A power regulation method, apparatus and electronic device

Abstract

The application discloses a power regulation method and device and electronic equipment, and relates to the technical field of power regulation. A regulation coefficient corresponding to a current moment is determined based on actual carbon emission and average reference carbon emission corresponding to the current moment, and then an upper limit value of charging power corresponding to a target electric vehicle at the current moment is determined. An optimization model is constructed, the upper limit value of charging power corresponding to the target electric vehicle at the current moment and charging demand of the target electric vehicle within a preset period are input into the optimization model, the optimization model is solved with the target of minimizing the total operation cost of a virtual power plant, and a power regulation strategy is obtained. The carbon emission intensity guides the charging behavior of the electric vehicle, and the energy storage system is used as an auxiliary adjustment resource to realize new energy consumption and carbon emission optimization operation. The system carbon transaction cost is reduced while the balance between power supply and demand and the charging demand of users are ensured.

Description

Technical Field

[0001] This application relates to the field of power regulation technology, and in particular to a power regulation method, device and electronic device. Background Technology

[0002] In recent years, carbon emission constraints have been introduced into virtual power plant optimization models to achieve low-carbon dispatch by constructing carbon emission cost or carbon quota constraints. However, most existing methods use static carbon emission factors or average emission levels for modeling, failing to fully characterize the dynamic changes in power system carbon emissions with operating conditions. This results in insufficient adaptability of dispatch strategies to real-time low-carbon operation requirements. Furthermore, it is difficult to simultaneously ensure users' charging needs within a preset period while also considering system carbon emission control and operational economics. Summary of the Invention

[0003] In view of this, this application provides a charging power regulation method, apparatus and electronic device to solve the above-mentioned problems existing in the prior art.

[0004] The objective of this application can be achieved through the following technical solutions: The first aspect of this application is to provide a power regulation method, comprising: Obtain the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle; The average baseline carbon emissions are determined based on the baseline carbon emissions at each time point within a preset period for the target electric vehicle. The adjustment coefficient for the current moment is determined based on the actual carbon emissions and the average baseline carbon emissions at the current moment. Get the current charging power limit of the charging pile; The upper limit of the charging power of the target electric vehicle at the current moment is determined based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile. An optimization model is constructed by inputting the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within the preset period.

[0005] In an optional embodiment, the constraint further includes: the charging power of the target electric vehicle at the current moment is not greater than the maximum charging power of the target electric vehicle.

[0006] In one optional embodiment, the total operating cost includes the sum of energy purchase cost, carbon trading cost, and gas turbine power generation cost within a preset period, wherein the energy purchase cost is the product of grid-purchased power and grid-purchased electricity price; The formula for calculating carbon trading costs is: ; in, This represents the carbon trading cost at time t. Indicates the price in the carbon trading market. This represents the actual carbon emissions at time t. The carbon emission allowance corresponding to time t; The formula for calculating actual carbon emissions is: ; in, This represents the carbon emission coefficient of the gas turbine at time t. This represents the gas turbine power at time t. This represents the carbon emission coefficient of the power grid at time t. This represents the grid power at time t; The formula for calculating carbon emission allowances is: ; in, This represents the carbon emission allowance coefficient at time t. This represents the gas turbine power at time t. This represents the wind power output at time t. This represents the photovoltaic power at time t. This indicates the mileage traveled by the target electric vehicle within a preset period. This indicates the carbon emission factor per unit mileage of a gasoline-powered vehicle. This represents the carbon emission factor per unit mileage of an electric vehicle. The expression for calculating the cost of gas turbine power generation is: ; This represents the cost of gas turbine power generation at time t. This indicates the price of gas turbine power generation.

[0007] In an optional embodiment, the power regulation strategy further includes the energy storage charging power and energy storage discharging power at the current moment, and the constraint condition further includes a power balance constraint, the expression of which is: ; in, This represents the energy storage discharge power at time t. This represents the load power at time t. This represents the charging power of the target electric vehicle at time t. This represents the energy storage charging power at time t.

[0008] In one optional embodiment, the constraints further include: the power purchased by the grid is not greater than the maximum power purchased by the grid, and the power of the gas turbine is within a preset range of gas turbine power.

