Electric vehicle frequency modulation scheduling method and scheduling device based on user uncertainty

Abstract

The invention provides an electric vehicle frequency modulation scheduling method and scheduling device based on user uncertainty, and the method comprises the steps: constructing a capacity prediction model which is used for simulating the incidence relation between the frequency modulation capacity of an electric vehicle and an uncertainty factor; a cost prediction model is constructed according to the capacity prediction model and used for simulating the variation trend of the total power grid frequency modulation cost along with the frequency modulation load of the electric vehicle; constructing a limiting condition group corresponding to each model; under the limitation of the limitation condition group, solving the capacity prediction model by taking the minimum value obtained by the output of the cost prediction model as a target function based on a particle swarm algorithm of stochastic simulation, obtaining the operation parameters of the electric vehicle, and generating a target frequency modulation scheduling strategy; and controlling the operation of the electric vehicle based on the target frequency modulation scheduling strategy so as to maximize the frequency modulation capacity income of unit cost. According to the method, the problem that frequency modulation scheduling control deviates due to the fact that uncertainty of user behaviors is not considered in a frequency modulation strategy in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency modulation, and in particular to a dispatching method, a dispatching device, a computer-readable storage medium, a processor and a power frequency modulation system for electric vehicle frequency modulation based on user uncertainty. Background Art

[0002] With the large-scale access of renewable energy and the continuous growth of peak load in my country, the randomness and instability of the power system are increasing, bringing greater challenges to the frequency regulation of the system. In order to maintain the real-time supply and demand balance of the power grid, the demand for system rotating reserve is increasing, and the frequency regulation cost of traditional generator sets is rising accordingly. The power grid urgently needs to develop new frequency regulation methods to make up for the shortcomings of traditional frequency regulation methods such as slow response speed and low efficiency.

[0003] In recent years, the coordinated dispatching technology on both sides of the source and the load has been continuously developed, and the application of dynamic demand response technology in power system frequency regulation has attracted more and more attention from scholars. The control strategy of dynamic controllable load is flexible, and the load level can be quickly adjusted without affecting or less affecting the user's use. In the smart grid environment, the dynamically controllable load can be used as a stable resource on the demand side to maintain the power balance of the power grid and provide auxiliary frequency regulation services, thereby promoting the consumption of new energy and improving the flexibility of the power grid. Residential load resources represented by electric vehicles are high-quality resources for participating in dynamic demand response, and have great potential for frequency regulation. The uncertainty related to user charging behavior has brought certain challenges to the participation of electric vehicle loads in secondary frequency regulation. The time and charge state of electric vehicles are affected by user behavior and are uncertain, which makes the frequency regulation capacity that can be provided by participating in frequency regulation uncertain, affecting the accuracy of frequency regulation optimization scheduling. Based on this, the present invention takes electric vehicle loads as an example, and proposes a strategy for electric vehicles to participate in power grid frequency regulation that takes into account the uncertainty of user behavior. It can characterize the uncertainty of the frequency regulation capacity available for electric vehicle clusters caused by the uncertainty of user behavior, and realize the participation of electric vehicles in power grid frequency regulation at a certain confidence level. Summary of the invention

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

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a scheduling method for electric vehicle frequency regulation based on user uncertainty is provided, including: constructing a capacity prediction model, the capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, the uncertainty factors at least include the charging start time, the charging end time and the initial charge state; constructing a cost estimation model according to the capacity prediction model, the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the electric vehicle; constructing a first restriction condition group and a second restriction condition group, the first restriction condition group is used to limit the operating parameters of the electric vehicle, and the second restriction condition group Used to limit the operating parameters of each node of the power frequency regulation system, the second restriction condition group at least includes charge state restriction conditions, charge and discharge power restriction conditions and charge and discharge state restriction conditions, and the first restriction condition group at least includes reactive power balance restriction conditions, generator power generation restriction conditions and power flow calculation restriction conditions; under the restrictions of the first restriction condition group and the second restriction condition group, the particle swarm algorithm based on random simulation solves the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generates the target frequency regulation scheduling strategy according to the operating parameters; based on the target frequency regulation scheduling strategy, the operation of the electric vehicle is controlled to maximize the frequency regulation capacity benefit per unit cost.

