A virtual power plant frequency modulation method and system based on electric vehicle V2G

Abstract

The present invention discloses a virtual power plant frequency regulation method and system based on electric vehicle V2G, belonging to the field of distribution network frequency regulation technology. The specific steps include: establishing a distributed photovoltaic power generation system, energy storage system, charging station and communication of the power grid in a set area to obtain a virtual power plant; step S2: obtaining data in the virtual power plant and constructing a frequency regulation optimization model based on electric vehicle classification; step S3: adjusting the frequency of the virtual power plant according to the output frequency regulation strategy of the frequency regulation optimization model. At the same time, a system based on the above method is disclosed, which adopts the above virtual power plant frequency regulation method and system based on electric vehicle V2G to classify the charged electric vehicles, adopt different strategies to participate in the frequency regulation of the virtual power plant, and significantly reduce the overall cost, and ensure the normal use of electric private cars, the operation effect of electric operating vehicles and the stability of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network frequency regulation, and in particular to a virtual power plant frequency regulation method and system based on electric vehicle V2G. Background Art

[0002] V2G (Vehicle-to-Grid) technology is a technology that connects electric vehicles (EVs) to the power grid, allowing electric vehicles to not only obtain electricity from the grid but also to transmit electricity back to the grid. This technology utilizes the energy storage function of electric vehicle batteries, making electric vehicles a mobile energy storage device that can supply power to the grid when the grid demand is high and store electricity when the grid has excess power. By adjusting the charging and discharging time of electric vehicles, it can help smooth the grid load curve, reduce the peak load of the grid, and improve the efficiency of grid operation. Car owners can obtain economic benefits by participating in the peak load regulation service of the grid, and grid operators can also benefit by reducing the cost of electricity during peak hours. However, all electric vehicles are classified into one category in the prior art. For example, the patent with announcement number CN116961032B discloses a method for controlling the frequency regulation of electric vehicles based on edge-cloud collaboration, including dividing the charging area of ​​electric vehicles based on the travel characteristics of electric vehicles and constructing electric vehicle aggregators; counting the number of grid connections, charging demand and maximum charging power that can be carried out in each charging area within 24 hours, and the objective function is to minimize the total cost and conditional risk value of all electric vehicle aggregators to purchase electricity and obtain frequency regulation compensation, and dispatch electric vehicles to respond to the frequency regulation needs of the grid. Although the existing patents have divided the charging areas, in reality, electric private cars and electric commercial vehicles have different needs and adopt different charging methods. In addition, the charging location of electric commercial vehicles is uncertain. Using the same charging strategy will result in electric private cars and electric commercial vehicles being unable to obtain a charging method suitable for themselves, affecting the use of electric vehicles and the operating results of electric commercial vehicles. Summary of the invention

[0003] The purpose of the present invention is to provide a virtual power plant frequency regulation method and system based on electric vehicle V2G to solve the above technical problems.

[0004] To achieve the above object, the present invention provides a virtual power plant frequency modulation method based on electric vehicle V2G, and the specific steps are as follows:

[0005] Step S1: Establishing the communication between the distributed photovoltaic power generation system, energy storage system, charging station and power grid in the set area to obtain a virtual power plant;

[0006] Step S2: Obtain data in the virtual power plant and construct a frequency regulation optimization model based on electric vehicle classification; the objective function of the frequency regulation optimization model is as follows:

[0007]

[0008] in, For the The charging cost of an electric private car, is the number of private electric cars, For the The charging cost of an electric vehicle is is the number of electric operating vehicles, is the network loss cost; For a cycle The peak-to-valley difference within

[0009] Step S3: Adjust the frequency of the virtual power plant according to the output frequency regulation strategy of the frequency regulation optimization model.

[0010] Preferably, the electric private car data includes a state coefficient, a time when the electric car is plugged in to establish a connection with the charging station, a time when the electric car is unplugged to disconnect from the charging station, a charging and discharging power, a charge state, a minimum charge state, an energy value involved in frequency modulation, a maximum charging power, and a charge conversion coefficient;

[0011] The data of electric commercial vehicles include the state coefficient, the time when the power is plugged in to establish a connection with the charging station, the time when the charge state reaches the set value, the time when the power is unplugged to disconnect from the charging station, the charging power, the charge state and the minimum charge state.

