Scheduling method and scheduling device for electric vehicle frequency regulation based on user uncertainty

Abstract

This application provides a scheduling method and device for electric vehicle frequency regulation based on user uncertainty. The method includes: constructing a capacity prediction model to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertainty factors; constructing a cost estimation model based on the capacity prediction model to simulate the changing trend of the total grid frequency regulation cost with the frequency regulation load of electric vehicles; constructing a set of constraints corresponding to each model; under the constraints of the set of constraints, solving the capacity prediction model using a particle swarm optimization algorithm based on stochastic simulation with the minimum output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicles and generate a target frequency regulation scheduling strategy; controlling the operation of electric vehicles based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This method solves the problem in the prior art where frequency regulation strategies do not consider the uncertainty of user behavior, leading to deviations in frequency regulation scheduling control.

Description

Technical Field

[0001] This invention relates to the field of power system frequency regulation technology, and more specifically, to a scheduling method, scheduling device, computer-readable storage medium, processor, and power frequency regulation system for electric vehicle frequency regulation based on user uncertainty. Background Technology

[0002] With the large-scale integration of renewable energy and the continuous growth of peak load in my country, the randomness and instability of the power system are increasing, posing greater challenges to system frequency regulation. To maintain real-time supply and demand balance in the power grid, the demand for system spinning reserves is constantly increasing, leading to a rise in the frequency regulation costs of traditional generator units. The power grid urgently needs to develop new frequency regulation methods to overcome the shortcomings of traditional methods, such as slow response speed and low efficiency.

[0003] In recent years, the technology of coordinated dispatching on both the source and load sides has been continuously developing, and the application of dynamic demand response technology in power system frequency regulation has received increasing attention from scholars. Dynamically controllable load control strategies are flexible and can quickly adjust load levels without affecting or with minimal impact on user usage. In a smart grid environment, dynamically controllable loads can serve as stable resources on the demand side to maintain grid power balance and provide frequency regulation auxiliary services, thereby promoting the consumption of new energy sources and improving grid flexibility. Residential load resources, represented by electric vehicles, are high-quality resources for participating in dynamic demand response and have enormous frequency regulation potential. Uncertainties related to user charging behavior pose certain challenges to the participation of electric vehicle loads in secondary frequency regulation. The timing of electric vehicle grid connection and state of charge are affected by user behavior and are therefore uncertain, making the frequency regulation capacity they can provide uncertain, affecting the accuracy of frequency regulation optimization and dispatch. Based on this, this invention, taking electric vehicle loads as an example, proposes a frequency regulation strategy for electric vehicles participating in the grid considering the uncertainty of user behavior. This strategy can characterize the uncertainty of the available frequency regulation capacity of electric vehicle clusters caused by the uncertainty of user behavior, and achieve electric vehicle participation in grid frequency regulation at a certain confidence level. Summary of the Invention

[0004] The main objective of this application is to provide a scheduling method, scheduling device, computer-readable storage medium, processor, and power frequency regulation system for electric vehicle frequency regulation based on user uncertainty, so as to at least solve the problem that the frequency regulation strategy in the prior art does not consider the uncertainty of user behavior, which leads to deviations in frequency regulation scheduling control.

[0005] To achieve the above objectives, according to one aspect of this application, a scheduling method for electric vehicle frequency regulation based on user uncertainty is provided, comprising: constructing a capacity prediction model, the capacity prediction model being used to simulate the correlation between the frequency regulation capacity of the electric vehicle and uncertainty factors, the uncertainty factors including at least charging start time, charging end time, and initial state of charge; constructing a cost estimation model based on the capacity prediction model, the cost estimation model being used to simulate the changing trend of the total grid frequency regulation cost with the frequency regulation load of the electric vehicle while meeting the travel demand of the electric vehicle; and constructing a first set of constraints and a second set of constraints, the first set of constraints being used to restrict the operating parameters of the electric vehicle, and the second set of constraints... The first set of constraints is used to limit the operating parameters of each node in the power frequency regulation system. The second set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints. The first set of constraints includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first and second sets of constraints, the capacity prediction model is solved using a particle swarm optimization algorithm based on stochastic simulation, with the minimum output of the cost prediction model as the objective function. The operating parameters of the electric vehicle are obtained, and the target frequency regulation scheduling strategy is generated based on the operating parameters. The operation of the electric vehicle is controlled based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0006] Optionally, a capacity prediction model is constructed, including: constructing a first objective formula. The first target formula is used to simulate the trend of the state of charge (SOC) of the electric vehicle as a function of charging power, wherein SOC i,j,t+1 and SOC i,j,t Let $\frac{t+1}{t}$ represent the state of charge of the j-th electric vehicle of aggregator $i$ at times $t+1$ and $t$, respectively, where $Δt$ is the charging duration and $η$ is the state of charge. ch and η dis These represent charge and discharge efficiencies, and E represents the charging and discharging power of the j-th electric vehicle of the aggregator i at time t. i,j Let the capacity of the j-th electric vehicle of aggregator i be represented; construct the second objective formula. The second target formula is used to simulate the trend of the charging power of the electric vehicle as a function of the electric vehicle's state of charge (SOC), where SOC... t Given the state of charge; construct the third objective formula. The third objective formula is used to simulate the trend of the aggregator's aggregator electric vehicle load changing with the electric vehicle's state of charge, wherein, Let i be the aggregated electric vehicle load at time t. For the set of states of charge of electric vehicles of the aggregator i; construct the fourth objective formula. and the fifth objective formula The fourth objective formula is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, and the fifth objective formula is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, wherein... and These refer to the up-frequency modulation capacity and the down-frequency modulation capacity of the electric vehicle, respectively. and These represent the maximum values ​​of the charging and discharging power, respectively; by combining the first objective formula, the second objective formula, the third objective formula, the fourth objective formula, and the fifth objective formula, the capacity prediction model is obtained.

[0007] Optionally, before constructing the first objective formula, the method further includes: constructing a sixth objective formula. The sixth objective formula is the probability density function of the charging start time, where μ t σ is the expected charging start time. t The variance of the charging start time, t arrive The charging start time is defined; a seventh objective formula is constructed. The seventh objective formula is the probability density function of the daily mileage of the electric vehicle, where μ b σ is the expected daily mileage. b Let s be the variance of the daily mileage, and s be the daily mileage; construct the eighth objective formula. The eighth objective formula is used to simulate the change trend of the initial state of charge with the daily mileage, where SOC0 is the initial state of charge, and SOC... m s represents the maximum available battery power of the electric vehicle. max The maximum driving range of the electric vehicle when fully charged; construct the ninth objective formula. The ninth objective formula is the probability density function of the charging end time, where, σ is the expected charging end time. l E is the variance of the charging end time. battery t represents the battery capacity of the electric vehicle. leave The charging end time is specified.

