicle is longer, if the direct current quick charging is used, the charging time generally needs 0.5-1.0 hours, and if the alternating current slow charging is used, the charging time is as long as several hours or even more than 10 hours, so that the electric vehicle charging station needs a fixed parking space to meet the charging requirement. Thus, the electric vehicle charging station can provide two functions, one being a charging function and the other being a parking function.
The conventional electric vehicle charging station has the following problems in charging, a municipal power grid is adopted as a power supply of an electric vehicle charging pile, and with the increase of electric vehicles and the charging pile, huge electricity load and impact are brought to the municipal power grid, and when the number of the electric vehicles is increased and gradually increased, the existing power grid is difficult to bear the electricity load; the charging load of the electric vehicle is a nonlinear load, and the electric energy quality of the municipal power grid can be influenced in the charging process; most of the charging power consumption is still consumed in the peak period of daytime power consumption, the electric energy in the valley period of night power consumption cannot be fully utilized, and the charging cost is high.
The traditional electric vehicle charging station has the following problems in the aspect of parking, as the traditional electric vehicle charging station adopts municipal power supply to supply power, the electric power supply is sufficient, so that the traditional charging station mainly has a charging function, and most of charging piles are direct-current quick charging piles, so that the charging time is shortened, the parking charging is mainly used for avoiding the long-time parking occupation of vehicles, and a large number of idle parking spaces can be generated when fewer charging vehicles are charged, so that the parking function of the electric vehicle charging station cannot be effectively utilized.
The embodiment of the application provides a mode of using solar photovoltaic to combine night off-peak electricity energy storage, adopts solar photovoltaic to provide electric power, and the off-peak electricity energy storage at night is assisted in use, avoids using electric network power in daytime electricity utilization peak section or flat peak section, can solve traditional electric vehicle charging station and strike urban electric network and get the electric problem from the electric network at peak time. However, by adopting a mode of combining solar energy photovoltaic with night off-peak electricity energy storage, the power supply is derived from the solar energy photovoltaic and the night off-peak electricity energy storage, and the daily power supply depends on the solar radiation intensity and the energy storage capacity, so that the power requirement of full-load direct current rapid charging in the whole day cannot be met.
According to the photovoltaic energy storage charging station charging calculation method, system, equipment and storage medium, when charging calculation is carried out, data processing is carried out in a preset first model through historical charging price and historical parking charging price, so that charging quantity and parking data quantity are obtained, data processing is carried out in a preset second model according to the obtained charging quantity and parking quantity by taking total storage capacity as constraint conditions, charging price and parking charging price of a corresponding time period are obtained, charging can be carried out according to the obtained charging price and parking charging price, comprehensive benefits of a charging station are maximized, charging rationality of the photovoltaic energy storage charging station is improved, and comprehensive benefits of the charging station are improved. Compared with the traditional charging station charging calculation mode, the charging station charging method is characterized in that a municipal power grid is generally adopted for charging the charging station, and a fixed charging price is generally adopted on the basis of no restriction of power capacity. With a fixed price charge, the overall benefit maximization may not be achieved. Based on the method, the charging calculation method of the photovoltaic energy storage charging station is provided, so that the problem that comprehensive benefits of the existing charging station are not ideal is solved.
Fig. 1 shows a flowchart of a charging calculation method of a photovoltaic energy storage charging station provided in the embodiment of the present application, where the charging calculation method of the photovoltaic energy storage charging station provided in the embodiment may be performed by a charging calculation device of the photovoltaic energy storage charging station, and the charging calculation device of the photovoltaic energy storage charging station may be implemented by software and/or hardware, and the charging calculation device of the photovoltaic energy storage charging station may be formed by two or more physical entities or may be formed by one physical entity. In general, the photovoltaic energy storage charging station charging computing device may be a terminal device, such as a computer device or the like.
The following description will take a computer device as an example of a main body for executing the charge calculation method of the photovoltaic energy storage charging station. Referring to fig. 1, the charging calculation method of the photovoltaic energy storage charging station specifically includes:
s101, inputting historical data into a preset first model for data processing to obtain a charging amount and a parking number, wherein the historical data comprises a historical charging price and a historical parking charging price.