[0006] Optionally, constructing a capacity prediction model includes: constructing a first target formula The first target formula is used to simulate the change trend of the state of charge of the electric vehicle with the charging power, wherein SOC i,j,t+1 and SOC i,j,t are the state of charge of the j-th electric vehicle of aggregator i at time t+1 and time t, Δt is the charging time, η ch and η dis are the charge and discharge efficiency, and are the charging and discharging power of the j-th electric vehicle of the aggregator i at time t, E i,j represents the capacity of the j-th electric vehicle of aggregator i; construct the second objective formula The second target formula is used to simulate the changing trend of the charging power of the electric vehicle with the state of charge of the electric vehicle, wherein SOC t For the state of charge; construct a third target formula The third objective formula is used to simulate the variation trend of the aggregated electric vehicle load of the aggregator with the state of charge of the electric vehicle, wherein: is the aggregated electric vehicle load of the aggregator i at time t, is the charge state set of the electric vehicles of the aggregator i; construct the fourth target formula And the fifth goal formula The fourth objective formula is used to simulate the change trend of the upward 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 change trend of the downward frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, wherein, and are the upper frequency regulation capacity and the lower frequency regulation capacity of the electric vehicle respectively, and are the maximum values ​​of the charge and discharge power respectively; the capacity prediction model is obtained by combining the first target formula, the second target formula, the third target formula, the fourth target formula and the fifth target formula.

[0007] Optionally, before constructing the first target formula, the method further includes: constructing a sixth target formula The sixth objective formula is the probability density function of the charging start time, where μ t is the expected charging start time, σ t is the variance of the charging start time, t arrive is the charging start time; construct the seventh target formula The seventh objective formula is the probability density function of the daily mileage of the electric vehicle, where μ b is the expected daily mileage, σ b is the variance of the daily mileage, s is the daily mileage; construct the eighth target formula The eighth target 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, SOC m is the maximum available power of the electric vehicle, s max is the maximum mileage that the electric vehicle can travel when fully charged; construct the ninth objective formula The ninth target formula is the probability density function of the charging end time, where: is the expected end time of charging, σ l is the variance of the charging end time, E battery is the battery capacity of the electric vehicle, t leave is the charging end time.

[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 change trend of the active power of the node in the power frequency modulation system with the aggregated temperature control load and the aggregated electric vehicle load, where E N is the set of nodes in the power grid; pf ih,t is the active power flowing from node i to node h at time t, is the active power of the generator at node i at time t, is the active base load of node i at time t, is the frequency modulation power change instruction of node i at time t; is the remaining capacity after the frequency regulation fails at node i at time t; construct the eleventh objective formula Twelfth Target Formula and the thirteenth goal formula The eleventh target formula is used to simulate the changing trend of power generation cost with the active power, the twelfth target formula is used to simulate the changing trend of carbon emission cost with the active power, and the thirteenth target formula is used to simulate the changing trend of frequency regulation failure cost with the remaining capacity of frequency regulation failure, wherein C gen is the power generation cost, C carbon is the carbon emission cost, C fail is the frequency modulation failure cost, a i and b i is the preset coefficient of the power generation cost, c tax represents the tax per unit of carbon emissions, c fail is the frequency regulation failure cost 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 cost of the power grid frequency regulation with the power generation cost, the carbon emission cost and the frequency regulation failure cost, wherein C total is the total cost of the grid frequency regulation; the tenth target formula, the eleventh target formula, the twelfth target formula, the thirteenth target formula and the fourteenth target formula are combined to obtain the cost estimation model.

