Charging station design method, charging station design system and charging station

Abstract

The invention discloses a charging station design method, a charging station design system and a charging station, and belongs to the technical field of comprehensive utilization of new energy automobile charging facilities and renewable energy sources, and the method comprises the following steps: obtaining real-time meteorological data and load data; preprocessing the meteorological data and the load data to obtain preprocessed data; and carrying out feature extraction on the preprocessed data to obtain predicted features. And establishing a prediction model of the energy power generation power and the load demand, and obtaining wind power generation prediction power, solar power generation prediction power and load demand prediction power at the future moment by adopting the established prediction model according to prediction characteristics. And selecting an energy scheduling strategy based on a predicted power result of the prediction model, meteorological sensor data and a preset control rule. And executing the selected energy scheduling strategy, and adjusting the working mode of the charging station. According to the invention, the energy utilization efficiency, economy and stability of the charging station are improved, and the operation cost and environmental influence are reduced.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle charging facilities and comprehensive utilization of renewable energy, and in particular to a charging station design method, a charging station design system and a charging station. Background Art

[0002] With the rapid development of new energy vehicles, the demand for charging facilities is increasing, especially for fast and efficient charging technology. However, there are a series of problems with traditional charging stations: most charging stations rely on city electricity and fail to effectively integrate renewable energy, resulting in energy waste and high operating costs; the charging efficiency of existing charging equipment is low, especially when high-power charging is required, the energy loss during the charging process is large; the flexibility of existing charging stations is poor, and it is difficult to adjust in real time according to grid load fluctuations.

[0003] A small number of new energy charging stations combine wind energy, solar energy and energy storage equipment to achieve effective use of renewable energy. However, these new energy charging stations still face some challenges in practical applications. Although the combination of wind energy, solar energy and energy storage equipment can effectively utilize renewable energy, due to the instability and intermittent nature of wind energy and solar energy, charging stations that rely on these energy sources may encounter unstable power supply, resulting in power outages or instability during the charging process, affecting the user's charging experience and charging efficiency. Some existing new energy charging stations still rely on traditional power networks as backup power sources. Once a power grid failure occurs or power demand fluctuates, the stability and availability of the charging stations are restricted. Summary of the invention

[0004] The present application aims to provide a charging station design method, a charging station design system and a charging station that solve the problem that the stability and availability of a charging station are restricted.

[0005] In order to achieve the above-mentioned purpose, the technical solution of this application is: A charging station design method, comprising: Step S1: Obtain real-time meteorological data and load data; Step S2: preprocessing the meteorological data and the load data to obtain preprocessed data; extracting features from the preprocessed data to obtain prediction features; Step S3: Establish a prediction model for energy generation power and load demand, and according to the prediction characteristics, use the established prediction model to obtain the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at a future moment; Step S4: Selecting an energy dispatch strategy based on the predicted power result of the prediction model, meteorological sensor data, and preset control rules; Step S5: Execute the selected energy scheduling strategy to adjust the working mode of the charging station.

[0006] Optionally, in step S1, the meteorological data includes: meteorological sensor data and meteorological forecast data, wherein the meteorological sensor data includes: wind speed, solar irradiance, ambient temperature, relative humidity; and the load data includes: load demand, DC bus voltage, and energy storage battery charge state.

[0007] Optionally, in step S2, the preprocessing includes: maximum value normalization, sliding mean filtering, and data standardization; The feature extraction includes: extracting valuable features from the preprocessed data, including time series features: daily variation trends of wind speed and solar irradiance; extracting weather variation features based on meteorological data in the preprocessed data; generating load prediction features based on historical load trends of load data in the preprocessed data; and constructing periodic features; After the feature extraction, it also includes: calculating the weight of the extracted features to obtain the prediction features, and the prediction features include: normalized wind speed, normalized solar irradiance, standardized temperature, hourly cycle, first-order difference of load power, and load power.

[0008] Optionally, in step S3, a long short-term memory network model is used to establish a prediction model for energy generation capacity and load demand, the input of the prediction model is the prediction feature, and the output of the prediction model is the initial wind power generation prediction power, initial solar power generation prediction power, and initial load demand prediction power at a future time.

[0009] Optionally, after the prediction model outputs the predicted power result, it also includes: correcting the initial wind power generation predicted power and the initial solar power generation predicted power to obtain a corrected wind power generation predicted power and a corrected solar power generation predicted power.

[0010] Optionally, in step S4, the preset control rules include: step S41 to step S413; Step S41: energy dispatch control starts; Step S42: Determine whether the charging station status is normal, if so, proceed to step S43; if not, proceed to step S410; Step S43: determine whether the revised wind power generation forecast power and the revised solar power generation forecast power are greater than the load demand forecast power, if so, proceed to step S44, if not, proceed to step S46; Step S44: Select strategy 1: scenery-solar priority mode; Step S45: adjusting the output according to the modified wind power generation prediction power and the modified solar power generation prediction power; proceeding to step S411; Step S46: Determine the state of charge of the energy storage battery. If it is greater than 70%, proceed to step S47; if it is greater than 30% and less than 70%, proceed to step S48; if it is less than 30%, proceed to step S49; Step S47: select strategy 2: wind-solar-storage collaborative mode, enable constant voltage-frequency ratio control, and proceed to step S411; Step S48: select strategy 3: storage-grid coordination mode 1, enable droop control, storage-grid joint output, and proceed to step S411; Step S49: Select strategy 4: storage-grid coordination mode 2, energy storage is charged from the grid, and proceed to step S411; Step S410: select strategy 5: island operation mode, enable virtual synchronous dual-winding generator control, and proceed to step S411; Step S411: executing the strategy; Step S412: Evaluate the energy dispatch control status, including: the state of charge of the energy storage battery, the error of the predicted power result, and the quality of the grid voltage. If the actual needs of the charging station are met, proceed to step S413; if not, return to step S41; Step S413: Energy scheduling control ends.