Since the charge amount and the parking amount are affected by the charge price and the parking charge price, it can be expressed as: power f.c ＝f(Price f. ,Price f.p )；N f. ＝f(Price f. ,Price f.p )；Power s. ＝f(Price s. ,Price s. )；N s. ＝f(Price s. ,Price s. ) Wherein Power is f.c Representing the amount of charge, price f. Representing the Price of charge of quick charge and Price f.p Representing the charge price of the fast-charging parking, N f. Representing the number of fast-charging stops, power s. Representing the slow charge amount, price s. Representing the Price of charge of slow charge and Price s. Represents the price of slow charge parking charge, N s. Representing the number of slow fill stops. Therefore, this is a complex nonlinear optimization problem for which a preset first model of the charge amount and the parking amount and the charge price and the parking price needs to be established. The first preset model comprises a first quick charge model and a first slow charge model.
And inputting the historical quick charge charging price and the historical quick charge parking charging price into a preset first quick charge model for data processing to obtain corresponding quick charge quantity and quick charge parking quantity. And inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model for data processing to obtain the corresponding slow charge amount and slow charge parking quantity.
The preset first fast charging model is expressed as:
(Power f.c (t),N f. (t))＝μ θ (Price f. (t),Price f. (t-1),Price f. (t),Price f.p (t-1)), where (Price) f.c (t) represents the Price, and the like of the quick charge at time t f. (t-1) quick Charge charging Price, price at t-1 h f. (t) represents the Price, and the like of the quick charge parking charge at the t-th hour f. (t-1) represents the fast charge parking charge price, mu, at t-1 hours θ Represents a neural network, θ represents a model parameter set of the neural network, power f.c (t) represents the quick charge amount at the t-th hour, N f. And (t) represents the number of fast-charge stops at the t-th hour.
The method comprises the steps of presetting a first slow charging model table as follows:
(Power s. (t),N s. (t))＝μ θ (Price s. (t),Price s. (t-1),Price s. (t),Price s. (t-1)), where Price s. (t) represents the Price, and the like of the slow charge at the t-th hour s. (t-1) charging Price, price by slow charging at t-1 h s. (t) represents the Price of the slow charge parking charge at time t, price s. (t-1) represents the slow charge parking charge price, mu, at t-1 hours θ Represents a neural network, θ represents a model parameter set of the neural network, power s. (t) represents the slow charge amount at t hours, N s. And (t) represents the number of slow charge stops at the t-th hour.
In one embodiment, the historical fast charge charges Price f. (t-1) and historical fast charging parking charging Price f. (t-1) inputting a preset first quick charge model: (Power) f.c (t),N f. (t))＝μ θ (Price f. (t),Price f. (t-1),Price f.p (t),Price f.p (t-1)), wherein μ θ Representing a neural network, wherein θ represents a model parameter set of the neural network, performing data processing according to the preset first quick charge model, and outputting a quick charge amount Power f.c (t) and fast charge parking quantity N f. (t). Charging Price of historical slow charging s. (t-1) and historical slow charging stopPrice for vehicle s. (t-1) inputting a preset first slow charge model (Power) s.c (t),N s. (t))＝μ θ (Price s. (t),Price s. (t-1),Price s. (t),Price s. (t-1)), wherein μ θ Represents a neural network, θ represents a set of model parameters for the neural network. Data processing is carried out according to a preset first slow charge model, and a corresponding slow charge amount Power is output s. (t) and slow charge parking quantity N s. (t)。
S102, inputting the charge quantity and the parking quantity into a preset second model, and performing data processing by taking the total charge quantity as a constraint condition to obtain a charge charging price and a parking charging price in a corresponding time period, wherein the total charge quantity comprises solar energy generating capacity and night charge quantity.
The application provides an embodiment that adopts solar photovoltaic to provide electric power and assist and utilize off-peak electricity accumulation at night, adopts solar photovoltaic to generate electricity and combines off-peak energy accumulation at night, provides electric power for electric vehicle charging station. The power generation time of solar photovoltaic power generation is 8-18 points per day, and the night valley electricity storage time is 0-6 points per day. And carrying out prediction processing according to the photovoltaic power generation prediction model to obtain the solar power generation amount. The solar energy generating capacity in each hour can be predicted by the photovoltaic power generation prediction model. And carrying out summation treatment according to the solar energy generating capacity and the night storage capacity to obtain the total storage capacity.