[0009] Optionally, constructing the first constraint condition group includes: constructing the state of charge constraint conditions corresponding to each electric vehicle to obtain the first target constraint conditions in, and is the maximum and minimum state of charge of the j-th electric vehicle of the aggregator i, SOC need In order to meet the minimum state of charge required by users for travel, The state of charge is SOC tThe charging power at the time; construct the charging and discharging power restriction conditions corresponding to each electric vehicle, and obtain the second target restriction conditions Among them, η ch and η dis are the efficiency of charge and discharge respectively; construct the charge and discharge state constraint conditions corresponding to each electric vehicle, and obtain the third target constraint condition in, and is a binary variable of the charging and discharging state of the charging load of the electric vehicle; and 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, constructing the second constraint condition group includes: constructing the reactive power balance constraint condition corresponding to the power frequency regulation system to obtain the fourth target constraint condition Among them, qf ih,t is the reactive power flowing from node i to node h at time t, is the reactive power of the generator at node i at time t, is the reactive basic load of node i at time t; construct the generator power generation constraint condition corresponding to the power frequency regulation system, and obtain the fifth target constraint condition in, and are the maximum and minimum active powers of the generator at node i respectively; and 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; constructing the power flow calculation constraint condition corresponding to the power frequency regulation system, and obtaining the sixth target constraint condition Among them, V i,t is the square of the voltage amplitude of node i at time t, r ih represents the resistance of ih, x ih Represents the reactance of line ih, V i max and V i min represents the maximum and minimum values ​​of the square of the voltage amplitude at node i, represents the maximum apparent power of line ih; and constructs the second constraint condition group according to the fourth target constraint condition, the fifth target constraint condition and the sixth target constraint condition.

[0011] Optionally, before solving the capacity prediction model by using the particle swarm algorithm based on random simulation to obtain the minimum value of the output of the cost estimation model as the objective function, the method further includes: constructing an alternative objective function minC total ; According to the alternative objective function, the restriction condition of the uncertainty factor is introduced to obtain the objective function: in, The objective function C total The minimum value when the confidence level is greater than or equal to β. Pr{} represents the probability of the event in the brackets, β, α i are the confidence of the constraint condition and the objective function, respectively. and are the actual charging and discharging power changes of the electric vehicle cluster participating in the up and down frequency modulation at time t, and They are respectively the upward frequency regulation capacity and the downward frequency regulation capacity that can be actually provided by the electric vehicle cluster at the node i at time t.

[0012] According to another aspect of the present application, a scheduling device for electric vehicle frequency regulation based on user uncertainty is provided, the device comprising: a first construction unit, for constructing a capacity prediction model, the capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and uncertainty factors, the uncertainty factors at least including the charging start time, the charging end time and the initial state of charge; a second construction unit, for constructing a cost estimation model according to the capacity prediction model, the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the electric vehicle; a third construction unit, for constructing a first restriction condition group and a second restriction condition group, the first restriction condition group is used to limit the operating parameters of the electric vehicle, and the second restriction condition group is used to limit the operating parameters of the electric vehicle. The restriction condition group is used to limit the operating parameters of each node of the power frequency regulation system, the first restriction condition group at least includes charge state restriction condition, charge and discharge power restriction condition and charge and discharge state restriction condition, and the second restriction condition group at least includes reactive power balance restriction condition, generator power generation restriction condition and power flow calculation restriction condition; the calculation unit is used to solve the capacity prediction model based on the particle swarm algorithm of random simulation under the restriction of the first restriction condition group and the second restriction condition group, with the output of the cost estimation model taking the minimum value as the objective function, to obtain the operating parameters of the electric vehicle, and generate a target frequency regulation scheduling strategy according to 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 the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described.