[0008] Optionally, constructing a cost estimation model based on the capacity prediction model includes: constructing a tenth objective formula. The tenth objective formula is used to simulate the changing trend of active power at nodes in the power frequency regulation system with aggregated temperature-controlled load and aggregated electric vehicle load, where E N pf is the set of nodes in the power grid. ih,t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. The frequency modulation power change command for node i at time t; Let the remaining capacity at node i at time t be the result of frequency modulation failure; construct the eleventh objective formula. Twelfth Target Formula And the thirteenth objective formula The eleventh objective formula is used to simulate the trend of power generation cost as a function of active power; the twelfth objective formula is used to simulate the trend of carbon emission cost as a function of active power; and the thirteenth objective formula is used to simulate the trend of frequency regulation failure cost as a function of the remaining capacity after frequency regulation failure, wherein C gen For the aforementioned power generation cost, C carbon For the carbon emission cost, C fail For the frequency modulation failure cost, a i and b i c is a preset coefficient for the power generation cost. tax The tax amount per unit of carbon emissions, c fail The cost of frequency modulation failure per unit flux; construct the fourteenth objective formula C. total =C gen +C carbon +C fail The fourteenth objective formula is used to simulate the changing trend of the total grid frequency regulation cost with respect to the generation cost, the carbon emission cost, and the frequency regulation failure cost, where C total The total cost of frequency regulation in the power grid is given by the formulas for the tenth, eleventh, twelfth, thirteenth, and fourteenth objectives. These formulas are then combined to obtain the cost estimation model.

[0009] Optionally, constructing a first set of constraints includes: constructing the state of charge constraints corresponding to each electric vehicle to obtain a first target constraint. in, and The SOC represents the maximum and minimum state of charge (SOC) of the j-th electric vehicle of the aggregator i. need To meet the minimum state of charge required for user travel needs, The state of charge is SOC. tThe charging power at the time; construct the charging and discharging power constraints for each electric vehicle, and obtain the second target constraint. Where, η ch and η dis The charging and discharging efficiencies are respectively determined; the charging and discharging state constraints corresponding to each electric vehicle are constructed to obtain the third target constraint. in, and The charging and discharging state of the electric vehicle's charging load is a binary variable; the first target constraint condition, the second target constraint condition, and the third target constraint condition are combined to obtain the first constraint condition group.

[0010] Optionally, a second set of constraints is constructed, including: constructing the reactive power balance constraints corresponding to the power frequency regulation system to obtain the fourth target constraint. Among them, qf ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. To represent the reactive power base load at node i at time t; construct the generator generation constraints corresponding to the power frequency regulation system to obtain the fifth objective constraint. in, and These are the maximum and minimum active power of the generator at node i, respectively; and These are the maximum and minimum reactive power of the generator at node i, respectively; Represents the maximum ramp rate of the generator at node i; constructs the power flow calculation constraints corresponding to the power frequency regulation system, and obtains the sixth objective constraint. Among them, V i,t Let r be the square of the voltage magnitude at node i at time t. ih Represents the resistance of ih, x ih V represents the reactance of line ih. i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. The maximum apparent power of line ih is represented; the second set of constraints is constructed based on the fourth, fifth and sixth objective constraints.

[0011] Optionally, before solving the capacity prediction model using the particle swarm optimization algorithm based on stochastic simulation with the minimum value of the output of the cost prediction model as the objective function, the method further includes: constructing an alternative objective function minC. total Based on the alternative objective function, constraints on the uncertainties are introduced to obtain the objective function: in, For the objective function C total The minimum value taken when the confidence level is greater than or equal to β, where Pr{} represents the probability of the event in parentheses being true, β, α i These represent the confidence levels of the constraints and the objective function, respectively. and These represent the changes in charging and discharging power of the electric vehicle cluster at node i at time t, respectively, during frequency modulation. and These represent the up-frequency modulation capacity and the down-frequency modulation capacity that the electric vehicle cluster at node i at time t can actually provide, respectively.

[0012] According to another aspect of this application, a scheduling device for electric vehicle frequency regulation based on user uncertainty is provided. The device includes: a first construction unit for constructing a capacity prediction model, the capacity prediction model simulating the correlation between the frequency regulation capacity of the electric vehicle and uncertainty factors, the uncertainty factors including at least charging start time, charging end time, and initial state of charge; a second construction unit for constructing a cost estimation model based on the capacity prediction model, the cost estimation model simulating the trend of the total grid frequency regulation cost changing with the frequency regulation load of the electric vehicle while meeting the travel demand of the electric vehicle; and a third construction unit for constructing a first set of constraints and a second set of constraints, the first set of constraints restricting the operating parameters of the electric vehicle, the second set of constraints... The constraint set is used to restrict the operating parameters of each node in the power frequency regulation system. The first constraint set includes at least state-of-charge constraints, charging and discharging power constraints, and charging and discharging state constraints. The second constraint set includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. The calculation unit is used to solve the capacity prediction model based on the particle swarm optimization algorithm of stochastic simulation, with the minimum output of the cost prediction model as the objective function, under the constraints of the first and second constraint sets, to obtain the operating parameters of the electric vehicle and generate a target frequency regulation scheduling strategy based on the operating parameters. The control unit is used to control the operation of the electric vehicle based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, a power frequency regulation system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including methods for performing any one of the methods described.

[0015] Applying the technical solution of this application, in the aforementioned scheduling method for electric vehicle frequency regulation based on user uncertainty, firstly, a capacity prediction model is constructed. This model simulates the correlation between the frequency regulation capacity of electric vehicles and uncertain factors, including at least the charging start time, charging end time, and initial state of charge. Then, a cost estimation model is constructed based on the capacity prediction model. This model simulates the changing trend of the total grid frequency regulation cost with the frequency regulation load of electric vehicles while meeting their travel needs. Finally, a first set of constraints and a second set of constraints are constructed. The first set of constraints restricts the operating parameters of the electric vehicles. The operating parameters of each node in the power frequency regulation system are determined. The second set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints. The second set of constraints also includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Then, under the constraints of the first and second sets of constraints, a particle swarm optimization algorithm based on stochastic simulation is used to solve the capacity prediction model with the minimum output of the cost prediction model as the objective function. This yields the operating parameters of the electric vehicle. A target frequency regulation scheduling strategy is then generated based on these operating parameters. Finally, the operation of the electric vehicle is controlled based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This application constructs a capacity prediction model based on the impact of uncertainties caused by user behavior on the participation of electric vehicle loads in secondary frequency regulation. On this basis, a frequency regulation cost model is constructed. Under relevant constraints, the objective function is to minimize the output value of the frequency regulation cost model, and the solution is obtained through a particle swarm optimization algorithm with random simulation. This ensures that the solution is obtained at a certain confidence level, thereby enabling electric vehicles to participate in power system frequency regulation while considering the interference of uncertain user behavior. This method solves the problem that frequency regulation strategies in the prior art do not consider the uncertainty of user behavior, which leads to deviations in frequency regulation scheduling and control. Attached Figure Description

[0016] Figure 1A hardware structure block diagram of a mobile terminal for a scheduling method of electric vehicle frequency modulation based on user uncertainty provided in an embodiment of this application is shown.