In one embodiment, the formula is according to:
calculating the total charge capacity, wherein Power represents the total charge capacity, power solar Representing solar energy Power generation storage Representing the amount of charge stored at night.
The preset second model is expressed as:
wherein Income represents total Income, price f.c Charging price for quick charge, power f.c Is a quick charge amount; price s. Charging price for slow charge, power s. Is a slow charge amount; price f.p Charge price for fast charging and parking, N f. The number of the fast-charging stops; price s. Charge price for slow charge parking, N s. Is the slow charge parking quantity. Will fill charge Power soon f. (t), number of fast charging stops N f. (t), slow charge amount Power s. (t) and slow charge parking quantity N s. (t) inputting into a preset second model to +.>
And N f. +N s. <N is a constraint condition, and the total income is used as an optimization target to perform data processing to obtain the Price of quick charge and charge in the corresponding time period f. Price for slow charging s. Price for quick charging parking f. And Price for slow charge parking s. Where Power represents the total charge capacity and N represents the total number of charging station parking spaces.
Optimizing the first model and the second model, and charging the Price of the quick charge f. Price for slow charging s. Price for quick charging parking f.p And Price for slow charge parking s. The adjustment is performed once per hour, and thus the optimization result is output once per hour.
In an embodiment, the historical data is updated at intervals of a preset time interval, and corresponding data processing is performed according to the updated historical data, a preset first model, a photovoltaic power generation prediction model and a preset second model, so that the charging and charging price and the parking and charging price of a new time period are obtained.
Illustratively, after the training of the preset first model, the photovoltaic power generation prediction model and the preset second model is completed, the optimized charging price in the future 6 hours is output by taking the total income of the day as the maximum optimization target, and the charging price output in the future 6 hours is optimized by rolling every hour. The price to be charged is output for the last time.
And S103, displaying the charging and charging price and the parking charging price of the corresponding time period on corresponding client software so as to remind a user of paying attention.
The optimization processing of each hour is carried out to obtain the Price of the quick charge and charge f. Price for slow charging s. Price for quick charging parking f. And Price for slow charge parking s. And displaying the information on the software of the corresponding client for the user to view. The user charges Price according to the quick charge corresponding to the current time period f. Price for slow charging s. Price for quick charging parking f. And Price for slow charge parking s. And selecting a corresponding charging mode and charging duration.
In one embodiment, when an increase in the number of stops is predicted, the duration of one round of charging may be very long if the charging charge price is low, resulting in a failure of the subsequent vehicle to charge, resulting in a reduction in the total revenue of the charging station. At this time, the Price of the slow charge can be obtained by processing the model s. And Price for slow charge parking s. More users are guided to select a quick charging mode, and the vehicle replacement frequency based on the quick charging mode is faster than that of a slow charging mode, so that the total number of vehicles in the quick charging mode is increased, the total income of the quick charging mode is increased, and the total income of the charging station is further improved. Similarly, if the number of predicted stops is small, the Price is charged by slow charging s. And Price for slow charge parking s. Higher, then the user can select quick charge mode and then lead to the charging station to appear more idle parking stalls and correspond the idle time that has prolonged idle parking stall to influence the comprehensive income of charging station. Therefore, the Price of the slow charge can be reduced when the predicted parking quantity is smaller s. And Price for slow charge parking s. The user is guided to select the charging mode of slow charging, the charging duration is longer based on slow charging, the corresponding parking duration is longer, and therefore final charging and parking charging are relatively increased, and comprehensive benefits of the charging station are improved.
The charging price and the parking price of the user are based on the charging price and the parking price of the time period corresponding to the charging time when the user starts to charge, and the charging price and the parking price of the following charging station are adjusted so as not to influence the charging price and the parking price of the user. When the charging time of the user spans two hours, the charging price and the parking charging price of the whole process are charged according to the charging price and the parking charging price corresponding to the charging starting time of the user.