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

[0015] Applying the technical solution of the present application, in the above-mentioned scheduling method for electric vehicle frequency regulation based on user uncertainty, first, a capacity prediction model is constructed, and the above-mentioned capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, and the above-mentioned uncertainty factors at least include the charging start time, the charging end time and the initial charge state; then, a cost estimation model is constructed according to the above-mentioned capacity prediction model, and the above-mentioned cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the above-mentioned electric vehicle; then, a first restriction condition group and a second restriction condition group are constructed, and the above-mentioned first restriction condition group is used to limit the operating parameters of the above-mentioned electric vehicle, and the above-mentioned first restriction condition group is used to limit The operating parameters of each node of the power frequency regulation system are controlled, the above-mentioned second constraint condition group at least includes charge state constraint condition, charge and discharge power constraint condition and charge and discharge state constraint condition, and the above-mentioned second constraint condition group at least includes reactive power balance constraint condition, generator power generation constraint condition and power flow calculation constraint condition; then, under the constraints of the above-mentioned first constraint condition group and the above-mentioned second constraint condition group, the particle swarm algorithm based on random simulation solves the above-mentioned capacity prediction model with the minimum value of the output of the above-mentioned cost estimation model as the objective function, and obtains the operating parameters of the above-mentioned electric vehicle, and generates a target frequency regulation scheduling strategy according to the above-mentioned operating parameters; finally, the operation of the above-mentioned electric vehicle is controlled based on the above-mentioned 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 uncertainty factors brought by user behavior on the participation of electric vehicle load in secondary frequency regulation, and on this basis constructs a frequency regulation cost model. Under relevant constraints, the output value of the frequency regulation cost model is minimized as the objective function through a particle swarm algorithm of random simulation to solve the problem. The solution is guaranteed to be solved at a confidence level, and electric vehicles are allowed to participate in power system frequency regulation under the interference of user uncertainty behavior. This method solves the problem that the frequency regulation strategy in the prior art does not consider the uncertainty of user behavior, resulting in deviations in frequency regulation dispatching control. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 2 A schematic flow chart of a method for scheduling frequency modulation of electric vehicles based on user uncertainty provided according to an embodiment of the present application is shown;

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

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

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0021] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] As introduced in the background technology, in the prior art, the time when electric vehicles connect to the grid and the state of charge are subject to uncertainty, which makes the frequency modulation capacity that can be provided by participating in the frequency modulation also uncertain, affecting the accuracy of the frequency modulation optimization scheduling. In order to solve the problem that the frequency modulation strategy does not consider the uncertainty of user behavior, resulting in deviations in the frequency modulation scheduling control, the embodiments of the present application provide a scheduling method for electric vehicle frequency modulation based on user uncertainty, a scheduling device for electric vehicle frequency modulation based on user uncertainty, a computer-readable storage medium, a processor and a power frequency modulation system.

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

[0026] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a method for scheduling electric vehicle frequency modulation based on user uncertainty according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as 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] In this embodiment, a scheduling method for electric vehicle frequency modulation based on user uncertainty is provided, which runs on a mobile terminal, a computer terminal or a similar computing device. It should be noted that the steps shown in the flowchart of 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 can be executed in an order different from that shown here.

[0029] Figure 2 FIG. 1 is a flow chart of a method for scheduling electric vehicle frequency modulation based on user uncertainty according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0030] Step S201, constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0031] Specifically, in order to introduce the constraints of user behavior uncertainty on the participation of electric vehicles in frequency modulation, the present application sets three uncertainty factors: charging start time, charging end time and initial charge state, and establishes them as random variables accordingly, and then constructs the above-mentioned capacity prediction model based on the correlation between random variables and the frequency modulation capacity of electric vehicles.

[0032] Step S202, constructing a cost estimation model based on the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0033] Specifically, based on the above-mentioned capacity prediction model, the simulation of the operating parameters of the electric vehicle is associated, and a cost estimation model is further established based on the operating parameters. The present application sets the above-mentioned cost estimation model to take into account the power generation cost, carbon emission cost and frequency regulation failure penalty cost of the power frequency regulation system.