[0012] Preferably, the power grid data includes electricity price, active power loss sensitivity, number of nodes, and net load power of electric vehicles.

[0013] Preferably, in step S2, The charging cost of an electric private car is calculated as follows:

[0014]

[0015] in, For the Electric private cars The state coefficient at the moment Electric private cars Always in charging state , when Electric private cars When in discharge state , For the The moment an electric private car plugs in and establishes a connection with a charging station, For the When an electric car is unplugged and disconnected from the charging station, For the Electric private cars The charging and discharging power at the moment, for The electricity price at the time;

[0016] No. The constraint function of the charging cost of an electric private car is as follows:

[0017]

[0018]

[0019] in, The first The charge state of an electric private car, The minimum state of charge set for electric car owners at the time of unplugging. is the charge conversion coefficient, is the energy value involved in frequency modulation, For the The maximum charging power of an electric private car.

[0020] Preferably, The charging cost calculation formula for an electric vehicle is as follows:

[0021]

[0022] in, For the Electric commercial vehicles The state coefficient at the moment Electric commercial vehicles Always in charging state , when Electric commercial vehicles When the power is off at any time , For the The moment an electric vehicle is plugged in to establish a connection with the charging station, For the When the charge state of an electric vehicle reaches the set value, For the When an electric vehicle is unplugged and disconnected from the charging station, For the Electric commercial vehicles Charging power at the moment;

[0023] No. The charging cost constraint function of an electric operating vehicle is as follows:

[0024]

[0025]

[0026] in, The first The charge status of an electric vehicle, The minimum state of charge set for electric commercial vehicle owners at the time of unplugging.

[0027] Preferably, the total power constraint function of electric private cars and electric commercial vehicles in the charging station is as follows:

[0028]

[0029]

[0030]

[0031] in, is the sum of the maximum charging power of electric private cars and electric commercial vehicles, is the sum of the maximum discharge powers of electric private cars, The number of electric private cars charged among electric private cars, The number of electric private cars that are charged in the electric private car market.

[0032] Preferably, The formula for calculating network loss cost is as follows:

[0033]

[0034] in, is the active power loss sensitivity, K is the number of nodes, For the The net load power of electric vehicles at each node.

[0035] Preferably, the traffic data within the set range of the electric vehicle is obtained through the virtual power plant. The traffic data includes the mileage from the passenger drop-off point where the electric vehicle arrives to the nearest charging station and the road condition coefficient of the corresponding road surface. The required power to reach the nearest charging station is solved based on the mileage and road condition coefficient. The calculation formula is as follows:

[0036]

[0037] in, The amount of electricity required for the electric vehicle to reach the nearest charging station. Regarding the required power and mileage and road condition coefficient The relationship function of

[0038] When the road is in a clear state, ;

[0039] When the road condition is chronic, ;

[0040] When the road is congested, ;

[0041] when hour, To ensure redundant power, the onboard controller of the electric vehicle prompts the electric vehicle to charge. It is the power of the electric vehicle at the current moment.

[0042] Preferably, The calculation formula is as follows:

[0043]

[0044] in, is the node impedance function in the mileage, is the segment impedance function between adjacent nodes in the mileage.

[0045] A system based on the above-mentioned virtual power plant frequency regulation method based on electric vehicle V2G includes:

[0046] The communication module is used to realize the communication connection between the distributed photovoltaic power generation system, energy storage system, charging station and power grid to form a virtual power plant; at the same time, the communication connection between the electric vehicle and the virtual power plant is established;

[0047] The on-board controller and positioning module in the electric vehicle, the positioning module is used to detect the position of the electric vehicle in real time; the on-board controller is used to calculate the required power and mileage and road condition coefficient The required power is calculated by the relationship function;

[0048] A frequency regulation optimization model based on electric vehicle classification is used to output frequency regulation strategies based on virtual power plant data;

[0049] The execution module is used to execute the frequency modulation strategy.

