Bidirectional energy management system and method for vehicle network interaction

Abstract

The invention provides a bidirectional energy management system and method for vehicle-network interaction, and the method comprises the steps: a distribution network information collection module collects distribution network information, an electric vehicle information collection module collects electric vehicle information, the electric vehicle information is preprocessed, and the preprocessed electric vehicle information is obtained; the data processing module processes the distribution network information to obtain the processed distribution network information, the data processing module processes the electric vehicle information to obtain the processed electric vehicle information, and the energy management scheduling module realizes energy management scheduling based on the processed distribution network information and the processed electric vehicle information. According to the bidirectional energy management system and method for vehicle-network interaction, bidirectional energy management of vehicle-network interaction can be realized based on new energy, the electric vehicle, the local load and the energy storage device, resources can be utilized to the greatest extent, and the system and method are convenient to use.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-grid interaction, and in particular to a bidirectional energy management system and method for vehicle-grid interaction. Background Art

[0002] As the future direction of automobile development, electric vehicles are rapidly popularizing around the world. While bringing development opportunities for smart grids, they also bring challenges to the safe and stable operation of power systems. The disordered charging behavior of large-scale electric vehicles connected to the grid will significantly increase the peak-to-valley difference of system load and reduce the reliability and economy of the power grid. In this context, the energy storage characteristics of electric vehicles themselves have received widespread attention.

[0003] Under the dual pressure of energy demand and environmental protection, although new energy power generation such as solar energy and wind energy has certain advantages, its power generation depends on natural resource conditions, which is intermittent and volatile, and difficult to regulate. Therefore, the large-scale connection of new energy power generation to the grid will affect the safe and stable operation of the power grid.

[0004] Therefore, it is very necessary to design a bidirectional energy management system and method based on renewable energy power generation for vehicle-grid interaction. Summary of the invention

[0005] The purpose of the present invention is to provide a two-way energy management system and method for vehicle-grid interaction, which can realize two-way energy management of vehicle-grid interaction based on new energy, electric vehicles, local loads and energy storage devices, maximize the use of resources and facilitate use.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A two-way energy management system for vehicle-grid interaction, comprising: a distribution network information collection module, an electric vehicle information collection module, a data processing module and an energy management scheduling module, wherein the distribution network information collection module and the electric vehicle information collection module are connected to the data processing module, and the data processing module is connected to the energy management scheduling module;

[0008] The distribution network information collection module is used to collect distribution network information;

[0009] The electric vehicle information acquisition module is used to collect electric vehicle information and preprocess the electric vehicle information to obtain preprocessed electric vehicle information; the processing process includes signal denoising, abnormal data cleaning and data normalization; the signal denoising process includes:

[0010] Shifting the original signal containing the electric vehicle information to obtain a shifted signal;

[0011] Performing wavelet decomposition on the translation signal to obtain a plurality of wavelet coefficients;

[0012] The filtering threshold is determined according to the decomposition scale and length of the signal; the filtering threshold is Among them, λ is the filtering threshold, j is the decomposition scale of the translation signal, and d j is the decomposition of wavelet coefficients with scale j, N represents the length of the translation signal, and median represents the median operation;

[0013] A denoising function is constructed according to the filtering threshold; the denoising function is: Among them, X represents the wavelet coefficient, n represents the adjustment coefficient;

[0014] Using the denoising function to remove the noise of the translation signal to obtain preprocessed electric vehicle information;

[0015] The data processing module is used to process the distribution network information and the electric vehicle information to obtain the processing result;

[0016] The energy management scheduling module is used to implement energy management scheduling according to the processing results.