[0011] Optionally, in step S5, after selecting the energy scheduling strategy, it also includes: continuously optimizing the scheduling strategy through reinforcement learning.

[0012] A charging station design system, executing a charging station design method as described in any one of the above, comprising: an environment perception module, a data preprocessing and feature extraction module, a deep learning and prediction module, an adaptive control module and an execution and scheduling module; The environment perception module, the data preprocessing and feature extraction module, the deep learning and prediction module, the adaptive control module and the execution and scheduling module are connected in sequence.

[0013] A charging station, executing a charging station design method as described in any one of the above, comprising: Wind, solar and storage systems; A dual-winding generator, wherein a first end of the dual-winding generator is connected to a first end of the wind-solar-storage system; A first ACDC converter, wherein a first end of the first ACDC converter is respectively connected to a first end of the dual-winding generator and a first end of the wind-solar-storage system; A second ACDC converter, wherein a first end of the second ACDC converter is connected to a second end of the dual-winding generator; A 400V charging pile, wherein a first end of the 400V charging pile is connected to a second end of the first ACDC converter; An 800V charging pile, wherein a first end of the 800V charging pile is connected to a second end of the second ACDC converter; A mains power interface is reserved, and a first end of the reserved mains power interface is respectively connected to a first end of the wind-solar-storage system, a first end of the dual-winding generator, and a first end of the ACDC converter.

[0014] Optionally, the wind-solar-storage system includes: Energy storage batteries; A wind turbine, wherein a first end of the wind turbine is connected to a first end of the energy storage battery; A solar panel, wherein a first end of the solar panel is connected to a first end of the energy storage battery and a first end of the wind turbine generator respectively; A bidirectional DCAC converter, wherein the first end of the bidirectional DCAC converter is respectively connected to the first end of the energy storage battery, the first end of the wind turbine, and the first end of the solar panel; the second end of the bidirectional DCAC converter is the first end of the wind-solar-storage system.

[0015] The present application provides a charging station design method, a charging station design system and a charging station. Based on the long short-term memory network model (LSTM), a prediction model for energy generation power and load demand is established, which can more accurately predict the power generation and charging demand of renewable energy, thereby achieving more efficient energy distribution and management, and improving the operating efficiency of the charging station. The dual-winding generator design can output voltages of different voltage levels to meet the charging needs of various vehicles. There is no need to use a DCDC converter, which simplifies the circuit structure, reduces the loss in the energy conversion process, and improves the charging efficiency. The multi-voltage output characteristics of the dual-winding generator enable the charging station to be compatible with the charging needs of various types of electric vehicles, improving the applicability and flexibility of the charging station. By integrating the wind, light and storage system, full use is made of renewable energy such as solar energy and wind energy, reducing dependence on traditional city electricity, reducing energy costs, and improving the sustainability of energy utilization of the charging station. Through the charging station design system and energy distribution strategy, the components in the charging station can be monitored and regulated in real time to ensure efficient distribution and flexible utilization of energy, and improve the stability and economy of the charging station operation.

[0016] In order to make the above features and advantages of the application more obvious and easy to understand, the following embodiments are specifically cited and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Flow chart of the charging station design method of the present application.

[0018] Figure 2 This is a flow chart of the preset control rules of this application.

[0019] Figure 3 A module diagram of the charging station design system of this application.

[0020] Figure 4 This is a circuit diagram of the charging station of the present application. DETAILED DESCRIPTION

[0021] In order to make the purpose and technical solution of the embodiment of the present application clearer, the technical solution of the embodiment of the present application will be clearly and completely described in conjunction with the drawings of the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment of the present application, not all of the embodiments. Based on the described embodiment of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0022] In one embodiment of the present application, please refer to Figure 1 , Figure 1 This is a flow chart of a charging station design method of the present application. The present application provides a charging station design method, including: steps S1 to S5.

[0023] Step S1: Acquire real-time meteorological data and load data.

[0024] Step S2: Preprocessing the meteorological data and the load data to obtain preprocessed data; extracting features from the preprocessed data to obtain prediction features.

[0025] Step S3: Establish a prediction model for energy generation power and load demand. According to the prediction characteristics, use the established prediction model to obtain the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at the future moment.

[0026] Step S4: Select an energy scheduling strategy based on the predicted power results of the prediction model, meteorological sensor data, and preset control rules.

[0027] Step S5: Execute the selected energy scheduling strategy to adjust the working mode of the charging station.

[0028] In step S1, see Figure 1 In step S1, real-time meteorological data and load data are obtained.

[0029] As an example, the meteorological data includes: meteorological sensor data and meteorological forecast data, wherein the meteorological sensor data includes: wind speed, solar irradiance, ambient temperature, relative humidity; the load data includes: load demand, DC bus voltage, energy storage battery charge state. The meteorological sensor data, meteorological forecast data and load data are numerically expressed as: Formula (1) in, X meteo Represents meteorological sensor data; v w Indicates wind speed ( ); G solar represents the solar irradiance ( ); T amb Indicates temperature ( ); RH Relative humidity ( ); X load Represents load data; P demand Indicates the charging demand power ( ); V bus Indicates the DC bus voltage ( ); SOC bat Indicates the state of charge of the energy storage battery ( ); X pred Represents weather forecast data; NWP represents the output of the numerical weather forecast model (spatial resolution 1km×1km), lat Indicates latitude, lon Indicates longitude, t represents time; T represents the transposed symbol.