[0009] In an optional embodiment, the constraints further include: The energy storage capacity is within the preset energy storage capacity range, and / or the energy storage charging power and energy storage discharging power at the current moment are mutually exclusive.

[0010] In one optional embodiment, obtaining the baseline carbon emissions of the target electric vehicle within a preset period in the virtual power plant includes: Obtain the baseline charging power of the target electric vehicle at each moment within a preset cycle in the virtual power plant; The gas turbine power and grid power corresponding to the benchmark charging power are determined based on the benchmark charging power and power balance constraints. The baseline carbon emissions corresponding to the baseline charging power are determined based on the gas turbine power and grid power corresponding to the baseline charging power.

[0011] In one optional embodiment, after obtaining the reference charging power corresponding to each moment within a preset period of the target electric vehicle in the virtual power plant, the method further includes: Obtain the probability density distribution of the target electric vehicle's battery capacity, the probability density distribution of the preset cycle driving mileage, and the probability distribution of the initial charging time. The driving mileage of the target electric vehicle within a preset period and the driving mileage per unit of electricity are determined based on the probability density distribution of the battery capacity of the target electric vehicle and the probability density distribution of the driving mileage within a preset period. Based on the target electric vehicle’s mileage within a preset period and the target electric vehicle’s mileage per unit of electricity, determine the target electric vehicle’s charging requirements within a preset period. The charging duration of the target electric vehicle within the preset period is determined based on the charging demand, charging power, and charging efficiency of the charging pile within the preset period of the target electric vehicle. Based on the probability distribution of the charging duration and start charging time of the target electric vehicle within a preset cycle, a charging load curve of the target electric vehicle in the virtual power plant is generated. The charging load curve is used to indicate the reference charging power of the target electric vehicle at each moment. The charging load curves of the target electric vehicles in the virtual power plant are summed to obtain the total charging load curve, which is used to indicate the reference charging power of all target electric vehicles in the virtual power plant at each time.

[0012] A third aspect of this application is to provide a power regulation device, comprising: The first acquisition module is used to acquire the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle. The first determining module is used to determine the average baseline carbon emissions based on the baseline carbon emissions corresponding to each moment within a preset period of the target electric vehicle. The second determining module is used to determine the adjustment coefficient corresponding to the current time based on the actual carbon emissions and the average baseline carbon emissions at the current time. The second acquisition module is used to acquire the upper limit value of the charging power of the charging pile at the current moment. The third determining module is used to determine the upper limit of the charging power of the target electric vehicle at the current moment based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment and the upper limit of the charging power of the charging pile; The fourth determination module is used to construct an optimization model. It inputs the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within a preset period.

[0013] A third aspect of this application is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the method as described in the first aspect.

[0014] A fourth aspect of this application is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method as described in the first aspect.

[0015] Compared with existing technologies, the power regulation method provided in this application determines the regulation coefficient corresponding to the current moment based on the actual carbon emissions and the average benchmark carbon emissions, and then determines the upper limit of the charging power corresponding to the target electric vehicle at the current moment. An optimization model is constructed, and the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period are input into the optimization model. The optimization model is solved with the objective of minimizing the total operating cost of the virtual power plant, resulting in a power regulation strategy. By guiding electric vehicle charging behavior through carbon emission intensity and utilizing energy storage systems as auxiliary regulation resources, the method achieves both renewable energy consumption and optimized carbon emission operation, reducing system carbon trading costs while ensuring power supply and demand balance and meeting user charging needs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A schematic flowchart of a power regulation method provided in an embodiment of this application; Figure 2 A structural block diagram of the power regulation device provided in the embodiments of this application; Figure 3 This is a structural block diagram of an electronic device for implementing a power regulation method, provided in an embodiment of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] It should be understood that in the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the related objects before and after it are in an "or" relationship. "Contains A, B and / or C" means containing any one, two, or three of A, B, and C.

[0021] It should be understood that in the embodiments of this application, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0022] To address the technical problems existing in related technologies, embodiments of this application provide a power regulation method, apparatus, and electronic device.