[0017] The present invention also provides a bidirectional energy management method for vehicle-grid interaction, which is applied to the above-mentioned bidirectional energy management system for vehicle-grid interaction and includes the following steps:

[0018] Step 1: The distribution network information collection module collects distribution network information;

[0019] Step 2: The electric vehicle information acquisition module acquires the electric vehicle information and preprocesses the electric vehicle information to obtain the preprocessed electric vehicle information; the processing process includes signal denoising, abnormal data cleaning and data normalization; the signal denoising process includes:

[0020] Shifting the original signal containing the electric vehicle information to obtain a shifted signal;

[0021] Performing wavelet decomposition on the translation signal to obtain a plurality of wavelet coefficients;

[0022] The filtering threshold is determined according to the decomposition scale and length of the signal; the filtering threshold is Among them, λ is the filtering threshold, j is the decomposition scale of the translation signal, and d j is the decomposition of wavelet coefficients with scale j, N represents the length of the translation signal, and median represents the median operation;

[0023] A denoising function is constructed according to the filtering threshold; the denoising function is: Among them, X represents the wavelet coefficient, n represents the adjustment coefficient;

[0024] Using the denoising function to remove the noise of the translation signal to obtain preprocessed electric vehicle information;

[0025] Step 3: The data processing module processes the network distribution information to obtain the processed network distribution information;

[0026] Step 4: The data processing module processes the electric vehicle information to obtain processed electric vehicle information;

[0027] Step 5: The energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information.

[0028] Optionally, in step 1, the network distribution information collection module collects network distribution information, specifically:

[0029] The distribution network information collection module collects distribution network information, wherein the distribution network information includes the predicted photovoltaic and microgrid load power curves at each moment of the distribution network.

[0030] Optionally, in step 2, the electric vehicle information collection module collects electric vehicle information, specifically:

[0031] The electric vehicle information collection module collects electric vehicle information, wherein the electric vehicle information includes the models, initial power and power required for daily use of all electric vehicles in a preset parking lot. Exemplarily, the power signal in the electric vehicle information of the present application is data obtained after preprocessing by the electric vehicle information collection module.

[0032] Optionally, in step 3, the data processing module processes the network distribution information to obtain the processed network distribution information, specifically:

[0033] The data processing module calculates the power that renewable energy can provide based on the power curves of photovoltaic and microgrid loads at each moment predicted by the distribution network.

[0034] Optionally, in step 4, the data processing module processes the electric vehicle information to obtain processed electric vehicle information, specifically:

[0035] The data processing module obtains the models, initial power S(t) and daily power required S of all electric vehicles in the preset parking lot. sum , calculate the power gap L(t) as:

[0036] L(t)=S(t)-S sum

[0037] Establishing an electric vehicle status model within a day based on the connection status of the electric vehicle;

[0038] According to the initial power S(t) of each electric vehicle and the power required for daily use Ssum and the power gap L(t), and establish the electric vehicle power gap model;

[0039] Based on the electric vehicle status model and the electric vehicle power gap model within a day, it is judged whether the electric vehicle can participate in the dispatch at time t. If it can be dispatched, the upper and lower limit values ​​of the electric vehicle dispatch at this moment are calculated according to the power gap L(t).

[0040] Optionally, in step 5, the energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information, specifically:

[0041] Based on the calculated upper and lower limit values ​​of the electric vehicle dispatch at the moment, the maximum charging power and the maximum discharging power of the mth electric vehicle at the moment t are obtained;

[0042]

[0043] In the formula, are the maximum charging and discharging power of the mth electric vehicle in the tth period, SOC 1,m is the initial charge state of the mth electric vehicle in this period, E m is the power of the mth electric car when it is fully charged;

[0044] The above formula is modified based on the rated power of the electric vehicle to obtain:

[0045]

[0046] P 1N,m , P 2N,m are the rated charging and discharging powers of the mth electric vehicle respectively;

[0047] The common charging and discharging power of all electric vehicles at time t is calculated based on the maximum charging power and maximum discharging power of all electric vehicles. for:

[0048]

[0049] In the formula, are the maximum charging and discharging power of all electric vehicles respectively;

[0050] Based on the common charging and discharging power of electric vehicles Renewable energy can provide power Conduct energy management and scheduling;

[0051] If the power provided by renewable energy is Greater than or equal to the common charging power of electric vehicles but:

[0052] If the power grid is at its peak electricity price, the power Supply local loads in the distribution network;

[0053] If the power grid is in the off-peak period, the power Supplying charging to the energy storage device;

[0054] During off-peak hours, when the energy storage device is fully charged, the remaining power is supplied to the local load;

[0055] If the common charging power of electric vehicles Greater than the power available from renewable energy sources but:

[0056] If the power grid is in the low electricity price period, the power grid will provide power Meet the charging load of electric vehicles;

[0057] If the power grid is in a non-low-price period, the energy storage device will generate power Meet the charging load;

[0058] During non-peak hours, if energy storage cannot meet the load demand of electric vehicles, the excess load will be borne by the power grid;

[0059] If the grid is at peak electricity prices and the local load is less than the power that renewable energy can provide Common discharge power of electric vehicles and the sum of the power that can be provided by the energy storage device, then the local load is powered by renewable energy, electric vehicles and energy storage equipment;

[0060] If the grid is at peak electricity prices and the local load is greater than the power that renewable energy can provide Common discharge power of electric vehicles The sum of the power that can be provided by the energy storage device is then supplied by renewable energy, electric vehicles and energy storage equipment to the local loads, and the rest is supplied by the grid.

[0061] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the bidirectional energy management system and method for vehicle-grid interaction provided by the present invention, the system includes a distribution network information collection module, an electric vehicle information collection module, a data processing module and an energy management scheduling module, the method includes the distribution network information collection module collecting distribution network information, the electric vehicle information collection module collecting electric vehicle information, the data processing module processing the distribution network information to obtain processed distribution network information, the data processing module processing the electric vehicle information to obtain processed electric vehicle information, the energy management scheduling module realizes energy management scheduling based on the processed distribution network information and electric vehicle information, the system and method can realize bidirectional energy management of vehicle-grid interaction based on new energy, electric vehicles, local loads and energy storage devices, can maximize the use of resources, and are easy to use. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of the structure of a bidirectional energy management system for vehicle-grid interaction according to an embodiment of the present invention;

[0063] Figure 2 The figure is a flow chart of a bidirectional energy management method for vehicle-grid interaction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The purpose of the present invention is to provide a two-way energy management system and method for vehicle-grid interaction, which can realize two-way energy management of vehicle-grid interaction based on new energy, electric vehicles, local loads and energy storage devices, maximize the use of resources and facilitate use.

[0065] like Figure 1 As shown, the bidirectional energy management system for vehicle-grid interaction provided by the embodiment of the present invention includes: a distribution network information collection module, an electric vehicle information collection module, a data processing module and an energy management scheduling module, wherein the distribution network information collection module and the electric vehicle information collection module are connected to the data processing module, and the data processing module is connected to the energy management scheduling module;

[0066] The distribution network information collection module is used to collect distribution network information;

[0067] The electric vehicle information collection module is used to collect electric vehicle information;

[0068] The data processing module is used to process the distribution network information and the electric vehicle information to obtain the processing result;

[0069] The energy management scheduling module is used to implement energy management scheduling according to the processing results.

[0070] like Figure 2As shown, the present invention also provides a bidirectional energy management method for vehicle-grid interaction, which is applied to the above-mentioned bidirectional energy management system for vehicle-grid interaction and includes the following steps:

[0071] Step 1: The distribution network information collection module collects distribution network information;

[0072] Step 2: The electric vehicle information acquisition module acquires the electric vehicle information and preprocesses the electric vehicle information to obtain the preprocessed electric vehicle information; the processing process includes signal denoising, abnormal data cleaning and data normalization; the signal denoising process includes:

[0073] Shifting the original signal containing the electric vehicle information to obtain a shifted signal;

[0074] Performing wavelet decomposition on the translation signal to obtain a plurality of wavelet coefficients;

[0075] The filtering threshold is determined according to the decomposition scale and length of the signal; the filtering threshold is Among them, λ is the filtering threshold, j is the decomposition scale of the translation signal, and d j is the decomposition of wavelet coefficients with scale j, N represents the length of the translation signal, and median represents the median operation;

[0076] A denoising function is constructed according to the filtering threshold; the denoising function is: Among them, X represents the wavelet coefficient, n represents the adjustment coefficient;

[0077] Using the denoising function to remove the noise of the translation signal to obtain preprocessed electric vehicle information;

[0078] Step 3: The data processing module processes the network distribution information to obtain the processed network distribution information;

[0079] Step 4: The data processing module processes the electric vehicle information to obtain processed electric vehicle information;

[0080] Step 5: The energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information.