On the other hand, referring to fig. 2, another charging calculation method for a photovoltaic energy storage charging station according to an embodiment of the present application is provided. The charging calculation method of the photovoltaic energy storage charging station is correspondingly executed by the server, and the flow of the charging calculation method of the photovoltaic energy storage charging station comprises the following steps:
s201, initial price.
And (5) according to the historical charging price or the historical calculation result, the corresponding initial price is drawn.
S202, price adjustment.
And carrying out price adjustment processing according to the historical data.
S203, observing the data to obtain the charge quantity and the parking quantity.
And carrying out data observation processing through a preset first model to obtain a corresponding charge amount and parking quantity. And carrying out data processing through a preset second model to obtain the charging price of the corresponding time period.
S204, updating the model.
And updating the historical data at intervals of preset time intervals, and carrying out model updating processing on a preset first model, a photovoltaic power generation prediction model and a preset second model according to the updated historical data.
S205, the error is satisfied, and a model is output.
And carrying out model updating processing on the preset first model, the photovoltaic power generation prediction model and the preset second model according to the updated historical data, outputting the updated preset first model, the updated photovoltaic power generation prediction model and the updated preset second model if the error meets the preset requirement, and carrying out charging calculation processing of the photovoltaic energy storage charging station through the updated preset first model, the updated photovoltaic power generation prediction model and the updated preset second model.
S206, the error is not satisfied.
And (3) carrying out model updating processing on the preset first model, the photovoltaic power generation prediction model and the preset second model according to the updated historical data, returning to the step S202 if the error does not meet the preset requirement, and carrying out the steps S203-S204 again after carrying out price adjustment until the model error is obtained to meet the preset requirement.
The charging price and the parking charging price floating per hour are adopted, and the charging mode and the parking time of the charging vehicle are guided through the price, so that daily income is maximized. And establishing an internal relation among the charging quantity, the parking quantity, the charging price and the parking charging price through a neural network, taking the total income of the day as an optimization target, taking the total storage capacity of which the storage quantity is not higher than the solar energy generating capacity and the night valley electricity storage quantity as a constraint condition, carrying out dynamic rolling optimization, finding out the most suitable charging price and parking charging price, and ensuring the maximization of the income of the solar energy photovoltaic energy storage type charging station.
And the historical charging price and the historical parking charging price are input into a preset first model to be subjected to data processing to obtain the charging quantity and the parking quantity, the charging quantity and the parking quantity are input into a preset second model, the data processing is performed by taking the total storage quantity as a constraint condition to obtain the charging price and the parking charging price of the corresponding time period, and the charging price and the parking charging price of the corresponding time period are displayed on corresponding client software to remind a user. By adopting the technical means, the obtained charging and charging prices and parking and charging prices in the corresponding time periods can be displayed on the corresponding client software to guide the user to select the corresponding charging time length, so that the parking and charging prices or charging and charging prices are improved when the parking quantity is more, the user is guided to select shorter charging time length to save the cost, and the charging station is enabled to improve the parking and charging price or charging and charging price on the basis of guaranteeing the total charging time length, so that the comprehensive benefit of the charging station is improved. In addition, when the parking quantity is small, the parking charge price or the charging charge price is reduced, the user is guided to select longer charging time, the idle parking space and idle time when the parking quantity is small are reduced, and although the parking charge price or the charging charge price is reduced, the charging time of the user is prolonged, so that the total parking charge and the total charging charge are increased, and the comprehensive benefit of the charging station is further improved.
Based on the above embodiments, fig. 3 is a schematic structural diagram of a charging calculation device of a photovoltaic energy storage charging station according to an embodiment of the present application. Referring to fig. 3, the charging calculation device of the photovoltaic energy storage charging station provided in this embodiment specifically includes: a first data processing unit 11, a second data processing unit 12 and a display unit 13.
The first data processing unit 11 is configured to input historical data into a preset first model for data processing, so as to obtain a charge amount and a parking number, where the historical data includes a historical charge price and a historical parking charge price;
a second data processing unit 12, configured to input the charge amount and the parking number into a preset second model, and perform data processing with a total storage capacity as a constraint condition, so as to obtain a charge price and a parking charge price for a corresponding time period, where the total storage capacity includes a solar energy power generation amount and a night storage capacity;
and the display unit 13 is used for displaying the charging price and the parking charging price of the corresponding time period on the corresponding client software so as to remind the user of paying attention.
