A charging station scheduling method based on photovoltaic power generation prediction

Abstract

The present invention relates to a charging station scheduling method based on photovoltaic power generation prediction, comprising: obtaining historical user data of a target charging station, predicting the peak power consumption W1, flat power consumption W2 and valley power consumption W3 of the target charging station in a unit time according to the historical user data; optimizing and solving the prediction of the photovoltaic installed capacity R when P reaches the maximum value according to the peak power consumption W1, flat power consumption W2 and valley power consumption W3 of the target charging station in a unit time, and a preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3]; providing multiple charging modes during the charging process, and dynamically adjusting the charging parameters of the target charging station. Through the present invention, the utilization efficiency of the charging station is improved, and the charging costs of the charging station and users are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging stations, and in particular to a charging station scheduling method based on photovoltaic power generation prediction. Background Art

[0002] Electric vehicles will become an important carrier of transportation electrification and one of the important paths for my country to achieve its dual carbon goals. From the perspective of the development trend of electric vehicle applications, fast charging of electric vehicles will be the main charging method in the future; the power of a single electric vehicle fast charging pile is generally 40kW or above, and it generally uses AC three-phase input, and outputs DC power for electric vehicles after conversion by the internal charging module; in addition, there are also fast charging piles based on shared DC bus DC input and DC output; no matter which input form is used, the properties of the DC load of the charging pile are very suitable for combining with photovoltaic power generation. Therefore, it has become a trend to configure charging stations with photovoltaic power generation. On the one hand, it can realize efficient charging of clean energy, and on the other hand, it can reduce the pressure on the public power grid.

[0003] In the process of implementing the present invention, the inventors found that the prior art has at least the following technical problems:

[0004] The charging stations in the existing technology do not have a prediction function. The charging current changes with the current photovoltaic power generation. If a cloud passes by and the photovoltaic power generation decreases, power will be drawn from the grid immediately. In fact, after the cloud passes, the photovoltaic power generation may increase again. From an overall point of view, even if power is not drawn from the grid, it will not affect the user's charging. This will invisibly increase the burden on the grid, especially during peak hours, and also increase the charging costs of the charging station and the user. Summary of the invention

[0005] The purpose of the present invention is to propose a charging station scheduling method based on photovoltaic power generation prediction to improve the utilization efficiency of charging stations and reduce the charging costs of charging stations and users.

[0006] To achieve the above object, the present invention proposes a charging station scheduling device based on photovoltaic power generation prediction, comprising the following steps:

[0007] Acquire historical user data of a target charging station, and predict peak power consumption W1, flat power consumption W2, and valley power consumption W3 of the target charging station in a unit time according to the historical user data;

[0008] According to the peak power consumption W1, flat power consumption W2 and valley power consumption W3 of the target charging station per unit time, and the preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3], an optimization solution is performed to predict the photovoltaic installed capacity R when P reaches the maximum value; wherein, L1 is the peak photovoltaic power generation, L2 is the flat photovoltaic power generation, L3 is the valley photovoltaic power generation, P1 is the unit electricity price income of the peak charging pile, P2 is the unit electricity price income of the flat charging pile, P3 is the unit electricity price income of the valley charging pile, and P4 is the unit electricity price income of the photovoltaic.

[0009] Providing multiple charging modes during the charging process and dynamically adjusting the charging parameters of the target charging station;

[0010] Mode 1: The user requires fast charging. If the current photovoltaic power generation cannot meet the user's charging power, the charging power will be supplemented by electricity from the grid. If the current photovoltaic power generation can meet the user's charging power, no electricity will be taken from the grid.

[0011] Mode 2: If the user allows charging to be completed within a T time period, the photovoltaic power generation of the target charging station within the T time period is predicted, and it is determined whether the photovoltaic power generation of the target charging station within the T time period is greater than or equal to the user's charging required power; if it can be met, no power is drawn from the grid within the T time period; if it cannot be met, power is drawn from the grid based on the difference between the photovoltaic power generation of the target charging station and the user's charging required power.

[0012] Preferably, the second mode further includes:

[0013] When the photovoltaic power generation of the target charging station can be met, the power generation is predicted based on the minimum time unit, and the charging current of the minimum time unit is determined according to the predicted power generation until the user's charging process is completed within the T time period.