[0050] Therefore, the present invention adopts the above-mentioned virtual power plant frequency regulation method and system based on electric vehicle V2G, which has the beneficial effects of classifying the charged electric vehicles and adopting different strategies to participate in the frequency regulation of the virtual power plant, which significantly reduces the overall cost and ensures the normal use of electric private cars, the operation effect of electric operating vehicles and the stability of the power grid.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of a virtual power plant frequency modulation method based on electric vehicle V2G of the present invention;

[0053] Figure 2 This is a block diagram of the system principle of the present invention;

[0054] Figure 3 is a load curve diagram of the present invention;

[0055] Figure 4 The real-time electricity price curve diagram of the present invention;

[0056] Figure 5 It is a bar chart of comprehensive cost of the present invention. DETAILED DESCRIPTION

[0057] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the invented product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, it should also be noted that, unless otherwise clearly specified and limited, the terms "setting", "installation", and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] like Figure 1 As shown, a virtual power plant frequency regulation method based on electric vehicle V2G, the specific steps are as follows:

[0060] Step S1: Establishing the communication between the distributed photovoltaic power generation system, energy storage system, charging station and power grid in a set area to obtain a virtual power plant.

[0061] Step S2: Obtain data within the virtual power plant and build a frequency regulation optimization model based on electric vehicle classification.

[0062] The data for private electric cars include the state coefficient, the time when the car is plugged in to establish a connection with the charging station, the time when the car is unplugged to disconnect from the charging station, the charging and discharging power, the charge state, the minimum charge state, the energy value involved in frequency modulation, the maximum charging power and the charge conversion coefficient.

[0063] The data of electric commercial vehicles include the state coefficient, the time when the power is plugged in to establish a connection with the charging station, the time when the charge state reaches the set value, the time when the power is unplugged to disconnect from the charging station, the charging power, the charge state and the minimum charge state.

[0064] The grid data includes electricity price, active power loss sensitivity, number of nodes, and net load power of electric vehicles. At the same time, the data of distributed photovoltaic power generation system and energy storage system are also collected to facilitate the subsequent calculation of peak-to-valley difference. This part is a conventional setting in this field and will not be discussed in detail here.

[0065] The objective function of the frequency modulation optimization model is as follows:

[0066]

[0067] in, For the The charging cost of an electric private car, is the number of private electric cars, For the The charging cost of an electric vehicle is is the number of electric operating vehicles, is the network loss cost; For a cycle The peak-to-valley difference within.

[0068] No. The charging cost of an electric private car is calculated as follows:

[0069]

[0070] in, For the Electric private cars The state coefficient at the moment Electric private cars Always in charging state , when Electric private cars When in discharge state , For the The moment an electric private car plugs in and establishes a connection with a charging station, For the When an electric car is unplugged and disconnected from the charging station, For the Electric private cars The charging and discharging power at the moment, for The electricity price at the time;

[0071] No. The constraint function of the charging cost of an electric private car is as follows:

[0072]

[0073]

[0074] in, The first The charge state of an electric private car, The minimum state of charge set for electric car owners at the time of unplugging. is the charge conversion coefficient, is the energy value involved in frequency modulation, For the The maximum charging power of an electric private car.

[0075] No. The charging cost calculation formula for an electric vehicle is as follows:

[0076]

[0077] in, For the Electric commercial vehicles The state coefficient at the moment Electric commercial vehicles Always in charging state , when Electric commercial vehicles When the power is off at any time , For the The moment an electric vehicle is plugged in to establish a connection with the charging station, For the When the charge state of an electric vehicle reaches the set value, For the When an electric vehicle is unplugged and disconnected from the charging station, For the Electric commercial vehicles Charging power at the moment;

[0078] No. The charging cost constraint function of an electric operating vehicle is as follows:

[0079]

[0080]

[0081] in, The first The charge status of an electric vehicle, The minimum state of charge set for electric commercial vehicle owners at the time of unplugging.