[0030] In step S2, see Figure 1 In step S2, the meteorological data and the load data are preprocessed to obtain preprocessed data; and feature extraction is performed on the preprocessed data to obtain prediction features.

[0031] As an example, the preprocessing includes: maximum value normalization, sliding mean filtering, and data standardization, and after the preprocessing, preprocessed data of the load data and the meteorological data are obtained.

[0032] Furthermore, the maximum value normalization includes: normalizing the wind speed and the solar irradiance to the maximum value. The maximum value normalization of the wind speed is expressed by the formula: Formula (2) in, represents the normalized wind speed, v w,min Indicates the minimum value of the wind speed data obtained, v w,max Indicates the maximum value of the obtained wind speed data. By linearly transforming the obtained wind speed data, the result value of the wind speed is mapped to [0-1] to obtain the normalized wind speed.

[0033] The maximum normalization formula for solar irradiance is: Formula (3) in, represents the normalized solar irradiance, G STC Indicates standard test conditions, which refers to the test parameters of solar panels in a standard laboratory environment: irradiance: 1000W / m²; battery temperature: 25℃; spectrum distribution: AM1.5. By comparing the real-time solar irradiance with the standard test conditions, the actual output efficiency of the photovoltaic system is evaluated.

[0034] Furthermore, the sliding mean filter includes: smoothing and differential processing of time series data such as temperature and load data. Set the window size N=60, and the input data includes: time series data such as temperature and load data, which can be expressed as: Formula (4) in, y t represents the filtered time series data, x k Indicates the window k Input data, x t Indicates the current time t The input data is 3σ, which represents three times the standard deviation (the threshold for determining outliers), and sgn represents the sign function. μ window Represents the mean of the input data in the window. The high-frequency noise of the input data is suppressed by the sliding mean filter. When it is detected that the input data at the current moment deviates from the mean by more than 3σ, the Kalman filter is triggered by the sign function to correct the input data.

[0035] Furthermore, data standardization includes: standardizing the load data and meteorological data so that their mean is 0 and their standard deviation is 1, which can be expressed as: Formula (5) Among them, Δ x t represents the first-order difference of the time series at time t, x t-1 Indicates time t-1 The input data is used to eliminate trend items through first-order differences, eliminate trend items in load data and meteorological data (such as the inherent changes in temperature rising during the day and falling at night), highlight short-term fluctuation characteristics (such as sudden changes in wind speed), make time series data more stable, facilitate subsequent model learning, and improve model training effects.

[0036] Furthermore, feature extraction is performed on the preprocessed data to obtain prediction features. The feature extraction includes: extracting valuable features from the preprocessed data, including time series features: daily variation trends of wind speed and solar irradiance; extracting weather change features based on meteorological data in the preprocessed data to provide more efficient input for subsequent deep learning models; generating load prediction features based on historical load trends of load data in the preprocessed data.

[0037] Furthermore, the periodic characteristics are constructed and expressed as follows: Formula (6) in, C day represents the hour period, C year represents the daily cycle, t Indicates the number of hours in a day (0≤t<24 hours), t =12 o'clock corresponds to noon, t =0 corresponds to midnight, d Indicates the cumulative day of the year (1≤ d <365 days). The intra-day periodicity of solar irradiance and load demand is characterized by periodic characteristics.

[0038] Furthermore, the weights of the extracted features are calculated, and the feature weights are calculated using random forest, which is expressed as follows: Formula (7) in, W i represents the weight of the i-th feature, N tree Indicates the number of decision trees in the random forest, Imp i (t) represents the weight of the i-th feature in the t-th decision tree, M represents the total number of features in the data set, Imp j (t) Represents the weight of the jth feature in the tth decision tree. By calculating and averaging the weights of each feature in each decision tree, the importance of each feature in the entire random forest model is comprehensively evaluated.

[0039] Furthermore, according to the weight calculation results of the extracted features, the top 6 features with the highest weights are selected as the prediction features of the model input, including normalized wind speed, normalized solar irradiance, standardized temperature, hourly period, first-order difference of load power, and load power.

[0040] Step S3: Establish a prediction model for energy generation capacity and load demand. According to the prediction characteristics, use the established prediction model to obtain the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at the future moment.

[0041] As an example, a long short-term memory network model (LSTM) is used to establish a prediction model for energy generation capacity and load demand. The prediction model LSTM is trained using historical data of normalized wind speed, normalized solar irradiance, standardized temperature, hourly cycle, first-order difference of load power, load power, wind power generation power, solar power generation power and load demand power to ensure that the prediction model LSTM can accurately predict the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at future times. The prediction model LSTM input is normalized wind speed, normalized solar irradiance, standardized temperature, hourly cycle, first-order difference of load power, and load power, and the output is the initial wind power generation prediction power at future times. (t+1), initial solar power generation prediction power (t+1), initial load demand forecast power (t+1). It can be expressed as: Formula (8) in, x t Represents the input vector at the current moment, which is used to carry the input features; h t-1 Indicates the hidden state of the previous moment, used as a short-term memory carrier: transmits the network state information of the previous moment; C t-1 Represents the cell state at the previous moment and is used to store important feature information across time steps; W f represents the forget gate of the weight matrix, W i represents the input gate of the weight matrix, W c represents the memory cell of the weight matrix, W o Represents the output gate of the weight matrix, which is used for the feature transformation matrix: mapping the input and hidden states to the gating signal; b f , b i , b c , b o Both represent bias terms, which are used for gate offset: adjusting the threshold of the gate activation function; f tRepresents the output of the forget gate, which is used to decide how much historical information to retain: controlling the cell state at the last moment C t-1 The proportion of forgetting; Represents a candidate memory unit, which is used for new information candidate value: contains the feature information extracted at the current moment; tanh represents the hyperbolic tangent function; i t Represents the output of the input gate, which is used to decide how much new information to update: controlling the candidate memory unit Write ratio; C t Represents the updated cell state, which is used for long-term memory update: integrating historical memory and the current input vector; h t Represents the current hidden state, used for short-term memory output: the hidden state passed to the next time step; o t Represents the output of the output gate, which is used to decide what information to output: controlling the updated cell state C t To the current hidden state h t conversion.