[0014] Preferably, wherein:

[0015] Peak photovoltaic power generation L1=f(R, T1, M1), R is the photovoltaic installed capacity, T1 is the peak temperature, M1 is the peak light intensity; f is the prediction function;

[0016] The flat-stage photovoltaic power generation L2=f(R, T2, M2), R is the photovoltaic installed capacity, T2 is the flat-stage temperature, M2 is the flat-stage light intensity; f is the prediction function;

[0017] The photovoltaic power generation in the valley section is L3=f(R, T3, M3), where R is the photovoltaic installed capacity, T3 is the valley temperature, and M3 is the valley light intensity; f is the prediction function.

[0018] Preferably, the one unit time is one year.

[0019] Preferably, the one unit time is one month;

[0020] The method specifically comprises:

[0021] According to the peak power consumption W1, flat power consumption W2, valley power consumption W3 of the target charging station each month, and the preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3], an optimization solution is performed to predict the photovoltaic installed capacity when P reaches the maximum value each month, thereby obtaining the photovoltaic installed capacity for 12 months in a year; R i is the photovoltaic installed capacity in the i-th month, i={1,2,…,12};

[0022] Calculate an R' based on the PV installed capacity for the 12 months of the year, so that R' is equal to R i The variance between them is minimized, and R' is determined as the photovoltaic installed capacity of the target charging station.

[0023] The present invention has at least the following beneficial effects:

[0024] Based on the historical user data and historical light data distribution, the power consumption data and photovoltaic power generation of the charging station are predicted, which can reasonably configure the photovoltaic capacity of the charging station to maximize energy benefits, avoid waste of electricity, save energy, and facilitate the realization of the "dual carbon" goals. At the same time, according to the prediction results of photovoltaic power generation, the power of the charging pile is dynamically adjusted to maximize the local consumption of clean energy.

[0025] Other features and advantages of the present invention will be set forth in the description which follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 The present invention is a flowchart of a charging station scheduling method based on photovoltaic power generation prediction in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In addition, in order to better illustrate the present invention, numerous specific details are given in the specific embodiments below. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In some examples, means well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0029] See also Figure 1 An embodiment of the present invention provides a charging station scheduling method based on photovoltaic power generation prediction, comprising the following steps:

[0030] Step S1, obtaining historical user data of a target charging station, and predicting peak power consumption W1, flat power consumption W2, and valley power consumption W3 of the target charging station in a unit time according to the historical user data;

[0031] Specifically, the historical user data includes user charging data within a certain period, such as the number of users and the total charging amount in a day, a week, a month, a quarter, half a year or a year. Because the charging behavior of users changes periodically over time, for example, users generally have their own charging habits and go to a fixed charging station for charging at fixed time intervals, so the power demand and distribution period of the target charging station in the future (which can also be a day, a week, a month, a quarter, half a year or a year) can be predicted based on the historical user data. In units of months, the peak power consumption of the target charging station in units of months can be predicted as W1 (kWh / month), the flat power consumption W2 (kWh / month), and the valley power consumption W3 (kWh / month) based on the historical user data in units of days.

[0032] Step S2, according to the peak power consumption W1, flat power consumption W2, valley power consumption W3 of the target charging station per unit time, and the preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3], optimize and solve to predict the photovoltaic installed capacity R when P reaches the maximum value;

[0033] Among them, L1 is the photovoltaic power generation in the peak period, L2 is the photovoltaic power generation in the flat period, L3 is the photovoltaic power generation in the valley period, P1 is the unit electricity price income of the charging pile in the peak period, P2 is the unit electricity price income of the charging pile in the flat period, P3 is the unit electricity price income of the charging pile in the valley period, and P4 is the unit electricity price income of photovoltaic power. Since the purchase price is fixed if the photovoltaic system returns to the grid, it does not distinguish between peak, valley and flat periods, and the income is set to be P4;

[0034] Specifically, the steps S1 and S2 are applied to the photovoltaic power generation construction stage of the charging station, especially adding photovoltaic power generation on the basis of the existing charging station, which is used to plan the photovoltaic installed capacity R to improve the utilization rate of the charging station and maximize the benefits of the charging station;

[0035] Step S3: providing multiple charging modes during the charging process and dynamically adjusting the charging parameters of the target charging station;

[0036] Mode 1: The user requires fast charging. If the current photovoltaic power generation cannot meet the user's charging power, the charging power will be supplemented by electricity from the grid. If the current photovoltaic power generation can meet the user's charging power, no electricity will be taken from the grid.