[0082] The total power constraint function of electric private cars and electric commercial vehicles in the charging station is as follows:

[0083]

[0084]

[0085]

[0086] in, is the sum of the maximum charging power of electric private cars and electric commercial vehicles, is the sum of the maximum discharge powers of electric private cars, The number of electric private cars charged among electric private cars, The number of electric private cars that are charged in the electric private car market.

[0087] The formula for calculating network loss cost is as follows:

[0088]

[0089] in, is the active power loss sensitivity, K is the number of nodes, For the The net load power of electric vehicles at each node.

[0090] The virtual power plant obtains traffic data within the set range of the electric operating vehicle. The traffic data includes the mileage from the passenger drop-off point to the nearest charging station and the road condition coefficient of the corresponding road surface. The required power to reach the nearest charging station is solved based on the mileage and road condition coefficient. The calculation formula is as follows:

[0091]

[0092] in, The amount of electricity required for the electric vehicle to reach the nearest charging station. Regarding the required power and mileage and road condition coefficient The relationship function of

[0093] When the road is in a clear state, ;

[0094] When the road condition is chronic, ;

[0095] When the road is congested, ;

[0096] when hour, To ensure redundant power, the onboard controller of the electric vehicle prompts the electric vehicle to charge. It is the power of the electric vehicle at the current moment.

[0097] Preferably, The calculation formula is as follows:

[0098]

[0099] in, is the node impedance function in the mileage, is the segment impedance function between adjacent nodes in the mileage.

[0100] Step S3: Adjust the frequency of the virtual power plant according to the output frequency regulation strategy of the frequency regulation optimization model.

[0101] like Figure 2 As shown, a system based on the above-mentioned method for virtual power plant frequency regulation based on electric vehicle V2G includes:

[0102] The communication module is used to realize the communication connection between the distributed photovoltaic power generation system, energy storage system, charging station and power grid to form a virtual power plant; at the same time, the communication connection between the electric vehicle and the virtual power plant is established;

[0103] The on-board controller and positioning module in the electric vehicle, the positioning module is used to detect the position of the electric vehicle in real time; the on-board controller is used to calculate the required power and mileage and road condition coefficient The required power is calculated by the relationship function;

[0104] A frequency regulation optimization model based on electric vehicle classification is used to output frequency regulation strategies based on virtual power plant data;

[0105] The execution module is used to execute the frequency modulation strategy.

[0106] In order to verify the superiority of the method adopted in this embodiment, a test experiment was conducted on the method without frequency modulation and the method adopting the frequency modulation strategy of this embodiment. The test results are as follows: Figure 3 , Figure 4 as well as Figure 5 As shown in the figure, it can be seen that the load level is more stable and the peak-to-valley difference is significantly reduced by adopting the technical solution of this embodiment. At the same time, the overall cost is also reduced.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A virtual power plant frequency modulation method based on electric vehicle V2G, characterized in that: The specific steps are as follows: Step S1: Establishing the communication between the distributed photovoltaic power generation system, energy storage system, charging station and power grid in the set area to obtain a virtual power plant; Step S2: Obtain data in the virtual power plant and construct a frequency regulation optimization model based on electric vehicle classification; the objective function of the frequency regulation optimization model is as follows: in, For the The charging cost of an electric private car, is the number of private electric cars, For the The charging cost of an electric vehicle is is the number of electric operating vehicles, is the network loss cost; For a cycle The peak-to-valley difference within No. The charging cost of an electric private car is calculated as follows: in, For the Electric private cars The state coefficient at the moment Electric private cars Always in charging state , when Electric private cars When in discharge state , For the The moment an electric private car plugs in and establishes a connection with a charging station, For the When an electric car is unplugged and disconnected from the charging station, For the Electric private cars The charging and discharging power at the moment, for The electricity price at the time; No. The constraint function of the charging cost of an electric private car is as follows: in, The first The charge state of an electric private car, The minimum state of charge set for electric car owners at the time of unplugging. is the charge conversion coefficient, is the energy value involved in frequency modulation, For the Maximum charging power for electric private vehicles; No. The charging cost calculation formula for an electric vehicle is as follows: in, For the Electric commercial vehicles The state coefficient at the moment Electric commercial vehicles Always in charging state , when Electric commercial vehicles When the power is off at any time , For the The moment an electric vehicle is plugged in to establish a connection with the charging station, For the When the charge state of an electric vehicle reaches the set value, For the When an electric vehicle is unplugged and disconnected from the charging station, For the Electric commercial vehicles Charging power at the moment; No. The charging cost constraint function of an electric operating vehicle is as follows: in, The first The charge status of an electric vehicle, The minimum state of charge set for electric vehicle owners at the time of unplugging; Step S3: Adjust the frequency of the virtual power plant according to the output frequency regulation strategy of the frequency regulation optimization model.