[0042] Specifically, the input vector x t It is expressed as: Formula (9) in, P load (t) represents the load power, Δ P load (t) represents the first-order difference of load power. Normalized wind speed Impact on initial wind power generation forecast (t+1); Normalized solar irradiance With standardized temperature Impact on initial solar power generation forecast (t+1); hour period C day , load power first order difference Δ P load (t) and load power P load (t) Impact on initial load demand forecast power (t+1).

[0043] Furthermore, the output gate controls the cell state To hidden state Information flow, hidden state In order to pass the intermediate features to the next time step, the temporal dependency of the historical input information is encoded. The final prediction output of the prediction model LSTM requires an additional fully connected layer to process the hidden state, which is expressed as: Formula (10) in, W wind is the weight matrix of the wind power generation prediction power output layer, W solar The weight matrix for the solar power generation prediction power output layer, W load The weight matrix of the power output layer for load demand prediction, b wind The bias term for the wind power generation prediction power output layer, b solar Predict the bias term of the power output layer for solar power generation, b load Predict the bias term of the power output layer for load demand.

[0044] As an example, the network parameters in the prediction model LSTM are 3 LSTM layers. The LSTM layer is used to capture the temporal dependencies in the time series. Increasing the number of network layers allows the model to learn more complex and abstract feature representations. The number of units in each LSTM layer is 128, 64, and 32, respectively. The number of units determines the dimension of the hidden state of each LSTM layer, indicating the number of features that each LSTM layer can learn. In the established prediction model LSTM, in order to prevent the prediction model LSTM from overfitting, the regularization method is used Dropout Randomly ignore the outputs of some neurons with a certain probability.

[0045] In one embodiment, Dropout= 0.2.

[0046] As an example, according to the established prediction model LSTM, the prediction features are input: normalized wind speed, normalized solar irradiance, standardized temperature, hourly cycle, first-order difference of load power, and load power, and the initial wind power generation prediction power, initial solar power generation prediction power, and initial load demand prediction power at the future moment are obtained.

[0047] Furthermore, the mean square error of the three prediction tasks of wind power generation, solar power generation and load demand is optimized, which can be expressed as follows: Formula (11) in, It represents the mean square error result of optimizing the three prediction tasks of wind power generation, solar power generation and load demand at the same time;P wind represents the actual wind power generation, P solar Indicates the actual solar power generation power, P load Indicates the actual load demand power, represents the initial wind power generation forecast power; represents the initial solar power generation forecast power; Represents the initial load demand forecast power; the initial wind power forecast power, initial solar power forecast power, and initial load demand forecast power after the initial forecast of the forecast model LSTM need to be close to the actual wind power, actual solar power, and actual load demand power. ɑ, β, and γ all represent weight coefficients, which are used to adjust the importance of each forecast task.

[0048] In one embodiment, α=0.4, β=0.3, and γ=0.3.

[0049] Furthermore, due to the significant characteristics of wind speed probability distribution, the Bayesian correction of wind power prediction results is used for wind power prediction tasks, which can be expressed as: Formula (12) in, represents the revised wind power generation forecast power, represents the historical actual wind speed dataset, Indicates the predicted wind speed value at the current moment, Represents the marginal probability distribution of historical wind speeds.

[0050] Furthermore, for the solar power generation prediction task, the Bayesian correction of the solar power generation prediction result is used, which is expressed as follows: Formula (13) in, Indicates the revised solar power generation forecast power, represents the historical irradiance distribution, represents the predicted irradiance value, Represents the marginal probability distribution of historical solar irradiance.

[0051] As an example, the predicted power results of the prediction model LSTM include: corrected wind power generation predicted power, corrected solar power generation predicted power, and load demand predicted power.

[0052] Step S4: Select an energy scheduling strategy based on the predicted power results of the prediction model LSTM, meteorological sensor data, and preset control rules.

[0053] As an example, see Figure 2, Figure 2 It is a flow chart of the preset control rule, and the preset control rule includes: step S41 to step S413.

[0054] Step S41: Energy dispatch control starts.

[0055] Step S42: Determine whether the charging station status is normal, if so, proceed to step S43; if not, proceed to step S410.

[0056] Step S43: Determine whether the revised wind power generation forecast power and the revised solar power generation forecast power are greater than the load demand forecast power. If so, proceed to step S44; if not, proceed to step S46.

[0057] Step S44: Select strategy 1: scenery priority mode.

[0058] Step S45: Adjust the output according to the revised wind power generation forecast power and the revised solar power generation forecast power; proceed to step S411.

[0059] Step S46: Determine the state of charge of the energy storage battery. If it is greater than 70%, proceed to step S47; if it is greater than 30% and less than 70%, proceed to step S48; if it is less than 30%, proceed to step S49.