[0017] Figure 2 A flowchart illustrating a scheduling method for electric vehicle frequency regulation based on user uncertainty, according to an embodiment of this application, is shown.

[0018] Figure 3 A structural block diagram of a scheduling device for electric vehicle frequency regulation based on user uncertainty, according to an embodiment of this application, is shown.

[0019] The above figures include the following reference numerals:

[0020] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] 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 of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., 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 data can be interchanged where appropriate for the embodiments of this application 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.

[0024] As described in the background section, the timing and state of charge of electric vehicles entering the grid in the prior art are uncertain, which makes the frequency regulation capacity they can provide uncertain, affecting the accuracy of frequency regulation optimization scheduling. In order to solve the problem that the frequency regulation strategy does not consider the uncertainty of user behavior, resulting in deviations in frequency regulation scheduling control, the embodiments of this application provide a scheduling method for electric vehicle frequency regulation based on user uncertainty, a scheduling device for electric vehicle frequency regulation based on user uncertainty, a computer-readable storage medium, a processor, and a power frequency regulation system.

[0025] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0026] The methods and embodiments provided in this application can be executed on a mobile terminal, a computer terminal, or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a scheduling method of electric vehicle frequency regulation based on user uncertainty, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0027] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the device information display method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0028] This embodiment provides a scheduling method for electric vehicle frequency regulation based on user uncertainty, which runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 2 This is a flowchart of a scheduling method for electric vehicle frequency regulation based on user uncertainty, according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Construct a capacity prediction model. The capacity prediction model is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, charging end time, and initial state of charge.

[0031] Specifically, in order to introduce the constraint of user behavior uncertainty on the participation of electric vehicles in frequency regulation, this application sets three uncertainty factors: charging start time, charging end time, and initial state of charge, and establishes them as random variables. Then, based on the correlation between random variables and the frequency regulation capacity of electric vehicles, the above-mentioned capacity prediction model is constructed.

[0032] Step S202: Construct a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the travel demand of electric vehicles.

[0033] Specifically, based on the simulation of the operating parameters of electric vehicles using the above-mentioned capacity prediction model, a cost estimation model is further established on the basis of the operating parameters. This application sets the above-mentioned cost estimation model to consider the power generation cost, carbon emission cost, and frequency regulation failure penalty cost of the power frequency regulation system.

[0034] Step S203: Construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0035] Specifically, corresponding constraint sets are constructed for each of the above models to restrict the input parameters of the models, resulting in the first constraint and the second constraint.

[0036] Step S204: Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters.

[0037] Specifically, under the constraints of the first and second sets of constraints, uncertainties are handled through random simulation, i.e. Monte Carlo simulation, and random variables are sampled from known probability distributions to provide a basis for the decision-making system. Then, based on the particle swarm optimization algorithm, the minimum value of the output of the cost prediction model is used as the objective function to solve the capacity prediction model, thereby obtaining the operating parameters of the electric vehicle. Based on the operating parameters, the target frequency regulation scheduling strategy is generated.

[0038] Step S205: Control the operation of the electric vehicle based on the above-mentioned target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0039] Specifically, the operation of the electric vehicles is controlled based on the aforementioned target frequency modulation scheduling strategy.

[0040] In this embodiment, firstly, a capacity prediction model is constructed to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertainties, including at least the charging start time, charging end time, and initial state of charge. Then, a cost estimation model is constructed based on the capacity prediction model to simulate the changing trend of the total grid frequency regulation cost with the frequency regulation load of electric vehicles, while meeting the travel demand of the electric vehicles. Finally, a first set of constraints and a second set of constraints are constructed. The first set of constraints restricts the operating parameters of the electric vehicles and the operating parameters of each node in the power frequency regulation system. The aforementioned second set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints. It also includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Subsequently, under the constraints of the aforementioned first and second sets of constraints, a particle swarm optimization algorithm based on stochastic simulation is used to solve the aforementioned capacity prediction model with the minimum output of the aforementioned cost prediction model as the objective function. This yields the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on these parameters. Finally, the operation of the electric vehicle is controlled based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This application constructs a capacity prediction model based on the impact of uncertainties arising from user behavior on the participation of electric vehicle loads in secondary frequency regulation. On this basis, a frequency regulation cost model is constructed. Under relevant constraints, the goal is to minimize the output value of the frequency regulation cost model, which is solved using a particle swarm optimization algorithm based on stochastic simulation. This ensures that the solution is obtained at a confidence level, enabling electric vehicles to participate in power system frequency regulation while considering the interference of uncertain user behavior. This method solves the problem in existing technologies where frequency regulation strategies do not consider the uncertainty of user behavior, leading to deviations in frequency regulation scheduling control.

[0041] In an optional implementation, to construct a capacity prediction model, step S201 includes:

[0042] Step S2011, construct the first target formula:

[0043]

[0044] The first objective formula described above is used to simulate the trend of the state of charge (SOC) of the electric vehicle as a function of charging power, where SOC... i,j,t+1 and SOC i,j,t Let $\frac{t+1}{t}$ represent the state of charge of the j-th electric vehicle of aggregator $i$ at times $t+1$ and $t$, respectively, where $Δt$ is the charging duration and $η$ is the state of charge. ch and η dis These represent charge and discharge efficiencies, and E represents the charging and discharging power of the j-th electric vehicle of the aforementioned aggregator i at time t. i,j This represents the capacity of the j-th electric vehicle of aggregator i;

[0045] Specifically, electric vehicles can charge from the grid or discharge to the grid using a bidirectional charging model. This application restricts the operating state of electric vehicles to three states: charging state, discharging state, and idle state. An electric vehicle can only be in one state at a time, but transitions between different states are possible.

[0046] Furthermore, based on the relationship between the state of charge and the charging and discharging power of the electric vehicle, a corresponding simulation formula is constructed, resulting in the aforementioned first objective formula.

[0047] Step S2012, construct the second objective formula The second objective formula described above is used to simulate the trend of the charging power of the electric vehicle as a function of its state of charge (SOC), where SOC... t The above-mentioned state of charge;

[0048] Specifically, based on the relationship between the charging and discharging power of electric vehicles and the state of charge, a corresponding formula is constructed to obtain the above-mentioned second objective formula.

[0049] Step S2013, construct the third objective formula The third objective formula described above is used to simulate the trend of the aggregator's aggregator electric vehicle load changing with the aforementioned state of charge of the electric vehicle, wherein, Let i be the aggregated electric vehicle load at time t. Let i be the set of states of charge of the electric vehicles of the aforementioned aggregator i;

[0050] Specifically, based on the charging and discharging power of each electric vehicle, aggregation is performed using the aggregator as a benchmark to obtain the aforementioned aggregated electric vehicle load.