Further, the first data processing unit 11 is further configured to input a historical fast charging price and a historical fast charging parking charging price into a preset first fast charging model for data processing, so as to obtain a corresponding fast charging amount and a corresponding fast charging parking number;
And inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model for data processing to obtain the corresponding slow charge amount and slow charge parking quantity.
Further, the first data processing unit 11 is further configured to store the historyPrice for quick charging f. (t-1) and historical fast charging parking charging Price f.p (t-1) inputting a preset first quick charge model: (Power) f.c (t),N f. (t))＝μ θ (Price f. (t),Price f. (t-1),Price f. (t),Price f.p (t-1)), wherein μ θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first quick charge model, and outputting a quick charge amount Power f.c (t) and fast charge parking quantity N f. (t)；
The method for processing the data by inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model to obtain the corresponding slow charge amount and the slow charge parking quantity comprises the following steps:
charging Price of historical slow charging s. (t-1) and historical Low charging parking charging Price s. (t-1) inputting a preset first slow charge model (Power) s. (t),N s. (t))＝μ θ (Price s. (t),Price s. (t-1),Price s. (t),Price s. (t-1)), wherein μ θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first slow charge model, and outputting a corresponding slow charge amount Power s. (t) and slow charge parking quantity N s. (t)。
Further, the second data processing unit 12 is further configured to input the fast charge amount, the fast charge parking amount, the slow charge amount, and the slow charge parking amount into a preset second model;
and in the preset second model, taking the total storage capacity as a constraint condition, and taking the total income as an optimization target to perform data processing so as to obtain the quick charge price, the slow charge price, the quick charge parking charge price and the slow charge parking charge price of the corresponding time period.
Further, the second data processing unit 12 is further configured to store the fast charge amount Power f.c (t) quick charging and stoppingNumber N f. (t), slow charge amount Power s. (t) and slow charge parking quantity N s. (t) inputting a preset second model
In which Income represents total Income, price f. Charging price for quick charge, power f.c Is a quick charge amount; price s. Charging price for slow charge, power s. Is a slow charge amount; price f.p Charge price for fast charging and parking, N f. To charge the number of stops quickly, price s. Charge price for slow charge parking, N s. The number of slow charging and stopping is the number of slow charging and stopping;
to be used for
And N f. +N s. <N is a constraint condition for data processing to obtain a Price of quick charge and charge in a corresponding time period f. Price for slow charging s. Price for quick charging parking f.p And Price for slow charge parking s. Where Power represents the total charge capacity and N represents the total number of charging station parking spaces.
Further, the device also comprises a third data processing unit;
the third data processing unit is used for carrying out prediction processing according to the photovoltaic power generation prediction model to obtain solar power generation capacity;
and carrying out summation treatment according to the solar energy generating capacity and the night storage capacity to obtain the total storage capacity.
Further, the device is further used for updating the historical data at preset time intervals;
and carrying out corresponding data processing according to the updated historical data, the preset first model, the photovoltaic power generation prediction model and the preset second model to obtain the charging price and the parking charging price of the new time period.
And the historical charging price and the historical parking charging price are input into a preset first model to be subjected to data processing to obtain the charging quantity and the parking quantity, the charging quantity and the parking quantity are input into a preset second model, the data processing is performed by taking the total storage quantity as a constraint condition to obtain the charging price and the parking charging price of the corresponding time period, and the charging price and the parking charging price of the corresponding time period are displayed on corresponding client software to remind a user. By adopting the technical means, the obtained charging and charging prices and parking and charging prices in the corresponding time periods can be displayed on the corresponding client software to guide the user to select the corresponding charging time length, so that the parking and charging prices or charging and charging prices are improved when the parking quantity is more, the user is guided to select shorter charging time length to save the cost, and the charging station is enabled to improve the parking and charging price or charging and charging price on the basis of guaranteeing the total charging time length, so that the comprehensive benefit of the charging station is improved. In addition, when the parking quantity is small, the parking charge price or the charging charge price is reduced, the user is guided to select longer charging time, the idle parking space and idle time when the parking quantity is small are reduced, and although the parking charge price or the charging charge price is reduced, the charging time of the user is prolonged, so that the total parking charge and the total charging charge are increased, and the comprehensive benefit of the charging station is further improved.