[0081] In step 1, the network distribution information collection module collects network distribution information, specifically:

[0082] The distribution network information collection module collects distribution network information, wherein the distribution network information includes the predicted photovoltaic and microgrid load power curves at each moment of the distribution network.

[0083] In step 2, the electric vehicle information collection module collects electric vehicle information, specifically:

[0084] The electric vehicle information collection module collects electric vehicle information, wherein the electric vehicle information includes the models, initial power and power required for daily use of all electric vehicles in a preset parking lot.

[0085] In step 3, the data processing module processes the network distribution information to obtain the processed network distribution information, specifically:

[0086] The data processing module calculates the power that renewable energy can provide based on the power curves of photovoltaic and microgrid loads at each moment predicted by the distribution network.

[0087] In step 4, the data processing module processes the electric vehicle information to obtain the processed electric vehicle information, specifically:

[0088] When hybrid electric vehicles run out of power (or reach the lower limit of discharge), there are still other energy sources to provide energy for their driving, while pure electric vehicles do not have such conditions. They must participate in orderly charging and discharging scheduling, and the remaining power can still ensure that the user's driving needs are met. Therefore, for pure electric vehicles, a user satisfaction model that considers the user's driving needs is established, that is, the power gap is calculated. The specific process is:

[0089] The data processing module obtains the models, initial power S(t) and daily power required S of all electric vehicles in the preset parking lot. sum , calculate the power gap L(t) as:

[0090] L(t)=S(t)-S sum

[0091] Among them, the amount of electricity required for daily use is S sum The calculation method is to first obtain the driving data of the electric vehicle within a certain period, and calculate its average daily driving power consumption according to the form data within the period, that is, the power required for daily use S sum ;

[0092] Establishing an electric vehicle status model within a day based on the connection status of the electric vehicle;

[0093] According to the initial power S(t) of each electric vehicle and the power required for daily use S sum and the power gap L(t), and establish the electric vehicle power gap model;

[0094] Based on the electric vehicle state model and electric vehicle power gap model within a day, it is judged whether the electric vehicle can participate in the dispatch at time t. If it can be dispatched, the upper and lower limit values ​​of the electric vehicle dispatch at this moment are calculated according to the power gap L(t). The specific process is as follows:

[0095] The states of electric vehicles are divided into four types: off-grid driving energy consumption, grid-connected controllable, grid-connected uncontrollable, and off-grid uncontrollable. When the electric vehicle is connected to the distribution network, the electric vehicle state at that moment is defined as grid-connected controllable; when the electric vehicle is connected to the grid, but the connected distribution network is a distribution network without a two-way energy management system, the electric vehicle state at that moment is defined as grid-connected uncontrollable; when the electric vehicle is off-grid and in the process of moving, the electric vehicle state at that moment is defined as off-grid driving energy consumption; when the electric vehicle is off-grid and in the process of parking, the electric vehicle state at that moment is defined as off-grid uncontrollable;

[0096] This application mainly dispatches electric vehicles that are connected to the network and can be controlled.

[0097] In step 5, the energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information, specifically:

[0098] Based on the calculated upper and lower limit values ​​of the electric vehicle dispatch at the moment, the maximum charging power and the maximum discharging power of the mth electric vehicle at the moment t are obtained;

[0099]

[0100] In the formula, are the maximum charging and discharging power of the mth electric vehicle in the tth period, SOC 1,m is the initial charge state of the mth electric vehicle in this period, E m is the power of the mth electric car when it is fully charged;

[0101] Because the maximum charge and discharge power of electric vehicles should take into account the rated power of their charging and discharging or the power limit of the charging and discharging facilities, the above formula is corrected based on the rated power to obtain:

[0102]

[0103] P 1N,m , P 2N,m are the rated charging and discharging powers of the mth electric vehicle respectively;

[0104] The common charging and discharging power of all electric vehicles at time t is calculated based on the maximum charging power and maximum discharging power of all electric vehicles. for:

[0105]

[0106] In the formula, are the maximum charging and discharging power of all electric vehicles respectively;

