CN116706963A - Large-scale electric vehicle V2G scheduling method based on regional power load prediction - Google Patents
Large-scale electric vehicle V2G scheduling method based on regional power load prediction Download PDFInfo
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Abstract
The application relates to a large-scale electric vehicle V2G scheduling method based on regional power load prediction, which comprises the following specific steps: step one: analyzing the historical data of the power load in the area to obtain the peak-valley fluctuation characteristics and seasonal fluctuation characteristics of the historical data; step two: predicting a power load curve in a short time in the future by adopting a long-short-term memory network LSTM; step three: calculating the dispatching priority of the electric vehicle based on the residual electric quantity and the use requirement of the electric vehicle; step four: and calculating the charge and discharge power of each electric automobile according to the dispatching priority and the charge and discharge state of the electric automobile, so as to realize large-scale dispatching of the electric automobile V2G. According to the application, the purpose of adjusting the fluctuation of the power grid is achieved by reasonably deciding the charge and discharge power of each charging automobile in the V2G mode.
Description
Technical Field
The application relates to the field of large-scale electric vehicle V2G scheduling methods based on regional power load prediction, in particular to a large-scale electric vehicle V2G scheduling method based on regional power load prediction.
Background
From the perspective of user cost, the current domestic V2G charging piles are high-current quick direct-current charging piles, have a plurality of test points, can play a certain role in peak clipping and valley filling for a power grid, but are generally arranged in an industrial park, and because the value of the current direct-current quick direct-current charging piles is high, the current direct-current quick-current direct-current charging piles are low in purchase intention of individual users of new energy automobiles and cannot be popularized in a large area, and the problem that the community power supply capacity is insufficient after the electric automobiles are purchased in a large scale in the future cannot be solved.
From the aspect of urgent demand for new energy, at present, renewable new energy such as photovoltaic and wind energy is greatly connected into a power grid, and from the aspect of sustainable development of society, effective utilization of renewable energy is an important direction for reforming the energy pattern. Due to uncertainty of renewable energy sources, fluctuation of the power grid can be caused, so that the power grid load is too high, and a large amount of energy storage systems are urgently needed to compensate to maintain stability of the power grid. In addition, when the electricity demand is low, the electricity consumption is smaller than the electricity output by the power grid, which means that the surplus energy is wasted. Therefore, the V2G technology is introduced, a large-scale electric vehicle V2G scheduling strategy based on regional power load prediction is provided, and the purpose of adjusting power grid fluctuation is achieved by reasonably deciding the charge and discharge power of each charging vehicle in a V2G mode.
Disclosure of Invention
The embodiment of the application aims to provide a large-scale electric vehicle V2G scheduling method based on regional power load prediction, so as to achieve the aim of improving the peak regulation and frequency modulation and safety emergency capacity of a power grid.
In order to achieve the above purpose, the present application provides the following technical solutions:
the embodiment of the application provides a large-scale electric vehicle V2G scheduling method based on regional power load prediction, which comprises the following specific steps:
step one: analyzing the historical data of the power load in the area to obtain the peak-valley fluctuation characteristics and seasonal fluctuation characteristics of the historical data;
step two: predicting a power load curve in a short time in the future by adopting a long-short-term memory network LSTM;
step three: calculating the dispatching priority of the electric vehicle based on the residual electric quantity and the use requirement of the electric vehicle;
step four: and calculating the charge and discharge power of each electric automobile according to the dispatching priority and the charge and discharge state of the electric automobile, so as to realize large-scale dispatching of the electric automobile V2G.
In the first step, the Pearson correlation analysis method is adopted to analyze the autocorrelation of the power load historical data.
In the second step, a long-short-period memory network LSTM is adopted to predict the power load curve in a short time in the future,
processing the power load historical data for secondary sampling by taking 10min as a secondary sampling period, and keeping the consistency of the data period;
based on the basic principle of LSTM, taking the current moment power load as input and the next moment power load as output, establishing a power load prediction model, calculating an input gate, a forgetting gate and an output gate of LSTM, and using a sigmoid function as an activation function during calculation;
calculating the output of the LSTM candidate memory cells, wherein the activation function adopts a tanh function;
calculating the output of the current memory cell based on the output of the forgetting gate, the input gate, the candidate memory cell and the memory cell at the last moment;
calculating a hidden state based on the output of the output gate and the current memory cell;
based on the error between the power load and the output gate at the next moment, calculating an update weight by adopting a gradient descent method, completing the training of the LSTM by continuously repeating the steps, and storing the trained weight and the structure of the LSTM;
based on the LSTM network after training, the current and the historical power load data are utilized to continuously predict the power load at the next moment in a rolling mode, so that the power load predicted values at a plurality of moments in the future are obtained.