[0060] Step S47: Select strategy 2: wind-solar-storage collaborative mode, enable constant voltage-frequency ratio (V / F) control, and proceed to step S411.

[0061] Step S48: Select strategy 3: storage-grid collaboration mode 1, enable droop control, and the storage and grid work together, and proceed to step S411.

[0062] Step S49: Select strategy 4: storage-grid coordination mode 2, energy storage is charged from the grid, and proceed to step S411.

[0063] Step S410: Select strategy 5: island operation mode, enable virtual synchronous dual-winding generator (vsg) control, and enter step S411.

[0064] Step S411: Execute the strategy.

[0065] Step S412: Evaluate the energy dispatch control status, including: the state of charge of the energy storage battery, the error of the predicted power result, and the quality of the grid voltage. If the actual needs of the charging station are met, proceed to step S413; if not, return to step S41.

[0066] Step S413: Energy scheduling control ends.

[0067] As an example, the strategy 1: wind-solar priority mode includes: when wind and solar power generation is sufficient, the electricity generated by them is used to charge vehicles first.

[0068] Strategy 2: Wind, solar and storage synergy mode, including: when the wind and solar power generation fluctuates or is temporarily insufficient, the energy storage battery supplements the power. If the wind and solar power generation power is greater than the vehicle charging power demand, the excess power is stored in the energy storage system; if the wind and solar power generation power is less than the vehicle charging power demand, the shortfall is provided by the energy storage system.

[0069] The strategy 3: storage-grid synergy mode 1, includes: when the combined power supply of the wind-solar-storage synergy mode still cannot meet the vehicle charging needs, obtaining electricity from the power grid.

[0070] The strategy 4: storage-grid synergy mode 2, includes: during the off-peak period of electricity consumption or when the charge of the energy storage battery is low, low-cost electricity can be obtained from the power grid and stored in the energy storage battery, and then released for use during the peak period of electricity consumption.

[0071] The strategy 5: island operation mode includes: when the energy storage battery and the power grid are unable to supply power, the generator is started to output voltages of 400V and 800V levels respectively for vehicle use.

[0072] Furthermore, when the environment changes, the prediction model LSTM will continuously optimize the scheduling strategy through reinforcement learning. For example, when the wind speed fluctuates greatly, the weight of wind power generation will be increased; when it is cloudy, the weight of battery discharge power will be increased.

[0073] As an example, the reinforcement learning includes: Q-learning update rules, rolling horizon control (RHC).

[0074] As an example, define the state-action space, which can be expressed as: Formula (14) in, s t represents the state vector, a t represents the action vector, K w represents the wind power generation dispatch weight, K s represents the solar power generation scheduling weight, K grid represents the grid interaction weight, K i Indicates the energy scheduling priority.

[0075] Furthermore, the wind power generation dispatch weight is adjusted according to the membership function Kw , expressed as: Formula (15) in, represents the adjusted wind power generation dispatch weight, μ wind (v w ) Represents wind speed membership, which is used to characterize wind energy availability.

[0076] Wind speed membership μ wind (v w ) The formula is: Formula (16) in, v cut-in Indicates the fan start-up wind speed; v rated Indicates the rated wind speed, i.e. the maximum output wind speed; the real-time wind speed is used in this formula v w The calculation is in line with the physical definition of the wind turbine power curve and directly corresponds to the technical parameters of the wind turbine. However, the normalized wind speed may destroy the physical meaning of the membership function and cause the control strategy to lose the equipment protection function. Therefore, the real-time wind speed is used instead of the normalized wind speed after normalization.

[0077] Furthermore, the Q-learning update rule is used to optimize the long-term scheduling strategy, which is expressed as: Formula (17) Among them, Q(s,a) represents the current state-action pair; Q(s',a') represents the next state-optimal action pair; It means taking the maximum value of Q(s',a') for all a'; η Represents the learning rate, which is used to update the step size; γ represents the discount factor, i.e., the future reward attenuation coefficient; r represents the reward function.

[0078] Furthermore, the design of the reward function r is expressed as: Formula (18) in, P curt It represents the abandoned wind and solar power, that is, the renewable energy power that cannot be used. Penalizing abandoned energy can improve the utilization rate of wind and solar energy.

[0079] In one embodiment, the design λ 1 =0.4 means energy abandonment penalty; λ 2 =0.2 means the energy storage battery is charged SOC bat balanced; λ 3 =0.1 means the voltage is stable.

[0080] As an example, rolling horizon control is used to accurately track short-term scheduling strategies. Rolling horizon control and Q-learning together constitute hierarchical control of scheduling strategies. Rolling horizon control is expressed as: Formula (19) Among them, u t:t+H represents the control input sequence from time t to t+H; P gen ( k ) represents the total power generated at the kth moment; P demand (k) represents the charging demand power at the kth moment, which is obtained by forward-looking optimization scheduling based on load demand prediction power; ρ Represents the control cost weight coefficient, which is used to weigh the tracking accuracy and motion amplitude; R represents the control input weight matrix, usually a diagonal matrix; u ( k ) represents the control input sequence at time k. The corresponding variable value scheme when the rolling horizon control formula obtains the minimum value is used to optimize the energy scheduling strategy.

[0081] As an example, after optimizing the energy scheduling strategy using reinforcement learning, it also includes: using the interior point method to solve the scheduling strategy for quadratic programming, which is expressed as: Formula (20) in, represents the minimum value of the predicted power of corrected wind power generation, represents the maximum value of the predicted power of corrected wind power generation, Indicates the minimum charge value of the energy storage battery. Indicates the maximum charge of the energy storage battery. V nom Indicates the nominal voltage of the system. For example, the allowable fluctuation range of an 800V DC bus is 760V~840V.