[0037] Specifically, when the user wants to complete charging as quickly as possible, similar to the existing technology, the charging power is mainly determined by the power limit fed back by the BMS. If the current photovoltaic power generation becomes smaller and cannot meet the charging power, it will immediately take electricity from the grid to supplement the power (equivalent to the uncontrolled power of the charging pile). This mode charges the highest fee and users can choose it independently.

[0038] Mode 2: If the user allows charging to be completed within a T time period, the photovoltaic power generation of the target charging station within the T time period is predicted, and it is determined whether the photovoltaic power generation of the target charging station within the T time period is greater than or equal to the power required for charging by the user; if it can be met, no power is taken from the grid within the T time period; if it cannot be met, power is taken from the grid based on the difference between the photovoltaic power generation of the target charging station and the power required for charging by the user;

[0039] Specifically, the user is allowed to complete charging within a certain time T. Based on the predicted data, it is determined whether the photovoltaic power generation of the target charging station within the T time period can meet the needs of the user; if it can be met, no electricity will be taken from the grid during the T time period. If it cannot be met, electricity will be taken from the grid based on the difference. At this time, even if electricity needs to be taken from the grid, the total amount of electricity taken from the grid is less than that of the first solution, which further increases the revenue of the charging station. The charge for this solution is lower than that of the first solution, and users can choose it independently.

[0040] In the embodiment of the present invention, based on configuring the photovoltaic capacity R of the target charging station, the charging parameters of the charging station can be dynamically adjusted to minimize the amount of electricity drawn from the power grid, so as to further increase the revenue of the target charging station.

[0041] In some embodiments, the second mode further includes:

[0042] When the photovoltaic power generation of the target charging station can meet the requirements, the power generation is predicted based on the minimum time unit, and the charging current of the minimum time unit is determined according to the predicted power generation until the user's charging process is completed within the T time period;

[0043] Specifically, the minimum time unit is 15 minutes. In the first 15 minutes, the power generation is X1, and the corresponding charging current is I1; in the second 15 minutes, the power generation is X2, and the corresponding charging current is I2, and so on, ultimately completing the user's charging process within the T time period.

[0044] In some embodiments, the photovoltaic power generation for one day, one week, one month, one quarter, half a year or one year can be predicted based on the geographical location, planned installed capacity and layout of the target charging station. Taking January as an example, the predicted power generation corresponding to the peak, flat and valley periods of the region can be predicted based on historical light data and climate information. For example, the exponential smoothing calculation method is used in conjunction with a certain time series prediction model to predict the future of the phenomenon. The principle is that the exponential smoothing value of any period is the weighted average of the actual observation value of the current period and the exponential smoothing value of the previous period. Of course, other data prediction methods can also be used.

[0045] Specifically, the prediction of photovoltaic power generation can be expressed by the following formula:

[0046] Peak photovoltaic power generation L1=f(R, T1, M1), R is the photovoltaic installed capacity, T1 is the peak temperature, M1 is the peak light intensity; f is the prediction function;

[0047] The flat-stage photovoltaic power generation L2=f(R, T2, M2), R is the photovoltaic installed capacity, T2 is the flat-stage temperature, M2 is the flat-stage light intensity; f is the prediction function;

[0048] The photovoltaic power generation in the valley section is L3=f(R, T3, M3), where R is the photovoltaic installed capacity, T3 is the valley temperature, and M3 is the valley light intensity; f is the prediction function.

[0049] In some embodiments, the one unit of time is one year.