[0023] The power regulation method provided in this application can be executed by an electronic device, such as a terminal or a server. The terminal can be a smartphone, tablet, laptop, or other similar device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. It is understood that this application does not limit the specific entity executing the power regulation method.

[0024] The technical solution of this application will be described in detail below through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments described below are used to explain the technical solution of this application and are not intended to limit actual use.

[0025] To address the technical problems existing in related technologies, embodiments of this application provide a power regulation method, such as... Figure 1 As shown, Figure 1 This is a flowchart of a power regulation method provided in an embodiment of this application. It should be noted that the steps shown may be executed in a logical order different from that shown in the flowchart. The method may include the following steps S101 to S106.

[0026] Step S101: Obtain the baseline carbon emissions of the target electric vehicle at each moment within a preset cycle in the virtual power plant.

[0027] It should be noted that virtual power plants aggregate resources scattered throughout cities, such as distributed photovoltaic systems, energy storage batteries, electric vehicle charging stations, and adjustable equipment in factories, through technologies like the Internet of Things and artificial intelligence. This allows for unified and optimized scheduling and grid trading. Target electric vehicles refer to electric vehicles that can be connected to and participate in the scheduling process within the virtual power plant.

[0028] In one optional embodiment, the preset period can be 6 hours, 24 hours, or 2 days, etc., and this application does not limit it.

[0029] In another optional embodiment, obtaining the baseline carbon emissions corresponding to each moment within a preset period of the target electric vehicle in the virtual power plant includes: obtaining the baseline charging power corresponding to each moment within a preset period of the target electric vehicle in the virtual power plant; determining the gas turbine power and grid power corresponding to the baseline charging power based on the baseline charging power and power balance constraints; and determining the baseline carbon emissions corresponding to the baseline charging power based on the gas turbine power and grid power corresponding to the baseline charging power.

[0030] It should be noted that the benchmark charging power refers to the conventional dispatch result of the virtual power plant under conditions where carbon intensity regulation mechanisms are not considered. The process of determining the gas turbine power and grid power corresponding to the benchmark charging power based on the benchmark charging power and power balance constraints, and determining the benchmark carbon emissions corresponding to the benchmark charging power based on the gas turbine power and grid power corresponding to the benchmark charging power, will be detailed in subsequent steps.

[0031] In one alternative embodiment, the reference charging power can be expressed as: t represents time. The baseline carbon emissions can be expressed as... .

[0032] In another optional embodiment, after obtaining the reference charging power corresponding to each moment within a preset period of the target electric vehicle in the virtual power plant, the method further includes: The process involves obtaining the probability density distributions of the target electric vehicle's battery capacity, preset period mileage, and initial charging time; determining the target electric vehicle's mileage within the preset period and its mileage per unit of electricity based on these probability density distributions; determining the target electric vehicle's charging demand within the preset period based on these mileages; determining the charging duration within the preset period based on the charging demand, charging power, and charging efficiency of the charging piles; generating a charging load curve for the target electric vehicle within a virtual power plant based on the probability distributions of the charging duration and initial charging time, which indicates the baseline charging power of the target electric vehicle at each moment; and summing the charging load curves of the target electric vehicles within the virtual power plant to obtain the total charging load curve, which indicates the baseline charging power of all target electric vehicles within the virtual power plant at each moment.

[0033] In one specific embodiment, the battery capacity of an electric vehicle determines the maximum energy it can store. This not only constrains the amount of energy charged over a preset period but also affects charging time and scheduling flexibility. Currently, most electric vehicle battery capacities are between 30-60 kWh, represented by a uniform probability function: ; in, The probability density distribution function represents the battery capacity.

[0034] In another specific embodiment, the preset cycle mileage of electric vehicles exhibits significant uncertainty and randomness; the mileage may vary considerably among different users, in different usage scenarios, and at different times. To reasonably model the electric vehicle charging load in power systems, virtual power plants, or carbon emission analysis, a probability density function is needed to characterize the statistical properties of the preset cycle mileage, which can be expressed as: ; in, This represents the probability density distribution of the driving mileage over a preset period. Indicates the preset cycle mileage. This represents the standard deviation of the mileage traveled over a preset period. This represents the expected mileage for a preset period.

[0035] In a more specific embodiment, =3.2, =0.88.