[0034] Step S203, constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency modulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0035] Specifically, a corresponding restriction condition group is constructed for each of the above models to restrict the input parameters of the model, thereby obtaining the above first restriction condition and the above second restriction condition.

[0036] Step S204, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters;

[0037] Specifically, under the constraints of the first and second constraint groups, uncertain factors 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 algorithm, the capacity prediction model is solved with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and the target frequency modulation scheduling strategy is generated according to the operating parameters.

[0038] Step S205: 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.

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

[0040] Through this embodiment, first, a capacity prediction model is constructed, and the above-mentioned capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, and the above-mentioned uncertainty factors at least include the charging start time, the charging end time and the initial charge state; then, a cost estimation model is constructed according to the above-mentioned capacity prediction model, and the above-mentioned cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the above-mentioned electric vehicle; then, a first restriction condition group and a second restriction condition group are constructed, and the above-mentioned first restriction condition group is used to limit the operating parameters of the above-mentioned electric vehicle, and the above-mentioned first restriction condition group is used to limit the operating parameters of each node of the power frequency regulation system, The second constraint condition group at least includes state of charge constraint condition, charge and discharge power constraint condition and charge and discharge state constraint condition, and the second constraint condition group at least includes reactive balance constraint condition, generator power generation constraint condition and flow calculation constraint condition; then, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation takes the output of the cost estimation model to obtain the minimum value as the objective function to solve the capacity prediction model, obtain the operating parameters of the electric vehicle, and generate the target frequency regulation scheduling strategy according to the 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 influence of uncertainty factors brought by user behavior on the participation of electric vehicle load in secondary frequency regulation, and on this basis constructs a frequency regulation cost model, solves it under relevant constraints with the output value of the frequency regulation cost model minimized as the objective function through the particle swarm algorithm of random simulation, ensures that the solution is performed at the confidence level, and realizes the participation of electric vehicles in power system frequency regulation under the interference of user uncertainty behavior. This method solves the problem that the frequency regulation strategy in the prior art does not consider the uncertainty of user behavior, resulting in deviation in frequency regulation dispatching control.

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

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

[0043]

[0044] The first objective formula is used to simulate the change trend of the state of charge of the electric vehicle with the charging power, where SOC i,j,t+1 and SOC i,j,t are the state of charge of the j-th electric vehicle of aggregator i at time t+1 and time t, Δt is the charging time, η ch and η dis are the charge and discharge efficiency, and are the charging and discharging power of the j-th electric vehicle of the above-mentioned aggregator i at time t, E i,j represents the capacity of the j-th EV of aggregator i;

[0045] Specifically, electric vehicles can be charged from or discharged to the power grid through a bidirectional charging model. This application limits the operating state of electric vehicles to three states: charging state, discharging state, and idle state. Electric vehicles can only be in one state in a period of time, and conversions can occur between different states.

[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 to obtain the above-mentioned first target formula.

[0047] Step S2012, construct the second target formula The second objective formula is used to simulate the changing trend of the charging power of the electric vehicle with the state of charge of the electric vehicle, where SOC t is the above-mentioned state of charge;

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

[0049] Step S2013, construct the third target formula The third objective formula is used to simulate the variation trend of the aggregated electric vehicle load of the aggregator with the state of charge of the electric vehicle, wherein: is the aggregated electric vehicle load of the aggregator i at time t, is the state of charge set of electric vehicles of the above aggregator i;

[0050] Specifically, based on the charging and discharging power of each electric vehicle, aggregation is performed based on the aggregator to obtain the above-mentioned aggregated electric vehicle load.