[0082] As an example, when the wind speed fluctuates greatly: relax the maximum value of the wind power forecast power Limitation, allowing greater fluctuations in wind power output, by adjusting the control input weight matrix R, reducing the penalty term for wind power regulation; when photovoltaic power is insufficient on cloudy days: tighten the minimum charge value of the energy storage battery , such as from 20% to 30%, increase the battery discharge power The prediction model LSTM is used to predict energy generation capacity and load demand, and the strategy is adjusted according to real-time fluctuations to ensure stable operation of the charging station.

[0083] In step S5, refer to Figure 1 In step S5, the selected energy scheduling strategy is executed to adjust the working mode of the charging station.

[0084] In another embodiment of the present application, please refer to Figure 3 , Figure 3 This is a module diagram of the charging station design system of the present application. The present application provides a charging station design system for executing the above-mentioned charging station design method, including: an environmental perception module 21, a data preprocessing and feature extraction module 22, a deep learning and prediction module 23, an adaptive control module 24 and an execution and scheduling module 25.

[0085] As an example, the environment perception module 21, the data preprocessing and feature extraction module 22, the deep learning and prediction module 23, the adaptive control module 24 and the execution and scheduling module 25 are connected in sequence.

[0086] As an example, the environmental perception module 21 obtains real-time meteorological data and load data, and outputs them to the data preprocessing and feature extraction module 22; the data preprocessing and feature extraction module 22 preprocesses the meteorological data and load data to obtain preprocessed data, extracts features from the preprocessed data to obtain prediction features, and outputs them to the deep learning and prediction module 23; the deep learning and prediction module 23 establishes a prediction model for energy generation capacity and load demand, and according to the prediction features, uses the established prediction model to obtain the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at several moments in the future, and outputs them to the adaptive control module 24; the adaptive control module 24 selects an energy scheduling strategy based on the predicted power results of the prediction model, meteorological sensor data and preset control rules, and outputs it to the execution and scheduling module 25; the execution and scheduling module 25 executes the selected energy scheduling strategy and adjusts the working mode of the charging station.

[0087] In another embodiment of the present application, please refer to Figure 4 , Figure 4 The circuit diagram of the charging station of the present application is shown in FIG. 1 . The present application provides a charging station for executing the above-mentioned charging station design method. The charging station includes: Wind and solar storage system 31; A dual-winding generator 32, a first end of the dual-winding generator 32 is connected to a first end of the wind-solar-storage system 31; ACDC converter 33, a first end of the ACDC converter 33 is connected to a first end of the dual-winding generator 32 and a first end of the wind-solar-storage system 31 respectively; An ACDC converter 34 , wherein a first end of the ACDC converter 34 is connected to a second end of the dual-winding generator 32 ; A 400V charging pile 35, wherein a first end of the 400V charging pile 35 is connected to a second end of the ACDC converter 33; An 800V charging pile 36 , a first end of the 800V charging pile 36 is connected to a second end of the ACDC converter 34 ; A mains power interface 37 is reserved, and a first end of the reserved mains power interface 37 is respectively connected to a first end of the wind-solar-storage system 31 , a first end of the dual-winding generator 32 , and a first end of the ACDC converter 33 .

[0088] As an example, the wind-solar-storage system 31 includes: Energy storage battery 311; A wind turbine 312, wherein a first end of the wind turbine 312 is connected to a first end of the energy storage battery 311; A solar panel 313, wherein a first end of the solar panel 313 is connected to a first end of the energy storage battery 311 and a first end of the wind generator 312 respectively; A bidirectional DCAC converter 314 , a first end of the bidirectional DCAC converter 314 is respectively connected to a first end of the energy storage battery 311 , a first end of the wind turbine 312 , and a first end of the solar panel 313 ; a second end of the bidirectional DCAC converter 314 is a first end of the wind-solar-storage system 31 .

[0089] As an example, the execution and scheduling module 25 of the charging station design system controls the charging station to execute and schedule the energy scheduling strategy according to the selected energy scheduling strategy.

[0090] As an example, in the wind-solar-storage system 31 , the wires connecting the energy storage battery 311 , the wind turbine 312 , the energy storage battery 311 , and the bidirectional DCAC converter 314 are 400V DC busbars.

[0091] As an example, the wires connecting the wind-solar-storage system 31, the dual-winding generator 32, the ACDC converter 33, and the reserved AC power interface 37 are 400V DC bus; the wires connecting the ACDC converter 33 and the 400V charging pile 35 are 400V DC bus.

[0092] As an example, the wires connecting the dual-winding generator 32 and the ACDC converter 34 are 800V DC bus bars; the wires connecting the ACDC converter 34 and the 800V charging pile 36 are 800V DC bus bars.

[0093] As an example, the energy storage battery 311 uses a lithium battery pack as an energy storage medium, which is connected to a 400V DC bus and connected to a 400V AC bus through a bidirectional DCAC converter 314. On the one hand, the energy storage battery 311 can store excess electric energy. When the power generated by the solar panel 313 and the wind turbine 312 is greater than the required power for vehicle charging, the energy storage battery 311 is charged to store electric energy and release electric energy when wind and solar power generation is insufficient or when electricity consumption is at peak. When the wind and solar power generation fluctuates or is temporarily insufficient and cannot meet the vehicle charging requirements, the energy storage battery 311 discharges to supplement the insufficient part and ensure the continuity of vehicle charging. On the other hand, the energy storage battery 311 can also stabilize the 400V DC bus voltage. The battery management system (BMS) monitors the parameters such as the power, voltage, and temperature of the energy storage battery in real time to ensure the safe and efficient operation of the wind, solar, and storage system 31.