[0036] In another specific embodiment, the probability distribution of the initial charging time can be expressed as: ; in, The probability distribution function representing the initial charging time. The standard deviation of the initial charging time. Let t represent the expected value of the initial charging time, and t represent time.

[0037] In a more specific embodiment, =3.4, This indicates 17.6.

[0038] In another specific embodiment, determining the driving mileage of the target electric vehicle within a preset period and the driving mileage of the target electric vehicle per unit of electricity based on the probability density distribution of the battery capacity of the target electric vehicle and the probability density distribution of the driving mileage within a preset period includes: obtaining the battery capacity of the target electric vehicle based on the probability density distribution of the battery capacity of the target electric vehicle, determining the driving mileage of the target electric vehicle within a preset period based on the probability density distribution of the driving mileage within a preset period, and determining the quotient of the driving mileage of the target electric vehicle within a preset period and the battery capacity of the target electric vehicle as the driving mileage of the target electric vehicle within a preset period and the driving mileage of the target electric vehicle per unit of electricity.

[0039] In a more specific embodiment, the target electric vehicle can achieve a range of 6 km / kWh per unit of electricity.

[0040] In another specific embodiment, the ultimate goal of the target electric vehicle charging scheduling is to ensure that electric vehicles have enough charge each day to complete their driving tasks. Without clearly defined daily charging needs, the optimization model might schedule too few charges or at inappropriate times, preventing the vehicles from fulfilling their daily usage requirements.

[0041] Based on the target electric vehicle's mileage within a preset period and its mileage per unit of electricity, the charging demand for the target electric vehicle within a preset period is calculated using the following formula: ; in, This indicates the charging demand of the target electric vehicle within a preset period. This indicates the target electric vehicle's preset mileage over a given period. This indicates the driving range of the target electric vehicle per unit of electricity.

[0042] In another specific embodiment, the charging time is determined by the amount of charging required within a preset cycle and the actual charging power, which represents the time required for the vehicle to continuously charge in the power grid to complete daily charging.

[0043] Based on the charging demand of the target electric vehicle within a preset period, the charging power of the charging pile, and the charging efficiency of the charging pile, the charging time of the target electric vehicle within a preset period is calculated using the following formula: ; in, This indicates the charging time of the target electric vehicle within a preset cycle. This indicates the charging demand of the target electric vehicle within a preset period. This indicates the charging power of the charging station. This indicates the charging efficiency of the charging station.

[0044] In this step, a benchmark charging power and benchmark carbon emissions are obtained as a reference for comparing emission reduction effects and carbon trading.

[0045] Step S102: Determine the average baseline carbon emissions based on the baseline carbon emissions corresponding to each moment within the preset cycle of the target electric vehicle.

[0046] In one alternative embodiment, the average baseline carbon emissions are calculated using the following formula: ; in, This represents the average baseline carbon emissions. Indicates the preset period. This represents the baseline carbon emissions at time t.

[0047] Step S103: Determine the adjustment coefficient corresponding to the current time based on the carbon emissions at the current time and the average baseline carbon emissions.

[0048] In one optional embodiment, the adjustment coefficient for the current time is calculated using the following formula, based on the current carbon emissions and the average baseline carbon emissions: ; in, This represents the adjustment coefficient at time t. This represents the actual carbon emissions at time t. This represents the average baseline carbon emissions.

[0049] Step S104: Obtain the upper limit of the charging power of the charging pile at the current moment.

[0050] In one optional embodiment, obtaining the upper limit value of the charging power of the charging pile at the current moment includes: ; in, This represents the upper limit of the charging power of the charging pile at time t. This represents the number of target electric vehicles within the virtual power plant at time t. This indicates the charging power of the charging station.

[0051] Step S105: Determine the upper limit of the charging power corresponding to the target electric vehicle at the current moment based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile.

[0052] In one optional embodiment, based on a preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile, the upper limit of the charging power corresponding to the target electric vehicle at the current moment is calculated using the following formula: ; in, This represents the upper limit of the target electric vehicle charging power at time t. This represents the upper limit of the charging power of the charging pile at time t. Indicates the preset response sensitivity. This represents the adjustment coefficient at time t.