[0050] In some embodiments, the one unit time is one month;

[0051] The method specifically comprises:

[0052] According to the peak power consumption W1, flat power consumption W2, valley power consumption W3 of the target charging station each month, and the preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3], an optimization solution is performed to predict the photovoltaic installed capacity when P reaches the maximum value each month, thereby obtaining the photovoltaic installed capacity for 12 months in a year; R i is the photovoltaic installed capacity in the i-th month, i={1,2,…,12};

[0053] Calculate an R' based on the PV installed capacity for the 12 months of the year, so that R' is equal to R i The variance between them is minimized, and R' is determined as the photovoltaic installed capacity of the target charging station.

[0054] It can be seen from the description of the above embodiments that the embodiments of the present invention have the following advantages:

[0055] Because the charging stations in the prior art do not have a prediction function, the charging current changes with the current photovoltaic power generation. If a cloud passes by and reduces the photovoltaic power generation, power will be taken from the power grid immediately. In fact, after the cloud passes, the photovoltaic power generation may increase again. From the overall point of view, even if power is not taken from the power grid, it will not affect the user's charging. This has invisibly added a burden to the power grid, especially during peak hours, and also increased the charging costs of the charging station and the user. According to the prediction of this solution, this situation can be avoided, and dynamic control can be performed based on the entire charging process of the user. Under the premise of ensuring that the user completes charging, the power drawn from the power grid can be reduced as much as possible. On the one hand, it reduces the pressure on the power grid, and on the other hand, it improves the photovoltaic utilization rate of the charging station, reduces the charging costs of the charging station and the user, and improves the revenue of the charging station, while also ensuring the user's charging experience.

[0056] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A charging station scheduling method based on photovoltaic power generation prediction, characterized in that: The following steps are involved: Obtain historical user data of a target charging station, and predict peak power consumption W1, flat power consumption W2, and valley power consumption W3 of the target charging station in a unit time according to the historical user data; the unit time is one month; According to the peak power consumption W1, flat power consumption W2, valley power consumption W3 of the target charging station each month, and the preset optimization function P=[P1*(W1-L1)+P4*L1]+[P2*(W2-L2)+P4*L2]+[P3*(W3-L3)+P4*L3], an optimization solution is performed to predict the photovoltaic installed capacity when P reaches the maximum value each month, thereby obtaining the photovoltaic installed capacity for 12 months in a year; R i is the photovoltaic installed capacity in the i-th month, i={1,2,…,12}; Calculate an R' based on the PV installed capacity for the 12 months of the year, so that R' is equal to R i The variance between them is minimized, and R' is determined as the photovoltaic installed capacity of the target charging station; Among them, L1 is the photovoltaic power generation in the peak period, L2 is the photovoltaic power generation in the flat period, L3 is the photovoltaic power generation in the valley period, P1 is the unit electricity price income of the charging pile in the peak period, P2 is the unit electricity price income of the charging pile in the flat period, P3 is the unit electricity price income of the charging pile in the valley period, and P4 is the unit electricity price income of photovoltaic; the photovoltaic power generation in the peak period L1=f(R, T1, M1), R is the photovoltaic installed capacity, T1 is the peak temperature, and M1 is the peak light intensity; f is the prediction function; the photovoltaic power generation in the flat period L2=f(R, T2, M2), R is the photovoltaic installed capacity, T2 is the flat temperature, and M2 is the flat light intensity; f is the prediction function; the photovoltaic power generation in the valley period L3=f(R, T3, M3), R is the photovoltaic installed capacity, T3 is the valley temperature, and M3 is the valley light intensity; f is the prediction function; Providing multiple charging modes during the charging process and dynamically adjusting the charging parameters of the target charging station; Mode 1: The user requires fast charging. If the current photovoltaic power generation cannot meet the user's charging power, the charging power will be supplemented by electricity from the grid. If the current photovoltaic power generation can meet the user's charging power, no electricity will be taken from the grid. Mode 2: If the user allows charging to be completed within a T time period, the photovoltaic power generation of the target charging station within the T time period is predicted, and it is determined whether the photovoltaic power generation of the target charging station within the T time period is greater than or equal to the user's charging required power. If this condition is met, no power is taken from the grid within the T time period, and the power generation is predicted based on the minimum time unit. The charging current of the minimum time unit is determined based on the predicted power generation until the user's charging process is completed within the T time period. If this condition is not met, power is taken from the grid based on the difference between the photovoltaic power generation of the target charging station and the user's charging required power.

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