[0094] The wind turbine 312 is installed at a suitable height and position to obtain stable wind energy. After the AC power generated by the wind turbine 312 is converted into DC power by the rectifier, it is connected to the 400V DC bus through the maximum power point tracking (MPPT) controller. The maximum power point tracking controller adjusts the operating parameters of the wind turbine 312 according to the wind speed change to ensure efficient power generation under different wind speed conditions. When the wind speed is within the rated range, the wind turbine 312 generates electricity stably and supplies power to the 400V DC bus to provide power for the subsequent charging process and the energy storage battery 311; when the wind speed is too high or too low, the wind turbine 312 takes corresponding protection measures, such as speed limiting or shutdown, to ensure the safety of the charging station.

[0095] The solar panel 313 is arranged on the roof of the charging station or in an open area around it, converts solar energy into direct current, and is connected to a 400V DC bus through a maximum power point tracking controller. The maximum power point tracking controller monitors the output voltage and current of the solar panel 313 in real time, and dynamically adjusts the working point according to environmental factors such as solar irradiance, so that the solar panel 313 always outputs electrical energy at maximum power. The electrical energy is transmitted through the 400V DC bus to provide electrical energy for the subsequent charging process and the energy storage battery 311.

[0096] The bidirectional DCAC converter 314 connects the 400V DC bus and the 400V AC bus. When the energy storage battery 311 is fully charged, the bidirectional DCAC converter 314 converts DC power into AC power for charging the car. When the energy storage battery 311 is undercharged and the wind and solar power cannot be replenished in time, the energy storage battery 311 can be charged by converting the AC power generated by the dual-winding generator 32 into DC power.

[0097] When the charging station is in normal condition, that is, when strategy 1, strategy 2, strategy 3, and strategy 4 are selected, the dual-winding generator 32 is used as a transformer, and its control winding is connected to the 400V DC bus through the bidirectional DCAC converter 314, converting the 400V AC output by the wind-solar-storage system 31 into 800V AC for fast charging of the car, and outputting 400V AC for conventional charging of the car; further, according to the energy supply situation and cost-benefit analysis, it is decided whether to start the dual-winding generator 32 to optimize energy utilization and reduce operating costs. In the emergency situation where the charging station is in an abnormal state, that is, the wind-solar-storage system 31 and the power grid are unable to supply power, when strategy 5 is selected, the charging station adopts strategy 5: island operation mode, and the dual-winding generator 32 is started as a generator, and its control winding outputs 400V AC, which is connected to the ACDC converter 33 to output 400V DC for conventional charging of electric vehicles. The power winding side outputs 800V AC power, which is connected to the ACDC converter 34 to output 800V DC power for fast charging of electric vehicles, achieving simultaneous output of 400V and 800V.

[0098] The reserved mains interface 37 is connected to the 400V AC bus. When the wind, solar and storage system 31 combined power supply still cannot meet the vehicle charging demand, that is, when strategy 3 or strategy 4 is selected, the control device obtains electric energy from the power grid and charges the vehicle after corresponding conversion. During the off-peak period, according to the electricity price strategy and the charge of the energy storage battery 311, low-priced electric energy is obtained from the power grid and stored in the energy storage battery 311; during the peak period, the electric energy stored in the energy storage battery 311 is used to charge the vehicle first, and when necessary, electricity is taken from the power grid to achieve two-way flow and optimal configuration of electric energy between the power grid and the charging station.

[0099] The present application provides a charging station design method, a charging station design system and a charging station. Based on the long short-term memory network model (LSTM), a prediction model LSTM of energy generation power and load demand is established, which can more accurately predict the power generation and charging demand of renewable energy, thereby achieving more efficient energy distribution and management, and improving the operating efficiency of the charging station. The dual-winding generator design can output voltages of different voltage levels to meet the charging needs of various vehicles. There is no need to use a DCDC converter, which simplifies the circuit structure, reduces the loss in the energy conversion process, and improves the charging efficiency. The multi-voltage output characteristics of the dual-winding generator 32 enable the charging station to be compatible with the charging needs of various types of electric vehicles, improving the applicability and flexibility of the charging station. By integrating the wind, light and storage system 31, full use is made of renewable energy such as solar energy and wind energy, reducing dependence on traditional city electricity, reducing energy costs, and improving the sustainability of energy utilization of the charging station. Through the charging station design system and energy distribution strategy, the components in the charging station can be monitored and regulated in real time to ensure efficient distribution and flexible utilization of energy, and improve the stability and economy of the charging station operation.

[0100] In summary, the charging station design method, charging station design system and charging station provided in the present application significantly improve the energy efficiency, economy and stability of the charging station, while reducing operating costs and environmental impact.

[0101] Although the present application has been disclosed as above with the embodiments, it is not intended to limit the present application. Any person with ordinary knowledge in the technical field can make some changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be determined by the scope of the attached patent application.