2. The method for frequency modulation of a virtual power plant based on electric vehicle V2G according to claim 1, characterized in that: The data of electric private cars include the state coefficient, the time of plugging in to establish connection with the charging station, the time of unplugging and disconnecting from the charging station, charging and discharging power, charge state, minimum charge state, energy value involved in frequency modulation, maximum charging power and charge conversion coefficient; The data of electric commercial vehicles include the state coefficient, the time when the power is plugged in to establish a connection with the charging station, the time when the charge state reaches the set value, the time when the power is unplugged to disconnect from the charging station, the charging power, the charge state and the minimum charge state.

3. The method for frequency modulation of a virtual power plant based on electric vehicle V2G according to claim 2 is characterized in that: Grid data include electricity price, active power loss sensitivity, number of nodes, and net load power of electric vehicles.

4. The method for frequency modulation of a virtual power plant based on electric vehicle V2G according to claim 1, characterized in that: The total power constraint function of electric private cars and electric commercial vehicles in the charging station is as follows: in, is the sum of the maximum charging power of electric private cars and electric commercial vehicles, is the sum of the maximum discharge powers of electric private cars, The number of electric private cars charged among electric private cars, is the number of electric private cars discharged from the electric private cars.

5. A virtual power plant frequency modulation method based on electric vehicle V2G according to claim 4, characterized in that: The formula for calculating network loss cost is as follows: in, is the active power loss sensitivity, K is the number of nodes, For the The net load power of electric vehicles at each node.

6. A virtual power plant frequency modulation method based on electric vehicle V2G according to claim 5, characterized in that: The traffic data within the set range of the electric operating vehicle is obtained through the virtual power plant. The traffic data includes the mileage from the passenger drop-off point to the nearest charging station and the road condition coefficient of the corresponding road surface. The required power to reach the nearest charging station is solved based on the mileage and road condition coefficient. The calculation formula is as follows: in, The amount of electricity required for the electric vehicle to reach the nearest charging station. Regarding the required power and mileage and road condition coefficient The relationship function of When the road is in a clear state, ; When the road condition is chronic, ; When the road is congested, ; when hour, To ensure redundant power, the onboard controller of the electric vehicle prompts the electric vehicle to charge. It is the power of the electric vehicle at the current moment.

7. A virtual power plant frequency modulation method based on electric vehicle V2G according to claim 6, characterized in that: The calculation formula is as follows: in, is the node impedance function in the mileage, is the segment impedance function between adjacent nodes in the mileage.

8. A system for a virtual power plant frequency modulation method based on electric vehicle V2G according to claim 7, characterized in that: include: The communication module is used to realize the communication connection between the distributed photovoltaic power generation system, energy storage system, charging station and power grid to form a virtual power plant; at the same time, the communication connection between the electric vehicle and the virtual power plant is established; The on-board controller and positioning module in the electric vehicle, the positioning module is used to detect the position of the electric vehicle in real time; the on-board controller is used to calculate the required power and mileage and road condition coefficient The required power is calculated by the relationship function; A frequency regulation optimization model based on electric vehicle classification is used to output frequency regulation strategies based on virtual power plant data; The execution module is used to execute the frequency modulation strategy.

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