[0053] It should be noted that the preset response sensitivity range is (0, 1), and can be set according to actual needs. This application does not limit this value.

[0054] Step S106: Construct an optimization model. Input the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within the preset period into the optimization model. Solve the optimization model with the goal of minimizing the total operating cost of the virtual power plant to obtain the power regulation strategy.

[0055] It should be noted that the optimization model refers to the mathematical rules used by the solver to automatically find the optimal power regulation scheme.

[0056] In one optional embodiment, the total operating cost includes the sum of energy purchase cost, carbon trading cost, and gas turbine power generation cost within a preset period, wherein the energy purchase cost is the product of grid-purchased power and grid-purchased electricity price; The formula for calculating the minimum total operating cost within a preset period is: ; in, This represents the total operating cost, and T represents the preset cycle. This represents the cost of gas turbine power generation at time t. This represents the carbon trading cost at time t. This represents the energy purchase cost at time t.

[0057] The formula for calculating carbon trading costs is: ; in, This represents the carbon trading cost at time t. Indicates the price in the carbon trading market. This represents the actual carbon emissions at time t. The carbon emission allowance corresponding to time t; The formula for calculating actual carbon emissions is: ; in, This represents the carbon emission coefficient of the gas turbine at time t. This represents the gas turbine power at time t. This represents the carbon emission coefficient of the power grid at time t. This represents the grid power at time t; The formula for calculating carbon emission allowances is: ; in, This represents the carbon emission allowance coefficient at time t. This represents the gas turbine power at time t. This represents the wind power output at time t. This represents the photovoltaic power at time t. This indicates the mileage traveled by the target electric vehicle within a preset period. This indicates the carbon emission factor per unit mileage of a gasoline-powered vehicle. This represents the carbon emission factor per unit of electric vehicle mileage.

[0058] In one specific embodiment, the energy purchase cost is calculated using the following formula: ; in, This represents the energy purchase cost at time t. This represents the power purchased by the power grid at time t. This represents the grid purchase price of electricity at time t.

[0059] In one specific embodiment, the expression for calculating the cost of gas turbine power generation is: ; This represents the cost of gas turbine power generation at time t. This indicates the price of gas turbine power generation.

[0060] In another specific embodiment, the carbon trading price is determined based on the actual trading price. The carbon emission coefficients for the gas turbine and the power grid are set according to actual demand; for example, the carbon emission coefficient for the gas turbine can be 0.5, and the carbon emission coefficient for the power grid can be 0.7. The carbon emission quota coefficient, the carbon emission factor per unit mileage for gasoline vehicles, and the carbon emission factor per unit mileage for electric vehicles are set according to actual conditions; for example, the carbon emission quota coefficient can be 0.5, and the carbon emission factor per unit mileage for gasoline vehicles can be... The carbon emission factor per unit of driving distance for electric vehicles can be 0.09. . It can be 0.45 yuan / kWh.

[0061] In another optional embodiment, the power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment, and the optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging needs of the target electric vehicle within a preset period.

[0062] In a specific embodiment, the statement that the charging power of the target electric vehicle at the current moment is not greater than the upper limit of the charging power at the current moment can be expressed as: ; in, This represents the charging power of the target electric vehicle at time t. This represents the upper limit of the charging power of the charging pile at time t. Indicates the preset response sensitivity. This represents the adjustment coefficient at time t.

[0063] It should be noted that when the adjustment coefficient is large, the charging power of electric vehicles decreases, and when the adjustment coefficient is small, the charging power of electric vehicles increases. That is, charging is reduced during high-carbon periods and increased during low-carbon periods.

[0064] In one specific embodiment, charging the target electric vehicle using the charging power corresponding to the target electric vehicle at the current moment to meet the charging demand of the target electric vehicle within a preset period can be expressed as: ; Where T represents the preset period. Indicates the time step. This represents the charging power of the target electric vehicle at time t. This indicates the charging demand of the target electric vehicle within a preset period.

[0065] In another optional embodiment, the constraint further includes: the charging power corresponding to the target electric vehicle at the current moment is not greater than the maximum charging power of the target electric vehicle.