Claims

1. A charging station design method, characterized in that: include, Step S1: Obtain real-time meteorological data and load data; Step S2: preprocessing the meteorological data and the load data to obtain preprocessed data; Performing feature extraction on the preprocessed data to obtain prediction features; Step S3: Establish a prediction model for energy generation power and load demand, and according to the prediction characteristics, use the established prediction model to obtain the initial wind power generation prediction power, initial solar power generation prediction power and initial load demand prediction power at a future moment; Step S4: Selecting an energy dispatch strategy based on the predicted power result of the prediction model, meteorological sensor data, and preset control rules; Step S5: Execute the selected energy scheduling strategy to adjust the working mode of the charging station.

2. A charging station design method as claimed in claim 1, characterized in that: In step S1, the meteorological data includes: meteorological sensor data and meteorological forecast data, wherein the meteorological sensor data includes: wind speed, solar irradiance, ambient temperature, relative humidity; the load data includes: load demand, DC bus voltage, and energy storage battery charge state.

3. A charging station design method as claimed in claim 1, characterized in that: In step S2, the preprocessing includes: maximum value normalization, sliding mean filtering, and data standardization; The feature extraction includes: extracting valuable features from the preprocessed data, including time series features: daily variation trends of wind speed and solar irradiance; extracting weather variation features based on meteorological data in the preprocessed data; generating load prediction features based on historical load trends of load data in the preprocessed data; and constructing periodic features; After the feature extraction, it also includes: calculating the weight of the extracted features to obtain the prediction features, and the prediction features include: normalized wind speed, normalized solar irradiance, standardized temperature, hourly cycle, first-order difference of load power, and load power.

4. A charging station design method as claimed in claim 1, characterized in that: In step S3, a long short-term memory network model is used to establish a prediction model for energy generation capacity and load demand, the input of the prediction model is the prediction feature, and the output of the prediction model is the initial wind power generation prediction power, the initial solar power generation prediction power, and the initial load demand prediction power at a future time.

5. A charging station design method as claimed in claim 4, characterized in that: After the prediction model outputs the predicted power result, it also includes: correcting the initial wind power generation predicted power and the initial solar power generation predicted power to obtain a corrected wind power generation predicted power and a corrected solar power generation predicted power.

6. A charging station design method as claimed in claim 5, characterized in that: In the step S4, the preset control rules include: step S41 to step S413; Step S41: energy dispatch control starts; Step S42: Determine whether the charging station status is normal, if so, proceed to step S43; if not, proceed to step S410; Step S43: determine whether the revised wind power generation forecast power and the revised solar power generation forecast power are greater than the load demand forecast power, if so, proceed to step S44, if not, proceed to step S46; Step S44: Select strategy 1: scenery-solar priority mode; Step S45: adjusting the output according to the modified wind power generation prediction power and the modified solar power generation prediction power; proceeding to step S411; Step S46: Determine the state of charge of the energy storage battery. If it is greater than 70%, proceed to step S47; if it is greater than 30% and less than 70%, proceed to step S48; if it is less than 30%, proceed to step S49; Step S47: select strategy 2: wind-solar-storage synergy mode, enable constant voltage-frequency ratio control, and proceed to step S411; Step S48: select strategy 3: storage-grid coordination mode 1, enable droop control, storage-grid joint output, and proceed to step S411; Step S49: Select strategy 4: storage-grid coordination mode 2, energy storage is charged from the grid, and proceed to step S411; Step S410: select strategy 5: island operation mode, enable virtual synchronous dual-winding generator control, and proceed to step S411; Step S411: executing the strategy; Step S412: Evaluate the energy dispatch control status, including: the state of charge of the energy storage battery, the error of the predicted power result, and the quality of the grid voltage. If the actual needs of the charging station are met, proceed to step S413; if not, return to step S41; Step S413: Energy scheduling control ends.

7. A charging station design method as claimed in claim 1, characterized in that: In the step S5, after selecting the energy scheduling strategy, it also includes: continuously optimizing the scheduling strategy through reinforcement learning.

8. A charging station design system, characterized in that: Executing a charging station design method as claimed in any one of claims 1 to 7, comprising: an environment perception module, a data preprocessing and feature extraction module, a deep learning and prediction module, an adaptive control module and an execution and scheduling module; The environment perception module, the data preprocessing and feature extraction module, the deep learning and prediction module, the adaptive control module and the execution and scheduling module are connected in sequence.

9. A charging station, characterized in that: Executing a charging station design method as claimed in any one of claims 1 to 7, comprising: Wind, solar and storage systems; A dual-winding generator, wherein a first end of the dual-winding generator is connected to a first end of the wind-solar-storage system; A first ACDC converter, wherein a first end of the first ACDC converter is respectively connected to a first end of the dual-winding generator and a first end of the wind-solar-storage system; A second ACDC converter, wherein a first end of the second ACDC converter is connected to a second end of the dual-winding generator; A 400V charging pile, wherein a first end of the 400V charging pile is connected to a second end of the first ACDC converter; An 800V charging pile, wherein a first end of the 800V charging pile is connected to a second end of the second ACDC converter; A mains power interface is reserved, and a first end of the reserved mains power interface is respectively connected to a first end of the wind-solar-storage system, a first end of the dual-winding generator, and a first end of the ACDC converter.

10. A charging station as claimed in claim 9, characterized in that: The wind-solar-storage system comprises: Energy storage batteries; A wind turbine, wherein a first end of the wind turbine is connected to a first end of the energy storage battery; A solar panel, wherein a first end of the solar panel is connected to a first end of the energy storage battery and a first end of the wind turbine generator respectively; A bidirectional DCAC converter, wherein the first end of the bidirectional DCAC converter is respectively connected to the first end of the energy storage battery, the first end of the wind turbine, and the first end of the solar panel; the second end of the bidirectional DCAC converter is the first end of the wind-solar-storage system.

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