[0051] Step S2014, construct the fourth objective formula and the fifth objective formula The fourth objective formula described above is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicles with the load of the aggregated electric vehicles, and the fifth objective formula described above is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicles with the load of the aggregated electric vehicles, wherein, and These refer to the aforementioned up-frequency modulation capacity and the aforementioned down-frequency modulation capacity of electric vehicles, respectively. and These represent the maximum charging and discharging power, respectively.

[0052] Specifically, based on the fourth and fifth objective formulas mentioned above, the up-frequency regulation capacity for electric vehicle load participation in frequency regulation can be simulated. and the aforementioned down-modulation capacity The correlation between the aggregated electric vehicle load and the aggregator.

[0053] Step S2015: Combine the above-mentioned first objective formula, the above-mentioned second objective formula, the above-mentioned third objective formula, the above-mentioned fourth objective formula and the above-mentioned fifth objective formula to obtain the above-mentioned capacity prediction model;

[0054] Specifically, the capacity prediction model is constructed based on the first objective formula, the second objective formula, the third objective formula, the fourth objective formula, and the fifth objective formula.

[0055] To introduce random variables, in one alternative implementation, the method further includes the following before constructing the first objective formula:

[0056] Step S301, construct the sixth objective formula:

[0057]

[0058] The sixth objective formula mentioned above is the probability density function of the charging start time, where μ t For the expected charging start time mentioned above, σ t Let t be the variance of the above charging start time. arrive The above refers to the charging start time;

[0059] Specifically, since the charging start time follows a normal distribution, the sixth objective formula mentioned above is constructed based on the probability density function to characterize the random variable of the charging start time.

[0060] Step S302, construct the seventh objective formula:

[0061]

[0062] The seventh objective formula mentioned above is the probability density function of the daily mileage of the electric vehicle, where μ b For the expected daily mileage mentioned above, σ b Let be the variance of the above daily mileage, and s be the above daily mileage;

[0063] Specifically, since the initial state of charge (SPC) is related to the daily mileage, and assuming that the SPC of an electric vehicle decreases linearly with mileage, the initial SPC can be predicted based on the daily mileage. Furthermore, since the daily mileage follows a log-normal distribution, the seventh objective formula mentioned above can be constructed based on the probability density function to characterize the random variable of daily mileage.

[0064] Step S303, construct the eighth objective formula:

[0065]

[0066] The eighth objective formula described above is used to simulate the trend of the initial state of charge changing with the daily mileage, where SOC0 is the initial state of charge, and SOC... m For the maximum available power of the aforementioned electric vehicles, s max This refers to the maximum driving range of the aforementioned electric vehicles when fully charged.

[0067] Specifically, based on the correlation between daily mileage and initial state of charge, the above-mentioned eighth objective formula is constructed to simulate the relationship between the initial state of charge and daily mileage.

[0068] Step S304, construct the ninth objective formula The ninth objective formula mentioned above is the probability density function of the charging end time, where, For the expected charging end time mentioned above, σ l E is the variance of the charging end time mentioned above. battery For the battery capacity of the aforementioned electric vehicle, t leave The charging end time is as described above;

[0069] Specifically, since the charging end time follows a normal distribution, the ninth objective formula mentioned above is constructed based on the probability density function, which is used to represent the random variable of the charging end time.

[0070] In order to construct a cost estimation model, in one optional implementation, step S202 above includes:

[0071] Step S2021, construct the tenth objective formula The tenth objective formula mentioned above is used to simulate the changing trend of active power at nodes in the aforementioned power frequency regulation system with the aggregated temperature-controlled load and aggregated electric vehicle load, where E N pf is the set of nodes in the power grid. ih,t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. The frequency modulation power change command for node i at time t; The remaining capacity at node i at time t is where the frequency modulation failed.

[0072] Specifically, based on the impact of electric vehicle regulation on the power generation of the power grid, the active power balance formula of each node of the power grid is constructed, and the tenth objective formula is obtained.

[0073] Step S2022, construct the eleventh objective formula: Twelfth Target Formula And the formula for the thirteenth objective: The eleventh objective formula above is used to simulate the trend of power generation cost changing with the aforementioned active power; the twelfth objective formula above is used to simulate the trend of carbon emission cost changing with the aforementioned active power; and the thirteenth objective formula above is used to simulate the trend of frequency regulation failure cost changing with the aforementioned remaining capacity after frequency regulation failure, where C gen For the aforementioned power generation costs, C carbon For the aforementioned carbon emission costs, C fail For the aforementioned frequency modulation failure cost, a i and b i c is the preset coefficient for the above power generation cost. tax The tax amount per unit of carbon emissions, c fail The cost of frequency modulation failure per unit flux;

[0074] Specifically, this application simplifies the cost of the power frequency regulation system into three parts: power generation cost, carbon emission cost, and frequency regulation failure penalty cost. Based on the correlation between the above costs and active power and fertility capacity, corresponding formulas are constructed to simulate the above correlation, resulting in the above eleventh objective formula, the twelfth objective formula, and the above thirteenth objective formula.

[0075] Step S2023, construct the fourteenth objective formula C total =C gen +C carbon +C fail The fourteenth objective formula mentioned above is used to simulate the changing trend of the total grid frequency regulation cost with respect to the generation cost, carbon emission cost, and frequency regulation failure cost, where C total The total cost of the aforementioned power grid frequency regulation;

[0076] Specifically, based on the correlation between the total cost of power frequency regulation and the aforementioned power generation cost, carbon emission cost, and frequency regulation failure cost, the above-mentioned fourteenth objective formula is constructed.

[0077] Step S2024: Combine the above tenth objective formula, the above eleventh objective formula, the above twelfth objective formula, the above thirteenth objective formula, and the above fourteenth objective formula to obtain the above cost estimation model;

[0078] Specifically, the cost estimation model is constructed based on the above tenth objective formula, the above eleventh objective formula, the above twelfth objective formula, the above thirteenth objective formula, and the above fourteenth objective formula.

[0079] In order to construct the first set of constraints, in one alternative implementation, step S203 includes:

[0080] Step S20311: Construct the above-mentioned state of charge constraints for each electric vehicle to obtain the first target constraint:

[0081]

[0082]

[0083] in, and Let SOC be the maximum and minimum state of charge (SOC) of the j-th electric vehicle of the above aggregator i. need To meet the minimum state of charge required for user travel needs, The above state of charge is SOC. t Charging power at that time;

[0084] Specifically, the aforementioned first objective constraint is used to limit the state of charge (SOC) of the electric vehicle's onboard battery to prevent overcharging or over-discharging. Furthermore, the aforementioned first objective constraint also limits the SOC at the end of charging to meet the minimum requirements for user travel.