[0066] In one specific embodiment, the fact that the charging power of the electric vehicle at the current moment is not greater than the maximum charging power of the electric vehicle can be expressed as: ,in, This indicates the maximum charging power of the target electric vehicle.

[0067] In another optional embodiment, the constraint further includes: the power purchased by the grid is not greater than the maximum power purchased by the grid.

[0068] In one specific embodiment, the power purchased by the grid not exceeding the maximum power purchased by the grid can be expressed as: ,in, This indicates the maximum power purchased by the power grid.

[0069] In a more specific embodiment, It can be 500kW.

[0070] In another alternative embodiment, the gas turbine power is within a preset gas turbine power range.

[0071] In a specific embodiment, the gas turbine power being within a preset gas turbine power range can be expressed as: ,in, This represents the minimum power output of the gas turbine. This indicates the maximum power output of the gas turbine.

[0072] In a more specific embodiment, It can be 20kW. It can be up to 150kW.

[0073] In another optional embodiment, the power regulation strategy further includes the energy storage charging power and energy storage discharging power at the current moment, and the constraint condition further includes a power balance constraint, the expression of which is: ; in, This represents the energy storage discharge power at time t. This represents the load power at time t. This represents the target electric vehicle charging power at time t. This represents the energy storage charging power at time t.

[0074] In another alternative embodiment, the constraints further include: The energy storage capacity is within the preset energy storage capacity range, and / or the energy storage charging power and energy storage discharging power at the current moment are mutually exclusive.

[0075] In one specific embodiment, the energy storage capacity being within a preset energy storage capacity range can be expressed as: ; in, This represents the energy storage capacity at time t. This represents the minimum energy storage capacity. This indicates the maximum energy storage capacity.

[0076] It should be noted that, ; in, This represents the energy storage capacity at time t+1. Indicates energy storage charging efficiency. This represents the energy storage charging power at time t. Indicates the energy storage discharge efficiency. This represents the energy storage discharge power at time t.

[0077] In another specific embodiment, the mutual exclusion of the energy storage charging power and energy storage discharging power at the current moment can be expressed as: ; in, This represents the energy storage charging power at time t. This represents the energy storage discharge power at time t.

[0078] In this embodiment, the adjustment coefficient for the current moment is determined based on the actual carbon emissions and the average baseline carbon emissions at the current moment, thereby determining the upper limit of the charging power for the target electric vehicle at the current moment. An optimization model is constructed by inputting the upper limit of the charging power for the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the objective of minimizing the total operating cost of the virtual power plant, resulting in a power regulation strategy. By guiding electric vehicle charging behavior through carbon emission intensity and utilizing energy storage systems as auxiliary regulation resources, the system achieves both renewable energy consumption and optimized carbon emission operation, reducing carbon trading costs while ensuring a balance between power supply and demand and meeting user charging needs.

[0079] Corresponding to the power regulation method provided in the embodiments of this application, the embodiments of this application also provide a power regulation device, such as... Figure 2 As shown, the power regulation device includes: The first acquisition module 201 is used to acquire the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle. The first determining module 202 is used to determine the average baseline carbon emissions based on the baseline carbon emissions corresponding to each moment within a preset period of the target electric vehicle. The second determining module 203 is used to determine the adjustment coefficient corresponding to the current time based on the actual carbon emissions and the average baseline carbon emissions at the current time. The second acquisition module 204 is used to acquire the upper limit value of the charging power of the charging pile at the current moment. The third determining module 205 is used to determine the upper limit of the charging power of the target electric vehicle at the current moment based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment and the upper limit of the charging power of the charging pile; The fourth determining module 206 is used to construct an optimization model. It inputs the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within the preset period.

[0080] Corresponding to the power regulation method provided in the embodiments of this application, the embodiments of this application also provide an electronic device for performing the power regulation method, such as... Figure 3 As shown, the electronic device includes: a processor 301; and a memory 302 for storing a program for a power regulation method. After the device is powered on and the program for the power regulation method is run by the processor, the following steps are performed: Obtain the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle; The average baseline carbon emissions are determined based on the baseline carbon emissions at each time point within a preset period for the target electric vehicle. The adjustment coefficient for the current moment is determined based on the actual carbon emissions and the average baseline carbon emissions at the current moment. Get the current charging power limit of the charging pile; The upper limit of the charging power of the target electric vehicle at the current moment is determined based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile. An optimization model is constructed by inputting the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within the preset period.