In the third step, the scheduling priority is a charging priority and a discharging priority, and the calculation formula is as follows
Wherein Q represents the total capacity of the battery of the electric automobile, S represents the percentage of the residual electric quantity of the electric automobile, and P c Indicating rated charging power of electric automobile, P d Representing rated discharge power sigma of electric automobile c Representing charging priority, sigma d Indicating the discharge priority. When scheduling is performed, the charging and discharging power of each electric automobile is decided according to the priority.
The fourth step is that, in particular,
if the expected power load is higher than the predicted power load of LSTM at the next moment, a discharging priority mode is entered, otherwise, a charging priority mode is entered, in the charging priority mode, the charging power of the electric automobile is adjusted to the maximum value according to the sequence from high to low of the charging priority until the current power load reaches the expected value at the next moment, in the discharging priority mode, the actual power of the electric automobile is adjusted according to the sequence from high to low of the discharging priority until the current power load reaches the expected value at the next moment, the specific calculation formula is as follows,
wherein S is c And S is d The charging decision threshold and the discharging decision threshold are respectively, P is the actual power of the electric automobile, if P is larger than 0, the electric automobile is charged, and if P is smaller than 0, the electric automobile is discharged to a power grid.
Compared with the prior art, the application has the beneficial effects that:
(1) According to the large-scale electric vehicle V2G scheduling strategy based on regional power load prediction, the Pearson correlation analysis method is adopted to analyze power load historical data, so that the characteristics of daily periodic fluctuation, seasonal periodic fluctuation, annual growth and the like of the power load are obtained.
(2) According to the large-scale electric vehicle V2G scheduling strategy based on regional power load prediction, the regional power load prediction model is established by adopting the LSTM network, so that effective guidance can be provided for the V2G scheduling strategy, and the purpose of scheduling in advance is achieved.
(3) According to the large-scale electric vehicle V2G scheduling strategy based on regional power load prediction, the scheduling priority of each electric vehicle is evaluated, the peak-valley fluctuation of the power grid load is balanced by utilizing the battery capacity of the electric vehicle under the condition of guaranteeing the daily use requirement of the electric vehicle, and the purpose of adjusting the power grid fluctuation is achieved by reasonably adjusting the charging and discharging power of the electric vehicle.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the result of predicting future power load changes using an LSTM network in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of simulation results of load changes of a regional power grid before and after the scheduling method of the embodiment of the application is applied;
fig. 4 is an enlarged partial schematic view of fig. 3.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
As shown in fig. 1, a large-scale electric vehicle V2G scheduling method based on regional power load prediction includes the following steps:
step one: analyzing the power load historical data in a certain area to obtain peak-valley fluctuation characteristics and seasonal fluctuation characteristics of the power load historical data;
step two: predicting a power load curve in a short time in the future by adopting a long-short-term memory network (LSTM);
step three: calculating the dispatching priority of the electric vehicle based on the residual electric quantity and the use requirement of the electric vehicle;
step four: and calculating the charge and discharge power of each electric automobile according to the dispatching priority and the charge and discharge state of the electric automobile, so as to realize large-scale dispatching of the electric automobile V2G.
Step one:
and analyzing the autocorrelation of the power load historical data by adopting a Pearson correlation analysis method. Through analysis by combining with a power load historical data change curve, the power load change has strong periodicity, and mainly takes days and years as fluctuation periods. The power load also has a characteristic of rising year by year as a whole. Therefore, when predicting the power load history data in a short time in the future, a time-series prediction method is required.
Step two:
and (3-1) processing the power load historical data for a 10min subsampling period to subsample, and keeping the consistency of the data period. Because the time span of the data is long, too long sampling period can lead to too complex prediction model, so that the subsampling is needed.
(3-2) based on the basic principle of LSTM, taking the current-time power load as input and the next-time power load as output, building a power load prediction model, and calculating the input gate, the forget gate and the output gate of LSTM. When calculating, the activating function uses a sigmoid function.
(3-3) calculating the output of the LSTM candidate memory cell, and using the tanh function as the activation function.
(3-4) calculating the output of the current memory cell based on the outputs of the forgetting gate, the input gate, the candidate memory cell, and the memory cell at the previous time.
(3-5) calculating the hidden state based on the output gate and the output of the current memory cell.
(3-6) calculating an update weight by adopting a gradient descent method based on the error between the power load and the output gate at the next moment, and finishing the training of the LSTM by continuously repeating the steps (3-2) to (3-6), and storing the weight and the structure of the LSTM after the training.
(3-7) based on the LSTM network after training, utilizing the current and the historical power load data to continuously predict the power load at the next moment in a rolling way, and obtaining the power load predicted values at a plurality of moments in the future.
Based on the second step, the result of predicting the future power load change by the LSTM network is shown in fig. 2.
Step three:
and calculating the dispatching priority of each dispatching automobile according to the residual electric quantity and the use requirement of the electric automobile. Scheduling priority is divided into charging priority and discharging priority, and their calculation formulas are as follows
Wherein Q represents the total capacity of the battery of the electric automobile, S represents the percentage of the residual electric quantity of the electric automobile, and P c Indicating rated charging power of electric automobile, P d Representing rated discharge power sigma of electric automobile c Representing charging priority, sigma d Indicating the discharge priority. When scheduling is performed, the charging and discharging power of each electric automobile is decided according to the priority.