[0085] Step S20312: Construct the above-mentioned charging and discharging power constraints for each electric vehicle to obtain the second target constraint:

[0086]

[0087] Where, η ch and η dis These represent the charging and discharging efficiencies, respectively.

[0088] Specifically, the aforementioned second objective constraint is used to limit the charging and discharging power of the electric vehicle to be less than its rated power.

[0089] Step S20313: Construct the above-mentioned charging and discharging state constraints for each electric vehicle to obtain the third target constraint:

[0090]

[0091] in, and The charging and discharging states of the charging load of the aforementioned electric vehicles are represented by two variables.

[0092] Specifically, and The charging and discharging state of the electric vehicle charging load is represented by a binary variable (1 for charging / discharging, 0 for not charging / discharging).

[0093] Step S20314: Combine the above-mentioned first objective constraint, the above-mentioned second objective constraint, and the above-mentioned third objective constraint to obtain the above-mentioned first constraint set.

[0094] Specifically, a first set of constraints is constructed based on the first objective constraint, the second objective constraint, and the third objective constraint.

[0095] In order to construct the second set of constraints, in one alternative implementation, step S203 includes:

[0096] Step S20321: Construct the reactive power balance constraints corresponding to the above power frequency regulation system to obtain the fourth objective constraint:

[0097]

[0098] Among them, qf ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. This represents the reactive base load at node i at time t.

[0099] Specifically, each node of the aforementioned power grid needs to satisfy both active power balance and reactive power balance. Based on this, the aforementioned fourth objective constraint is constructed based on the reactive power balance of each node of the power grid.

[0100] Step S20322: Construct the generator generation constraint conditions corresponding to the above power frequency regulation system to obtain the fifth objective constraint condition:

[0101]

[0102] in, and These are the maximum and minimum active power of the generator at node i, respectively; and These are the maximum and minimum reactive power of the generator at node i, respectively; This represents the maximum gradeability of the generator at node i;

[0103] Specifically, the fifth objective constraint condition is obtained by constraining the generator operating status of each node in the aforementioned power grid.

[0104] Step S20323: Construct the power flow calculation constraints corresponding to the above power frequency regulation system to obtain the sixth objective constraint:

[0105]

[0106] Among them, V i,tLet r be the square of the voltage magnitude at node i at time t. ih Represents the resistance of ih, x ih V represents the reactance of line ih. i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. This represents the maximum apparent power of line ih;

[0107] Specifically, the power flow equations of the power system are represented by a linearized Distflow model. Without considering network losses, the above formula is obtained. Furthermore, node voltage and power flow constraints are constructed based on the power flow equations to obtain the above sixth objective constraint.

[0108] Step S20324: Construct the second set of constraints based on the fourth, fifth and sixth objective constraints.

[0109] Specifically, the second set of constraints is constructed by combining the aforementioned fourth objective constraint, the aforementioned fifth objective constraint, and the aforementioned sixth objective constraint.

[0110] To obtain the objective function, before solving the capacity prediction model using the minimum value of the output of the aforementioned cost prediction model obtained by the particle swarm optimization algorithm based on stochastic simulation as the objective function, the above method further includes:

[0111] Step S401: Construct alternative objective functions minC total ;

[0112] Specifically, the objective function C of the optimization model that does not consider the uncertainty of user behavior total It consists of three parts, namely, the cost of electricity generation C. gen Carbon emission cost C carbon And the cost of FM failure penalty C fail The objective function is as follows:

[0113] C total =C gen +C carbon +C fail ;

[0114]

[0115] Among them, a i and b i A coefficient representing the cost of electricity generation; c tax Indicates carbon tax; c is the carbon emission factor of the generator at node i; fail This represents the unit cost of frequency modulation failure.

[0116] Step S402: Introduce a constraint condition for the above uncertainty factor according to the above alternative objective function, and obtain the above objective function: where is the minimum value of the objective function C total when the confidence level is greater than or equal to β, Pr{} represents the probability that the event in the parentheses holds, and β, α i are the confidence levels of the constraint condition and the objective function respectively and are the changes in the charging and discharging power of the electric vehicle cluster actually participating in the up and down frequency regulation at node i at time t respectively and are the above-mentioned up-regulation capacity and the above-mentioned down-regulation capacity that the electric vehicle cluster can actually provide at node i at time t respectively

[0117] Specifically, the chance-constrained programming (CCP) is applied to consider the uncertainty of the frequency regulation capacity available for electric vehicles. The above alternative objective function introduces a constraint condition for the above uncertainty factor, and the optimization model as above is obtained. Pr{} represents the probability that the event in the parentheses holds; β, α i are the confidence levels of the constraint condition and the objective function is the minimum value of the objective function C total when the confidence level is at least β represents the changes in the charging and discharging power of the electric vehicle cluster actually participating in the up and down frequency regulation at node i at time t

[0118] In the above embodiment, the above optimization model is solved. Let the uncertainty factor be a random variable ξ. Based on the stochastic simulation algorithm, the above optimization model is rewritten as follows:

[0119]

[0120] According to the probability distribution Φ(ξ) of the random variable ξ, generate N independent random vectors ξ i (i = 1, 2,..., N); set f i = f(x, ξ i ); take N' = βN. According to the law of large numbers, take the N'-th smallest element in the sequence {f1, f2,..., f N} as the objective function value

[0121] For the exchange settlement constraint satisfied by the random variable ξ, it is rewritten based on the stochastic simulation algorithm as follows:

[0122] Pr{g(x, ξ) ≤ 0} ≥ α

[0123] Set counter N' = 0; 1. Generate a random variable ξ based on the probability distribution Φ(ξ) of the random variable ξ; 2. If g(x,ξ)≤0 holds, then N' = N' + 1; Repeat steps 1 and 2 N times. If the chance constraint holds, then it is true; otherwise, it is false.

[0124] By using the aforementioned particle swarm optimization algorithm based on stochastic simulation to solve the chance-constrained stochastic optimization problem, we can obtain the response of electric vehicles to different frequency regulation commands at a certain confidence level, thereby optimizing the frequency regulation performance of the entire power system. The power system dispatch center sends frequency regulation commands to load aggregators through AGC (Automatic Generation Control). Specifically, these commands indicate the power that load aggregators need to increase or decrease at a specific time. Using our proposed optimization model, under the overall AGC frequency regulation commands, we can accurately determine the frequency regulation power that each load aggregator should undertake based on its current state and capacity. This method fully considers the uncertainty of the frequency regulation capacity that electric vehicle loads can provide, more closely reflects the real situation, and thus can more effectively allocate frequency regulation responsibilities.

[0125] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0126] This application also provides a scheduling device for electric vehicle frequency regulation based on user uncertainty. It should be noted that this scheduling device can be used to execute the scheduling method for electric vehicle frequency regulation based on user uncertainty provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0127] The following describes the electric vehicle frequency regulation scheduling device based on user uncertainty provided in the embodiments of this application.