[0107] Based on the common charging and discharging power of electric vehicles Renewable energy can provide power Conduct energy management and scheduling;

[0108] If the power provided by renewable energy is Greater than or equal to the common charging power of electric vehicles but:

[0109] If the power grid is at its peak electricity price, the power Supply local loads in the distribution network;

[0110] If the power grid is in the off-peak period, the power Supplying charging to the energy storage device;

[0111] During off-peak hours, when the energy storage device is fully charged, the remaining power is supplied to the local load;

[0112] If the common charging power of electric vehicles Greater than the power available from renewable energy sources but:

[0113] If the power grid is in the low electricity price period, the power grid will provide power Meet the charging load of electric vehicles;

[0114] If the power grid is in a non-low-price period, the energy storage device will generate power Meet the charging load;

[0115] During non-peak hours, if energy storage cannot meet the load demand of electric vehicles, the excess load will be borne by the power grid;

[0116] If the grid is at peak electricity prices and the local load is less than the power that renewable energy can provide Common discharge power of electric vehicles and the sum of the power that can be provided by the energy storage device, then the local load is powered by renewable energy, electric vehicles and energy storage equipment;

[0117] If the grid is at peak electricity prices and the local load is greater than the power that renewable energy can provide Common discharge power of electric vehicles The sum of the power that can be provided by the energy storage device is then supplied by renewable energy, electric vehicles and energy storage equipment to the local loads, and the rest is supplied by the grid.

[0118] In addition, when the excess electricity of electric vehicles is dispatched to supply local loads, the distribution network manager will give certain subsidies to electric vehicle users who participate in system energy management.

[0119] The present invention provides a two-way energy management system and method for vehicle-grid interaction. The system includes a distribution network information collection module, an electric vehicle information collection module, a data processing module and an energy management scheduling module. The method includes the distribution network information collection module collecting distribution network information, the electric vehicle information collection module collecting electric vehicle information, the data processing module processing the distribution network information to obtain processed distribution network information, the data processing module processing the electric vehicle information to obtain processed electric vehicle information, and the energy management scheduling module realizing energy management scheduling based on the processed distribution network information and electric vehicle information. The system and method can realize two-way energy management of vehicle-grid interaction based on new energy, electric vehicles, local loads and energy storage devices, can maximize the use of resources, and are easy to use.

Claims

1. A two-way energy management system for vehicle-grid interaction, characterized in that: include: A distribution network information collection module, an electric vehicle information collection module, a data processing module and an energy management and scheduling module, wherein the distribution network information collection module and the electric vehicle information collection module are connected to the data processing module, and the data processing module is connected to the energy management and scheduling module; The distribution network information collection module is used to collect distribution network information; The electric vehicle information acquisition module is used to collect electric vehicle information and preprocess the electric vehicle information to obtain preprocessed electric vehicle information; the processing process includes signal denoising, abnormal data cleaning and data normalization; The signal denoising process includes: Shifting the original signal containing the electric vehicle information to obtain a shifted signal; Performing wavelet decomposition on the translation signal to obtain a plurality of wavelet coefficients; The filtering threshold is determined according to the decomposition scale and length of the signal; the filtering threshold is Among them, λ is the filtering threshold, j is the decomposition scale of the translation signal, and d j is the decomposition of wavelet coefficients with scale j, N represents the length of the translation signal, and median represents the median operation; A denoising function is constructed according to the filtering threshold; the denoising function is: Among them, X represents the wavelet coefficient, n represents the adjustment coefficient; Using the denoising function to remove the noise of the translation signal to obtain preprocessed electric vehicle information; The data processing module is used to process the distribution network information and the electric vehicle information to obtain the processing result; The energy management scheduling module is used to implement energy management scheduling according to the processing results.

2. A bidirectional energy management method for vehicle-grid interaction, applied to the bidirectional energy management system for vehicle-grid interaction according to claim 1, characterized in that: The steps include: Step 1: The distribution network information collection module collects distribution network information; Step 2: The electric vehicle information collection module collects electric vehicle information; Step 3: The data processing module processes the network distribution information to obtain the processed network distribution information; Step 4: The data processing module processes the electric vehicle information to obtain processed electric vehicle information; Step 5: The energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information.