Step four:
if the expected power load is higher than the LSTM predicted next moment power load, then the discharging priority mode is entered, otherwise the charging priority mode is entered. In the charging priority mode, the charging power of the electric automobile is adjusted to the maximum value according to the sequence from high to low of the charging priority, and the current power load reaches the expected value at the next moment. In the discharging priority mode, the actual power of the electric automobile is adjusted according to the order of the discharging priority from high to low until the current power load reaches the expected value at the next moment, and a specific calculation formula is as follows. It is noted that even in the discharge priority mode, most electric vehicles are still in a charged state.
Wherein S is c And S is d And the charging decision threshold and the discharging decision threshold are respectively, and P is the actual power of the electric automobile. If P is greater than 0, it indicates that the electric vehicle is charging, and if P is less than 0, it indicates that the electric vehicle is discharging to the power grid.
As shown in fig. 3 and fig. 4, by adopting the dispatching method of the application, the simulation result of the load change of the power grid in the area can be known that the fluctuation of the power grid is obviously reduced.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (5)
1. The large-scale electric vehicle V2G scheduling method based on regional power load prediction is characterized by comprising the following specific steps of:
step one: analyzing the historical data of the power load in the area to obtain the peak-valley fluctuation characteristics and seasonal fluctuation characteristics of the historical data;
step two: predicting a power load curve in a short time in the future by adopting a long-short-term memory network LSTM;
step three: calculating the dispatching priority of the electric vehicle based on the residual electric quantity and the use requirement of the electric vehicle;
step four: and calculating the charge and discharge power of each electric automobile according to the dispatching priority and the charge and discharge state of the electric automobile, so as to realize large-scale dispatching of the electric automobile V2G.
2. The method for V2G dispatching of large-scale electric vehicles based on regional power load prediction according to claim 1, wherein in the first step, the autocorrelation of the power load history data is analyzed by using a Pearson correlation analysis method.
3. The method for large-scale electric vehicle V2G scheduling based on regional power load prediction according to claim 1, wherein the predicting the power load curve in the short time in the future by using the long-short-term memory network LSTM is specifically,
processing the power load historical data for secondary sampling by taking 10min as a secondary sampling period, and keeping the consistency of the data period;
based on the basic principle of LSTM, taking the current moment power load as input and the next moment power load as output, establishing a power load prediction model, calculating an input gate, a forgetting gate and an output gate of LSTM, and using a sigmoid function as an activation function during calculation;
calculating the output of the LSTM candidate memory cells, wherein the activation function adopts a tanh function;
calculating the output of the current memory cell based on the output of the forgetting gate, the input gate, the candidate memory cell and the memory cell at the last moment;
calculating a hidden state based on the output of the output gate and the current memory cell;
based on the error between the power load and the output gate at the next moment, calculating an update weight by adopting a gradient descent method, completing the training of the LSTM by continuously repeating the steps, and storing the trained weight and the structure of the LSTM;
based on the LSTM network after training, the current and the historical power load data are utilized to continuously predict the power load at the next moment in a rolling mode, so that the power load predicted values at a plurality of moments in the future are obtained.
4. The method for dispatching the large-scale electric automobile V2G based on regional power load prediction according to claim 1, wherein in the third step, the dispatching priority is a charging priority and a discharging priority, and the calculation formula is as follows
Wherein Q represents the total capacity of the battery of the electric automobile, S represents the percentage of the residual electric quantity of the electric automobile, and P c Indicating rated charging power of electric automobile, P d Representing rated discharge power sigma of electric automobile c Representing charging priority, sigma d Indicating the discharge priority. When scheduling is performed, the charging and discharging power of each electric automobile is decided according to the priority.
5. The method for large-scale electric vehicle V2G scheduling based on regional power load prediction according to claim 1, wherein the fourth step is specifically,
if the expected power load is higher than the predicted power load of LSTM at the next moment, a discharging priority mode is entered, otherwise, a charging priority mode is entered, in the charging priority mode, the charging power of the electric automobile is adjusted to the maximum value according to the sequence from high to low of the charging priority until the current power load reaches the expected value at the next moment, in the discharging priority mode, the actual power of the electric automobile is adjusted according to the sequence from high to low of the discharging priority until the current power load reaches the expected value at the next moment, the specific calculation formula is as follows,
wherein S is c And S is d The charging decision threshold and the discharging decision threshold are respectively, P is the actual power of the electric automobile, if P is larger than 0, the electric automobile is charged, and if P is smaller than 0, the electric automobile is discharged to a power grid.
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| CN117621898A (en) * | 2023-12-08 | 2024-03-01 | 杭州育恩科技有限公司 | Intelligent parking lot charging pile charging control method and system considering power grid electricity price |
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