[0128] Figure 3 This is a structural block diagram of a scheduling device for electric vehicle frequency regulation based on user uncertainty, according to an embodiment of this application. Figure 3 As shown, the device includes:

[0129] The first building unit 10 is used to build a capacity prediction model. The capacity prediction model is used to simulate the relationship between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, the charging end time, and the initial state of charge.

[0130] Specifically, in order to introduce the constraint of user behavior uncertainty on the participation of electric vehicles in frequency regulation, this application sets three uncertainty factors: charging start time, charging end time, and initial state of charge, and establishes them as random variables. Then, based on the correlation between random variables and the frequency regulation capacity of electric vehicles, the above-mentioned capacity prediction model is constructed.

[0131] The second building unit 20 is used to build a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the above travel demand of electric vehicles.

[0132] Specifically, based on the simulation of the operating parameters of electric vehicles using the above-mentioned capacity prediction model, a cost estimation model is further established on the basis of the operating parameters. This application sets the above-mentioned cost estimation model to consider the power generation cost, carbon emission cost, and frequency regulation failure penalty cost of the power frequency regulation system.

[0133] The third construction unit 30 is used to construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0134] Specifically, corresponding constraint sets are constructed for each of the above models to restrict the input parameters of the models, resulting in the first constraint and the second constraint.

[0135] The computing unit 40 is used to solve the capacity prediction model based on the particle swarm algorithm of random simulation with the minimum value of the output of the cost prediction model as the objective function under the constraints of the first set of constraints and the second set of constraints, to obtain the operating parameters of the electric vehicle, and to generate a target frequency regulation scheduling strategy based on the operating parameters.

[0136] Specifically, under the constraints of the first and second sets of constraints, uncertainties are handled through random simulation, i.e. Monte Carlo simulation, and random variables are sampled from known probability distributions to provide a basis for the decision-making system. Then, based on the particle swarm optimization algorithm, the minimum value of the output of the cost prediction model is used as the objective function to solve the capacity prediction model, thereby obtaining the operating parameters of the electric vehicle. Based on the operating parameters, the target frequency regulation scheduling strategy is generated.

[0137] Control unit 50 is used to control the operation of the electric vehicle based on the above-mentioned target frequency modulation scheduling strategy, so as to maximize the frequency modulation capacity benefit per unit cost.

[0138] Specifically, the operation of the electric vehicles is controlled based on the aforementioned target frequency modulation scheduling strategy.

[0139] Through the above embodiments, the first construction unit constructs a capacity prediction model, which is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertainties, including at least the charging start time, charging end time, and initial state of charge. The second construction unit constructs a cost estimation model based on the capacity prediction model, which is used to simulate the changing trend of the total grid frequency regulation cost with the frequency regulation load of electric vehicles while meeting the travel needs of the electric vehicles. The third construction unit constructs a first set of constraints and a second set of constraints, whereby the first set of constraints restricts the operating parameters of the electric vehicles, and the second set of constraints restricts the operation of each node in the power frequency regulation system. The first set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints. The second set of constraints includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first and second sets of constraints, the calculation unit solves the capacity prediction model using a particle swarm optimization algorithm based on stochastic simulation, with the minimum output of the cost prediction model as the objective function, to obtain the operating parameters of the electric vehicle. Based on these operating parameters, a target frequency regulation scheduling strategy is generated. The control unit controls the operation of the electric vehicle based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This application constructs a capacity prediction model based on the impact of user behavior uncertainties on the participation of electric vehicle loads in secondary frequency regulation. On this basis, a frequency regulation cost model is constructed. Under relevant constraints, the frequency regulation cost model output is minimized as the objective function, and the solution is obtained using a particle swarm optimization algorithm based on stochastic simulation, ensuring that the solution is obtained at a confidence level. This method enables electric vehicles to participate in power system frequency regulation while considering user uncertainty. This method solves the problem that frequency regulation strategies in the prior art do not consider the uncertainty of user behavior, leading to deviations in frequency regulation scheduling control.

[0140] The aforementioned electric vehicle frequency regulation scheduling device based on user uncertainty includes a processor and a memory. The first, second, and third building units, the computing unit, and the control unit are all stored as program units in the memory. The processor executes these program units stored in the memory to implement the corresponding functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0141] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and adjusting kernel parameters can improve the scheduling accuracy of electric vehicle frequency regulation.

[0142] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0143] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the aforementioned scheduling method for electric vehicle frequency regulation based on user uncertainty.

[0144] Specifically, the scheduling method for electric vehicle frequency regulation based on user uncertainty includes:

[0145] Step S201: Construct a capacity prediction model. The capacity prediction model is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, charging end time, and initial state of charge.

[0146] Step S202: Construct a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the travel demand of electric vehicles.

[0147] Step S203: Construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0148] Step S204: Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters.

[0149] Step S205: Control the operation of the electric vehicle based on the above-mentioned target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0150] This invention provides a processor for running a program, wherein the program executes the above-described scheduling method for electric vehicle frequency regulation based on user uncertainty.

[0151] Specifically, the scheduling method for electric vehicle frequency regulation based on user uncertainty includes:

[0152] Step S201: Construct a capacity prediction model. The capacity prediction model is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, charging end time, and initial state of charge.

[0153] Step S202: Construct a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the travel demand of electric vehicles.

[0154] Step S203: Construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0155] Step S204: Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters.

[0156] Step S205: Control the operation of the electric vehicle based on the above-mentioned target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0157] This invention provides a power frequency regulation system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0158] Step S201: Construct a capacity prediction model. The capacity prediction model is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, charging end time, and initial state of charge.

[0159] Step S202: Construct a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the travel demand of electric vehicles.

[0160] Step S203: Construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0161] Step S204: Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters.

[0162] Step S205: Control the operation of the electric vehicle based on the above-mentioned target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0163] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps:

[0164] Step S201: Construct a capacity prediction model. The capacity prediction model is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, charging end time, and initial state of charge.

[0165] Step S202: Construct a cost estimation model based on the above capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles under the condition of meeting the travel demand of electric vehicles.

[0166] Step S203: Construct a first set of limiting conditions and a second set of limiting conditions. The first set of limiting conditions is used to limit the operating parameters of the electric vehicle, and the second set of limiting conditions is used to limit the operating parameters of each node of the power frequency regulation system. The first set of limiting conditions includes at least a state of charge limiting condition, a charging and discharging power limiting condition, and a charging and discharging state limiting condition. The second set of limiting conditions includes at least a reactive power balance limiting condition, a generator power generation limiting condition, and a power flow calculation limiting condition.

[0167] Step S204: Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters.

[0168] Step S205: Control the operation of the electric vehicle based on the above-mentioned target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost.