The photovoltaic energy storage charging station charging calculation device provided by the embodiment of the application can be used for executing the photovoltaic energy storage charging station charging calculation method provided by the embodiment, and has corresponding functions and beneficial effects.
Based on the above embodiments, fig. 4 is a schematic structural diagram of a photovoltaic energy storage charging station system according to an embodiment of the present application. Referring to fig. 4, the photovoltaic energy storage charging station system provided in this embodiment specifically includes: the solar photovoltaic panel 21, the rectifying device 22, the photovoltaic storage battery 23, the off-peak electric storage battery 24, the intelligent charging control center 25 and the charging pile 26, wherein the charging pile 26 comprises at least one fast charging pile 261 and at least one slow charging pile 262. The solar photovoltaic panel 21 is connected to a photovoltaic storage battery 23 and an intelligent charge control center 25. The photovoltaic storage battery 23 is connected to the off-peak electric storage battery 24 and the intelligent charge control center 25. The off-peak electricity storage battery 24 is connected with the rectifying device 22 and the intelligent charging control center 25, and the rectifying device 22 is used for being connected to the municipal power grid 27. The intelligent charging control center 25 is also connected with a charging pile 26 for transmitting electric energy of the photovoltaic storage battery 23 and the off-peak electric storage battery 24 to the charging pile 26.
The photovoltaic energy storage charging station system consists of a power supply layer, an energy storage layer and a charging layer. The power layer is responsible for providing the electric power source, and the energy storage layer is responsible for storing the electric energy, and the charge layer is responsible for charging for electric automobile. Wherein the power supply layer comprises a solar photovoltaic panel 21 and a rectifying device 22. The solar photovoltaic panel 21 is used for converting solar energy into electric energy, the rectifying device 22 is used for being connected into the municipal power grid 27, the municipal power grid 27 provides municipal alternating current for the charging system, and the municipal alternating current is converted into direct current through the rectifying device 22. The energy storage layer consists of a photovoltaic battery 23 and a low-valley battery 24. The photovoltaic storage battery 23 is used for storing surplus direct current produced by the solar photovoltaic panel. The off-peak battery 24 is used to draw electricity from the municipal power grid for energy storage during the night. The charging layer is made up of a single or multiple charging posts 26. The charging pile 26 is connected to a charging bus bar, and takes electricity from the solar photovoltaic panel 21, the photovoltaic storage battery 23 or the off-peak battery 24 in a mode determined by the electric energy distribution control center. The intelligent charging control center 25 dynamically adjusts charging rates of fast charging, slow charging and parking according to the solar photovoltaic power generation amount, the storage battery residual power, the required power and the predicted parking amount on the same day, so as to achieve the purpose of matching the charging and parking requirements.
Above-mentioned, utilize solar photovoltaic power generation and battery energy storage to provide the electric automobile for charging the stake. In the daytime, clean and low-cost electric energy is provided for the charging pile by utilizing solar photovoltaic power generation, and at night, the low-valley electricity is fully utilized to charge the storage battery, so that the effects of peak clipping and valley filling of the power grid are achieved, and the charging cost of the electric automobile is reduced.
The photovoltaic energy storage charging station system provided by the embodiment of the application can be used for executing the photovoltaic energy storage charging station charging calculation method provided by the embodiment, and has corresponding functions and beneficial effects.
The embodiment of the application provides a photovoltaic energy storage charging station charging computing device, referring to fig. 5, the photovoltaic energy storage charging station charging computing device includes: processor 31, memory 32, communication module 33, input device 34 and output device 35. The number of processors in the photovoltaic energy storage charging station billing computing device may be one or more and the number of memories in the photovoltaic energy storage charging station billing computing device may be one or more. The processor, memory, communication module, input device and output device of the photovoltaic energy storage charging station charging computing device may be connected by a bus or other means.
The memory 32 is used as a computer readable storage medium for storing software programs, computer executable programs and modules, such as program instructions/modules corresponding to the charging calculation method of the photovoltaic energy storage charging station according to any embodiment of the present application (for example, the first data processing unit, the second data processing unit and the display unit in the charging calculation device of the photovoltaic energy storage charging station). The memory may mainly include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, the memory may further include memory remotely located with respect to the processor, the remote memory being connectable to the device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication module 33 is used for data transmission.