[0081] Corresponding to the power regulation method provided in the embodiments of this application, the embodiments of this application also provide a computer-readable storage medium storing a program for the power regulation method, which is executed by a processor to perform the following steps: Obtain the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment within a preset cycle; The average baseline carbon emissions are determined based on the baseline carbon emissions at each time point within a preset period for the target electric vehicle. The adjustment coefficient for the current moment is determined based on the actual carbon emissions and the average baseline carbon emissions at the current moment. Get the current charging power limit of the charging pile; The upper limit of the charging power of the target electric vehicle at the current moment is determined based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile. An optimization model is constructed by inputting the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within the preset period.

[0082] Corresponding to the power regulation method provided in the embodiments of this application, the embodiments of this application also provide a computer program containing instructions, which, when executed by a computer, cause the computer to perform the following steps: Obtain the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment within a preset cycle; The average baseline carbon emissions are determined based on the baseline carbon emissions at each time point within a preset period for the target electric vehicle. The adjustment coefficient for the current moment is determined based on the actual carbon emissions and the average baseline carbon emissions at the current moment. Get the current charging power limit of the charging pile; The upper limit of the charging power of the target electric vehicle at the current moment is determined based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment, and the upper limit of the charging power of the charging pile. An optimization model is constructed by inputting the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within the preset period.

[0083] It should be noted that for a detailed description of the power regulation device, electronic device, computer-readable storage medium and computer program product provided in the embodiments of this application, please refer to the relevant description of the power regulation method embodiments provided in the embodiments of this application, which will not be repeated here.

[0084] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0085] In a typical configuration, an electronic device includes one or more processors (Central Processing Units), input / output interfaces, network interfaces, and memory.

[0086] Memory may include non-persistent storage in computer-readable media, such as random access memory and / or non-volatile memory, like read-only memory or flash memory. Memory is an example of computer-readable media.

[0087] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable operations, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, compact disc read-only memory, digital video disc or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media, such as modulated data signals and carrier waves.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, compact disc read-only memory, optical storage, etc.) containing computer-usable program code.

[0089] Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

Claims

1. A power regulation method, characterized in that, include: Obtain the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle; The average baseline carbon emissions are determined based on the baseline carbon emissions at each moment within the preset period of the target electric vehicle. The adjustment coefficient corresponding to the current time is determined based on the actual carbon emissions at the current time and the average baseline carbon emissions. Get the current charging power limit of the charging pile; The upper limit of the charging power of the target electric vehicle at the current moment is determined based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment and the upper limit of the charging power of the charging pile. An optimization model is constructed by inputting the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within a preset period. The total operating cost includes the sum of energy purchase cost, carbon trading cost, and gas turbine power generation cost within a preset period, wherein the energy purchase cost is the product of the grid power purchase capacity and the grid power purchase price; The formula for calculating the carbon trading cost is as follows: ； in, This represents the carbon trading cost at time t. Indicates the price in the carbon trading market. This represents the actual carbon emissions at time t. The carbon emission allowance corresponding to time t; The formula for calculating the actual carbon emissions is: ； in, This represents the carbon emission coefficient of the gas turbine at time t. This represents the gas turbine power at time t. This represents the carbon emission coefficient of the power grid at time t. This represents the grid power at time t; The formula for calculating the carbon emission allowance is as follows: ； in, This represents the carbon emission allowance coefficient at time t. This represents the gas turbine power at time t. This represents the wind power output at time t. This represents the photovoltaic power at time t. This indicates the mileage traveled by the target electric vehicle within a preset period. This indicates the carbon emission factor per unit mileage of a gasoline-powered vehicle. This represents the carbon emission factor per unit mileage of an electric vehicle. The expression for calculating the power generation cost of the gas turbine is as follows: ； This represents the cost of gas turbine power generation at time t. This indicates the price of gas turbine power generation.