[0169] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0170] 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 embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0174] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0175] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0176] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, 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 transient computer-readable media, such as modulated data signals and carrier waves.

[0177] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0178] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0179] 1) The electric vehicle frequency regulation scheduling method based on user uncertainty of this application firstly constructs a capacity prediction model to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertainty factors, including at least the charging start time, charging end time, and initial state of charge. Then, based on the capacity prediction model, a cost estimation model is constructed to simulate the changing trend of the total grid frequency regulation cost with the frequency regulation load of electric vehicles while meeting the travel demand of the electric vehicles. Finally, a first set of constraints and a second set of constraints are constructed. The first set of constraints restricts the operating parameters of the electric vehicles, and the second set of constraints restricts power frequency regulation. The operating parameters of each node in the system are determined by the following constraints: the first set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints; the second set of constraints includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Then, under the constraints of the first and second sets of constraints, a particle swarm optimization algorithm based on stochastic simulation is used to solve the capacity prediction model with the minimum output of the cost prediction model as the objective function, yielding the operating parameters of the electric vehicle. A target frequency regulation scheduling strategy is then generated based on these operating parameters. Finally, the operation of the electric vehicle is controlled based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This application constructs a capacity prediction model based on the impact of uncertainties caused by user behavior on the participation of electric vehicle loads in secondary frequency regulation. On this basis, a frequency regulation cost model is constructed. Under relevant constraints, the objective function is to minimize the output value of the frequency regulation cost model, and the solution is obtained through a particle swarm optimization algorithm with random simulation. This ensures that the solution is obtained at a certain confidence level, thereby enabling electric vehicles to participate in power system frequency regulation while considering the interference of uncertain user behavior. This method solves the problem that frequency regulation strategies in the prior art do not consider the uncertainty of user behavior, which leads to deviations in frequency regulation scheduling and control.

[0180] 2) The electric vehicle frequency regulation scheduling device based on user uncertainty of this application comprises: a first building unit constructing a capacity prediction model, which is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertainty factors, including at least the charging start time, charging end time, and initial state of charge; a second building unit constructing a cost estimation model based on the capacity prediction model, which is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles while meeting the travel demand of the electric vehicles; and a third building unit constructing a first set of limiting conditions and a second set of limiting conditions, where the first set of limiting conditions is used to limit the operating parameters of the electric vehicles, and the second set of limiting conditions is used to limit the power grid frequency regulation capacity of electric vehicles. The operating parameters of each node in the power frequency regulation system are determined by the following constraints: the first set of constraints includes at least state-of-charge constraints, charging / discharging power constraints, and charging / discharging state constraints; the second set of constraints includes at least reactive power balance constraints, generator generation constraints, and power flow calculation constraints. Under the constraints of the first and second sets of constraints, the computing unit solves the capacity prediction model using a particle swarm optimization algorithm based on stochastic simulation, with the minimum output of the cost prediction model as the objective function, to obtain the operating parameters of the electric vehicle. Based on these operating parameters, a target frequency regulation scheduling strategy is generated. The control unit controls the operation of the electric vehicle based on the target frequency regulation scheduling strategy to maximize the frequency regulation capacity benefit per unit cost. This application constructs a capacity prediction model based on the impact of uncertainties caused by user behavior on the participation of electric vehicle loads in secondary frequency regulation. On this basis, a frequency regulation cost model is constructed. Under relevant constraints, the objective function is to minimize the output value of the frequency regulation cost model, and the solution is obtained through a particle swarm optimization algorithm with random simulation. This ensures that the solution is obtained at a certain confidence level, thereby enabling electric vehicles to participate in power system frequency regulation while considering the interference of uncertain user behavior. This method solves the problem that frequency regulation strategies in the prior art do not consider the uncertainty of user behavior, which leads to deviations in frequency regulation scheduling and control.

[0181] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A scheduling method for electric vehicle frequency regulation based on user uncertainty, characterized in that, include: A capacity prediction model is constructed to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors, which include at least the charging start time, charging end time, and initial state of charge. A cost estimation model is constructed based on the capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost as the frequency regulation load of electric vehicles changes, under the condition of meeting the travel demand of electric vehicles. Construct a first set of constraints and a second set of constraints. The first set of constraints is used to restrict the operating parameters of the electric vehicle, and the second set of constraints is used to restrict the operating parameters of each node of the power frequency regulation system. The first set of constraints includes at least a state of charge constraint, a charge and discharge power constraint, and a charge and discharge state constraint. The second set of constraints includes at least a reactive power balance constraint, a generator power generation constraint, and a power flow calculation constraint. Under the constraints of the first set of constraints and the second set of constraints, the particle swarm optimization algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function to obtain the operating parameters of the electric vehicle, and a target frequency regulation scheduling strategy is generated based on the operating parameters. The electric vehicle is controlled based on the target frequency modulation scheduling strategy to maximize the frequency modulation capacity benefit per unit cost.

2. The method according to claim 1, characterized in that, Constructing a capacity prediction model includes: Constructing the first objective formula The first target formula is used to simulate the trend of the state of charge (SOC) of the electric vehicle as a function of charging power, wherein SOC i，j，t+1 and SOC i，j，t Let $\frac{t+1}{t}$ represent the state of charge of the j-th electric vehicle of aggregator $i$ at times $t+1$ and $t$, respectively, where $Δt$ is the charging duration and $η$ is the state of charge. ch and η dis These represent charge and discharge efficiencies, and E represents the charging and discharging power of the j-th electric vehicle of the aggregator i at time t. i，j This represents the capacity of the j-th electric vehicle of aggregator i; Constructing the second objective formula The second target formula is used to simulate the trend of the charging power of the electric vehicle as a function of the electric vehicle's state of charge (SOC), where SOC... t The state of charge; Constructing the third objective formula The third objective formula is used to simulate the trend of the aggregator's aggregator electric vehicle load changing with the electric vehicle's state of charge, wherein, Let i be the aggregated electric vehicle load at time t. Let i be the set of states of charge of the electric vehicles of the aggregator i; Constructing the fourth objective formula and the fifth objective formula The fourth objective formula is used to simulate the changing trend of the up-frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, and the fifth objective formula is used to simulate the changing trend of the down-frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, wherein... and These refer to the up-frequency modulation capacity and the down-frequency modulation capacity of the electric vehicle, respectively. and These represent the maximum charging and discharging power, respectively. The capacity prediction model is obtained by combining the first objective formula, the second objective formula, the third objective formula, the fourth objective formula, and the fifth objective formula.