The processor 31 executes various functional applications of the device and data processing by running software programs, instructions and modules stored in the memory, i.e. implements the photovoltaic energy storage charging station charging calculation method described above.
The input means 34 may be used to receive entered numeric or character information and to generate key signal inputs related to user settings and function control of the device. The output means 35 may comprise a display device such as a display screen.
The photovoltaic energy storage charging station charging calculation equipment provided by the embodiment can be used for executing the photovoltaic energy storage charging station charging calculation method provided by the embodiment, and has corresponding functions and beneficial effects.
The present embodiments also provide a storage medium storing computer-executable instructions that when executed by a computer processor are for performing a photovoltaic energy storage charging station charging calculation method comprising: inputting historical data into a preset first model for data processing to obtain a charging amount and a parking quantity, wherein the historical data comprises a historical charging price and a historical parking charging price; inputting the charge quantity and the parking quantity into a preset second model, and performing data processing by taking the total charge quantity as a constraint condition to obtain a charge charging price and a parking charging price in a corresponding time period, wherein the total charge quantity comprises solar energy power generation quantity and night charge quantity; and displaying the charging and charging prices and the parking charging prices of the corresponding time periods on corresponding client software so as to remind the user of paying attention.
Storage media-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, lanbas (Rambus) RAM, etc.; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a second, different computer system connected to the first computer system through a network such as the internet. The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.
Of course, the storage medium storing the computer executable instructions provided in the embodiments of the present application is not limited to the photovoltaic energy storage charging station charging calculation method described above, and may also perform the related operations in the photovoltaic energy storage charging station charging calculation method provided in any embodiment of the present application.
The photovoltaic energy storage charging station charging calculation device, the storage medium and the photovoltaic energy storage charging station charging calculation equipment provided in the above embodiments can execute the photovoltaic energy storage charging station charging calculation method provided in any embodiment of the present application, and technical details not described in detail in the above embodiments can be referred to the photovoltaic energy storage charging station charging calculation method provided in any embodiment of the present application.
The foregoing description is only of the preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, and the scope of the present application is determined by the scope of the claims.
Claims (10)
1. The charging calculation method for the photovoltaic energy storage charging station is characterized by comprising the following steps of:
inputting historical data into a preset first model for data processing to obtain a charging amount and a parking quantity, wherein the historical data comprises a historical charging price and a historical parking charging price;
inputting the charge quantity and the parking quantity into a preset second model, and performing data processing by taking the total charge quantity as a constraint condition to obtain a charge charging price and a parking charging price in a corresponding time period, wherein the total charge quantity comprises solar energy power generation quantity and night charge quantity;
and displaying the charging and charging prices and the parking charging prices of the corresponding time periods on corresponding client software so as to remind the user of paying attention.
2. The method of claim 1, wherein the inputting the history data into the preset first model for data processing to obtain the charge amount and the parking number includes:
inputting the historical quick charge charging price and the historical quick charge parking charging price into a preset first quick charge model for data processing to obtain corresponding quick charge quantity and quick charge parking quantity;
and inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model for data processing to obtain the corresponding slow charge amount and slow charge parking quantity.