3. The bidirectional energy management method for vehicle-grid interaction according to claim 2, characterized in that: In step 1, the network distribution information collection module collects network distribution information, specifically: The distribution network information collection module collects distribution network information, wherein the distribution network information includes the predicted photovoltaic and microgrid load power curves at each moment of the distribution network.

4. The bidirectional energy management method for vehicle-grid interaction according to claim 2, characterized in that: In step 2, the electric vehicle information collection module collects electric vehicle information, specifically: The electric vehicle information collection module collects electric vehicle information, wherein the electric vehicle information includes the models, initial power and power required for daily use of all electric vehicles in a preset parking lot.

5. The bidirectional energy management method for vehicle-grid interaction according to claim 2, characterized in that: In step 3, the data processing module processes the network distribution information to obtain the processed network distribution information, specifically: The data processing module calculates the power that renewable energy can provide based on the power curves of photovoltaic and microgrid loads at each moment predicted by the distribution network.

6. The bidirectional energy management method for vehicle-grid interaction according to claim 5, characterized in that: In step 4, the data processing module processes the electric vehicle information to obtain the processed electric vehicle information, specifically: The data processing module obtains the models, initial power S(t) and daily power required S of all electric vehicles in the preset parking lot. sum , calculate the power gap L(t) as: L(t)=S(t)-S sum Establishing an electric vehicle status model within a day based on the connection status of the electric vehicle; According to the initial power S(t) of each electric vehicle and the power required for daily use S sum and the power gap L(t), and establish the electric vehicle power gap model; Based on the electric vehicle status model and the electric vehicle power gap model within a day, it is judged whether the electric vehicle can participate in the dispatch at time t. If it can be dispatched, the upper and lower limit values ​​of the electric vehicle dispatch at this moment are calculated according to the power gap L(t).

7. The bidirectional energy management method for vehicle-grid interaction according to claim 6, characterized in that: In step 5, the energy management scheduling module implements energy management scheduling based on the processed distribution network information and electric vehicle information, specifically: Based on the calculated upper and lower limit values ​​of the electric vehicle dispatch at the moment, the maximum charging power and the maximum discharging power of the mth electric vehicle at the moment t are obtained; In the formula, are the maximum charging and discharging power of the mth electric vehicle in the tth period, SOC 1,m is the initial charge state of the mth electric vehicle in this period, E m is the power of the mth electric car when it is fully charged; The above formula is modified based on the rated power of the electric vehicle to obtain: P 1N,m , P 2N,m are the rated charging and discharging powers of the mth electric vehicle respectively; The common charging and discharging power of all electric vehicles at time t is calculated based on the maximum charging power and maximum discharging power of all electric vehicles. for: In the formula, are the maximum charging and discharging power of all electric vehicles respectively; Based on the common charging and discharging power of electric vehicles Renewable energy can provide power Conduct energy management and scheduling; If the power provided by renewable energy is Greater than or equal to the common charging power of electric vehicles but: If the power grid is at its peak electricity price, the power Supply local loads in the distribution network; If the power grid is in the off-peak period, the power Supplying charging to the energy storage device; During off-peak hours, when the energy storage device is fully charged, the remaining power is supplied to the local load; If the common charging power of electric vehicles Greater than the power available from renewable energy sources but: If the power grid is in the low electricity price period, the power grid will provide power Meet the charging load of electric vehicles; If the power grid is in a non-low-price period, the energy storage device will generate power Meet the charging load; During non-peak hours, if energy storage cannot meet the load demand of electric vehicles, the excess load will be borne by the power grid; If the grid is at a peak electricity price and the local load is less than the power that renewable energy can provide Common discharge power of electric vehicles and the sum of the power that can be provided by the energy storage device, then the local loads are powered by renewable energy, electric vehicles and energy storage equipment; If the grid is at peak electricity prices and the local load is greater than the power that renewable energy can provide Common discharge power of electric vehicles The sum of the power that can be provided by the energy storage device is then supplied by renewable energy, electric vehicles and energy storage equipment to the local loads, and the rest is supplied by the grid.

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