3. The method according to claim 2, characterized in that, Before constructing the first objective formula, the method further includes: Constructing the sixth objective formula The sixth objective formula is the probability density function of the charging start time, where μ t σ is the expected charging start time. t The variance of the charging start time, t arrive The charging start time; Constructing the seventh objective formula The seventh objective formula is the probability density function of the daily mileage of the electric vehicle, where μ b σ is the expected daily mileage. b Let be the variance of the daily driving mileage, and s be the daily driving mileage; Constructing the eighth objective formula The eighth objective formula is used to simulate the change trend of the initial state of charge with the daily mileage, where SOC0 is the initial state of charge, and SOC... m s represents the maximum available battery power of the electric vehicle. max The maximum driving range of the electric vehicle when it is fully charged; Constructing the Ninth Objective Formula The ninth objective formula is the probability density function of the charging end time, where, σ is the expected charging end time. l Let t be the variance of the charging end time. leave The charging end time is specified.

4. The method according to claim 2, characterized in that, Based on the capacity prediction model, a cost estimation model is constructed, including: Constructing the tenth objective formula The tenth objective formula is used to simulate the changing trend of active power at nodes in the power frequency regulation system with aggregated temperature-controlled load and aggregated electric vehicle load, where E N pf is the set of nodes in the power grid. ki，t Let pf be the active power flowing from node k to node i at time t. ih，t Let be the active power flowing from node i to node h at time t. Let be the active power of the generator at node i at time t. Let be the active base load of node i at time t. The frequency modulation power change command for node i at time t; The remaining capacity at node i at time t is where the frequency modulation failed. Constructing the Eleventh Objective Formula Twelfth Target Formula And the thirteenth objective formula The eleventh objective formula is used to simulate the trend of power generation cost as a function of active power; the twelfth objective formula is used to simulate the trend of carbon emission cost as a function of active power; and the thirteenth objective formula is used to simulate the trend of frequency regulation failure cost as a function of the remaining capacity after frequency regulation failure, wherein C gen For the aforementioned power generation cost, C carbon For the carbon emission cost, C fail For the frequency modulation failure cost, a i and b i c is a preset coefficient for the power generation cost. tax The tax amount per unit of carbon emissions, c fail The cost of frequency modulation failure per unit flux. Let be the carbon emission factor of the generator at node i; Construct the fourteenth objective formula C total =C gen +C carbon +C fail The fourteenth objective formula is used to simulate the changing trend of the total grid frequency regulation cost with the generation cost, the carbon emission cost, and the frequency regulation failure cost, where C total The total cost of frequency regulation for the power grid; The cost estimation model is obtained by combining the tenth objective formula, the eleventh objective formula, the twelfth objective formula, the thirteenth objective formula, and the fourteenth objective formula.

5. The method according to claim 2, characterized in that, Construct the first set of constraints, including: The state of charge constraints for each electric vehicle are constructed to obtain the first target constraint. in, and Let SOC be the maximum and minimum state of charge (SOC) of the j-th electric vehicle of the aggregator i. need To meet the minimum state of charge required for user travel needs, The state of charge is SOC. t Charging power at that time, E battery The battery capacity of the electric vehicle; Construct the charging and discharging power constraints for each electric vehicle to obtain the second target constraint. Where, η ch and η dis These represent the charging and discharging efficiencies, respectively. The charging and discharging state constraints for each electric vehicle are constructed to obtain the third target constraint. in, and The charging and discharging states of the charging load of the electric vehicle are represented by a binary variable. The first set of constraints is obtained by combining the first objective constraint, the second objective constraint, and the third objective constraint.

6. The method according to claim 4, characterized in that, Construct a second set of constraints, including: Construct the reactive power balance constraints corresponding to the power frequency regulation system to obtain the fourth objective constraint. Among them, qf ki，t Let qf be the reactive power flowing from node k to node i at time t. ih,t Let be the reactive power flowing from node i to node h at time t. Let be the reactive power of the generator at node i at time t. This represents the reactive base load at node i at time t. Construct the generator generation constraints corresponding to the power frequency regulation system to obtain the fifth objective constraint. in, and These are the maximum and minimum active power of the generator at node i, respectively; and These are the maximum and minimum reactive power of the generator at node i, respectively; This represents the maximum gradeability of the generator at node i; Construct the power flow calculation constraints corresponding to the power frequency regulation system to obtain the sixth objective constraint. Among them, V i，t V is the square of the voltage magnitude at node i at time t. h，t r is the square of the voltage magnitude at node h at time t. ih x represents the resistance of line ih. ih V represents the reactance of line ih. i max and V i min This represents the maximum and minimum squared values ​​of the voltage magnitude at node i. This represents the maximum apparent power of line ih; The second set of constraints is constructed based on the fourth objective constraint, the fifth objective constraint, and the sixth objective constraint.

7. The method according to claim 6, characterized in that, Before the particle swarm optimization algorithm based on stochastic simulation is used to solve the capacity prediction model with the minimum value of the output of the cost prediction model as the objective function, the method further includes: Construct alternative objective functions min C total ; Based on the alternative objective function, constraints on the uncertainties are introduced to obtain the objective function: in, For the objective function C total The minimum value taken when the confidence level is greater than or equal to β, where Pr{} represents the probability of the event in parentheses being true, β, α i These represent the confidence levels of the constraints and the objective function, respectively. and These represent the changes in charging and discharging power of the electric vehicle cluster at node i at time t, respectively, during frequency modulation. and These represent the up-frequency modulation capacity and the down-frequency modulation capacity that the electric vehicle cluster at node i at time t can actually provide, respectively.

8. A scheduling device for electric vehicle frequency regulation based on user uncertainty, characterized in that, The device includes: The first building unit is used to build a capacity prediction model, which is used to simulate the correlation between the frequency regulation capacity of electric vehicles and uncertain factors. The uncertain factors include at least the charging start time, the charging end time, and the initial state of charge. The second building unit is used to build a cost estimation model based on the capacity prediction model. The cost estimation model is used to simulate the trend of the total grid frequency regulation cost changing with the frequency regulation load of electric vehicles while meeting the travel demand of electric vehicles. The third construction unit is used to construct a first set of constraints and a second set of constraints. The first set of constraints is used to restrict the operating parameters of the electric vehicle, and the second set of constraints is used to restrict the operating parameters of each node of the power frequency regulation system. The first set of constraints includes at least a state of charge constraint, a charging and discharging power constraint, and a charging and discharging state constraint. The second set of constraints includes at least a reactive power balance constraint, a generator power generation constraint, and a power flow calculation constraint. The computing unit is configured to, under the constraints of the first set of constraints and the second set of constraints, solve the capacity prediction model based on the particle swarm optimization algorithm of random simulation with the minimum value of the output of the cost prediction model as the objective function, to obtain the operating parameters of the electric vehicle, and generate a target frequency regulation scheduling strategy based on the operating parameters; The control unit is used to control the operation of the electric vehicle based on the target frequency modulation scheduling strategy, so as to maximize the frequency modulation capacity benefit per unit cost.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

10. A power frequency regulation system, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising methods for performing any one of claims 1 to 7.

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