3. The method according to claim 2, wherein the inputting the historical fast charge price and the historical fast charge parking charge price into the preset first fast charge model for data processing to obtain the corresponding fast charge amount and the fast charge parking number includes:
charging Price of historical quick charge f.c (t-1) and historical fast charging parking charging Price f.p (t-1) inputting a preset first quick charge model:
(Power f.c (t)，N f.p (t))＝μ θ (Price f.c (t)，Price f.c (t-1)，Price f.p (t)，Price f.p (t-1)), wherein μ θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first quick charge model, and outputting a quick charge amount Power f.c (t) and fast charge parking quantity N f.p (t)；
The method for processing the data by inputting the historical slow charge charging price and the historical slow charge parking charging price into a preset first slow charge model to obtain the corresponding slow charge amount and the slow charge parking quantity comprises the following steps:
charging Price of historical slow charging s.c (t-1) and historical Low charging parking charging Price s.p (t-1) inputting a preset first slow charge model (Power) s.c (t)，N s.p (t))＝μ θ (Price s.c (t)，Price s.c (t-1)，Price s.p (t)，Price s.p (t-1)), wherein μ θ Representing a neural network, θ representing a set of model parameters of the neural network;
performing data processing according to the preset first slow charge model, and outputting a corresponding slow charge amount Power s.c (t) and slow charge parking quantity N s.p (t)。
4. The method according to claim 2, wherein the inputting the charge amount and the parking number into a preset second model, performing data processing on the total charge amount as a constraint condition, and obtaining a charge price and a parking charge price for a corresponding period of time, includes:
inputting the fast charge amount, the fast charge parking number, the slow charge amount and the slow charge parking number into a preset second model;
and in the preset second model, taking the total storage capacity as a constraint condition, and taking the total income as an optimization target to perform data processing so as to obtain the quick charge price, the slow charge price, the quick charge parking charge price and the slow charge parking charge price of the corresponding time period.
5. A method according to claim 3, wherein said inputting the charge amount and the parking number into a preset second model, and performing data processing on the total charge amount as a constraint condition, to obtain a charge price and a parking price for a corresponding period of time, comprises:
will fill charge Power soon f.c (t), number of fast charging stops N f.p (t), slow charge amount Power s.c (t) and slow charge parking quantity N s.p (t) input Presetting a second model
In which Income represents total Income, price f.c Charging price for quick charge, power f.c Is a quick charge amount; price s.c Charging price for slow charge, power s.c Is a slow charge amount; price f.p Charge price for fast charging and parking, N f.p To charge the number of stops quickly, price s.p Charge price for slow charge parking, N s.p The number of slow charging and stopping is the number of slow charging and stopping;
to be used for
And N f.p +N s.p Data processing is carried out by taking < N as constraint condition, and the Price of quick charge and charge in the corresponding time period is obtained f.c Price for slow charging s.c Price for quick charging parking f.p And Price for slow charge parking s.p Where Power represents the total charge capacity and N represents the total number of charging station parking spaces.
6. The method according to claim 1, wherein the step of inputting the history data into a preset first model for data processing to obtain the charge amount and the parking amount comprises:
performing prediction processing according to a photovoltaic power generation prediction model to obtain solar power generation capacity;
and carrying out summation treatment according to the solar energy generating capacity and the night storage capacity to obtain the total storage capacity.
7. The method according to claim 6, wherein the inputting the charge amount and the parking number into the second model, performing data processing on the total charge amount as a constraint condition, and obtaining the charge price and the parking price for the corresponding period of time, includes:
Updating historical data at preset time intervals;
and carrying out corresponding data processing according to the updated historical data, the preset first model, the photovoltaic power generation prediction model and the preset second model to obtain the charging price and the parking charging price of the new time period.
8. A photovoltaic energy storage charging station system for implementing the method of any of claims 1-7, comprising: the intelligent charging system comprises a solar photovoltaic panel, a rectifying device, a photovoltaic storage battery, an off-peak electricity storage battery, an intelligent charging control center and a charging pile, wherein the charging pile comprises at least one fast charging pile and at least one slow charging pile;
the solar photovoltaic panel is connected with the photovoltaic storage battery and the intelligent charging control center;
the photovoltaic storage battery is connected with the off-peak electricity storage battery and the intelligent charging control center;
the off-peak electricity storage battery is connected with the rectifying device and the intelligent fast charging control center, and the rectifying device is used for being connected to a municipal power grid;
the intelligent charging control center is also connected with the charging pile and used for transmitting electric energy of the photovoltaic storage battery and the off-peak electricity storage battery to the charging pile.
9. A photovoltaic energy storage charging station charging computing device, comprising:
a memory and one or more processors;
the memory is used for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-7.
10. A storage medium storing computer executable instructions which, when executed by a processor, are adapted to carry out the method of any one of claims 1 to 7.
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| CN117078326A (en) * | 2023-10-17 | 2023-11-17 | 深圳鹏城新能科技有限公司 | Lease charging method, system and medium for photovoltaic energy storage device |
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