2. The power regulation method according to claim 1, characterized in that, The constraint also includes: the charging power of the target electric vehicle at the current moment is not greater than the maximum charging power of the target electric vehicle.

3. The power regulation method according to claim 1, characterized in that, The power regulation strategy also includes the energy storage charging power and energy storage discharging power at the current moment, and the constraint conditions also include power balance constraints, the expression of which is: ； in, This represents the energy storage discharge power at time t. This represents the load power at time t. This represents the charging power of the target electric vehicle at time t. This represents the energy storage charging power at time t.

4. The power regulation method according to claim 1, characterized in that, The constraints also include: the power purchased by the power grid is not greater than the maximum power purchased by the power grid, and the power of the gas turbine is within the preset range of gas turbine power.

5. The power regulation method according to claim 1, characterized in that, The constraints also include: The energy storage capacity is within the preset energy storage capacity range, and / or the energy storage charging power and energy storage discharging power at the current moment are mutually exclusive.

6. The power regulation method according to claim 3, characterized in that, The acquisition of the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment within a preset period includes: Obtain the baseline charging power of the target electric vehicle at each moment within a preset cycle in the virtual power plant; The gas turbine power and grid power corresponding to the benchmark charging power are determined based on the benchmark charging power and the power balance constraint. The baseline carbon emissions corresponding to the baseline charging power are determined based on the gas turbine power and grid power corresponding to the baseline charging power.

7. The power regulation method according to claim 6, characterized in that, After obtaining the reference charging power corresponding to each moment within a preset period of the target electric vehicle in the virtual power plant, the method further includes: Obtain the probability density distribution of the battery capacity of the target electric vehicle, the probability density distribution of the driving mileage over a preset period, and the probability distribution of the starting charging time. The driving mileage of the target electric vehicle within the preset period and the driving mileage of the target electric vehicle per unit of electricity are determined based on the probability density distribution of the battery capacity of the target electric vehicle and the probability density distribution of the driving mileage of the preset period. Based on the mileage of the target electric vehicle within a preset period and the mileage of the target electric vehicle per unit of electricity, the charging requirements of the target electric vehicle within the preset period are determined. The charging duration of the target electric vehicle within the preset period is determined based on the charging demand, charging power, and charging efficiency of the charging pile within the preset period. Based on the probability distribution of the charging duration and starting charging time of the target electric vehicle within a preset period, a charging load curve of the target electric vehicle in the virtual power plant is generated. The charging load curve is used to indicate the reference charging power of the target electric vehicle at each moment. The charging load curves of the target electric vehicles in the virtual power plant are summed to obtain the total charging load curve, which is used to indicate the reference charging power of all target electric vehicles in the virtual power plant at each time.

8. A power regulation device for implementing the power regulation method of claim 1, characterized in that, include: The first acquisition module is used to acquire the baseline carbon emissions of the target electric vehicle within the virtual power plant at each moment in a preset cycle. The first determining module is used to determine the average baseline carbon emissions based on the baseline carbon emissions corresponding to each moment within a preset period of the target electric vehicle. The second determining module is used to determine the adjustment coefficient corresponding to the current time based on the actual carbon emissions at the current time and the average benchmark carbon emissions at the current time. The second acquisition module is used to acquire the upper limit value of the charging power of the charging pile at the current moment. The third determining module is used to determine the upper limit of the charging power of the target electric vehicle at the current moment based on the preset response sensitivity, the adjustment coefficient corresponding to the current moment and the upper limit of the charging power of the charging pile; The fourth determination module is used to construct an optimization model. It inputs the upper limit of the charging power corresponding to the target electric vehicle at the current moment and the charging demand of the target electric vehicle within a preset period into the optimization model. The optimization model is solved with the goal of minimizing the total operating cost of the virtual power plant to obtain a power regulation strategy. The power regulation strategy includes at least the charging power corresponding to the target electric vehicle at the current moment. The optimization model includes at least the following constraints: the charging power corresponding to the target electric vehicle at the current moment is not greater than the upper limit of the charging power corresponding to the current moment, and the charging power corresponding to the target electric vehicle at the current moment is used to charge the target electric vehicle to meet the charging demand of the target electric vehicle within a preset period.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the power regulation method according to any one of claims 1-7.

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