[0051] Step S2014, construct the fourth target formula And the fifth goal formula The fourth objective formula is used to simulate the change trend of the upper 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 change trend of the lower frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, wherein: and are the above-mentioned upper frequency regulation capacity and the above-mentioned lower frequency regulation capacity of electric vehicles, respectively. and are the maximum values ​​of charge and discharge power respectively;

[0052] Specifically, based on the fourth target formula and the fifth target formula, the above-mentioned upper frequency regulation capacity of the electric vehicle load participating in the frequency regulation can be simulated. and the above lower frequency modulation capacity The association with the aggregated EV load of the aggregator.

[0053] Step S2015, combining the first target formula, the second target formula, the third target formula, the fourth target formula and the fifth target formula to obtain the capacity prediction model;

[0054] Specifically, the capacity prediction model is constructed according to the first target formula, the second target formula, the third target formula, the fourth target formula and the fifth target formula.

[0055] In order to introduce random variables, in an optional implementation, before constructing the first target formula, the method further includes:

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

[0057]

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

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

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

[0061]

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

[0063] Specifically, since the initial state of charge is related to the daily mileage, the state of charge of the electric vehicle is assumed to decrease linearly with the driving mileage, and the initial state of charge can be estimated based on the daily mileage. Furthermore, the daily mileage satisfies the log-normal distribution, and the seventh objective formula is constructed based on the probability density function to characterize the random variable of the daily mileage.

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

[0065]

[0066] The eighth target 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, SOC m is the maximum available power of the electric vehicle mentioned above, s max The maximum mileage that the electric vehicle can travel when fully charged;

[0067] Specifically, based on the association between the daily mileage and the initial state of charge, the eighth target formula is constructed to simulate the relationship between the initial state of charge and the daily mileage.

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

[0069] Specifically, since the charging end time satisfies the normal distribution, the ninth target formula is constructed based on the probability density function to obtain the random variable of the charging end time.

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

[0071] Step S2021, construct the tenth target formula The above tenth objective formula is used to simulate the change trend of the active power of the node in the above power frequency modulation system with the aggregated temperature control load and the aggregated electric vehicle load, where E N is the set of nodes in the power grid; pf ih,t is the active power flowing from node i to node h at time t, is the active power of the generator at node i at time t, is the active base load of node i at time t, is the frequency modulation power change instruction of node i at time t; is the remaining capacity after frequency regulation failure at node i at time t;

[0072] Specifically, based on the impact of the regulation of electric vehicles on the power generation of the power grid, an active power balance formula for each node of the above power grid is constructed to obtain the above tenth target formula.

[0073] Step S2022, constructing the eleventh target formula: Twelfth Target Formula And the thirteenth goal formula: The above eleventh target formula is used to simulate the changing trend of power generation cost with the above active power, the above twelfth target formula is used to simulate the changing trend of carbon emission cost with the above active power, and the above thirteenth target formula is used to simulate the changing trend of frequency regulation failure cost with the above remaining capacity of frequency regulation failure, where C gen is the above power generation cost, C carbon is the above carbon emission cost, C fail is the above-mentioned frequency regulation failure cost, a i and b i is the preset coefficient of the above power generation cost, c tax represents the tax per unit of carbon emissions, c fail is the frequency regulation failure cost per unit flux;

[0074] Specifically, the present application simplifies the cost of the power frequency regulation system into three parts, namely, power generation cost, carbon emission cost and frequency regulation failure penalty cost, and constructs corresponding formulas based on the correlation between the above costs and active power and reproductive capacity to simulate the above correlation, and obtains the above eleventh target formula, the twelfth target formula and the above thirteenth target formula.

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

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

[0077] Step S2024, combining the tenth target formula, the eleventh target formula, the twelfth target formula, the thirteenth target formula and the fourteenth target formula to obtain the cost estimation model;

[0078] Specifically, the cost estimation model is constructed according to the tenth target formula, the eleventh target formula, the twelfth target formula, the thirteenth target formula and the fourteenth target formula.

[0079] In order to construct the first restriction condition group, in an optional implementation, step S203 includes:

[0080] Step S20311, construct the above-mentioned state of charge restriction conditions corresponding to each electric vehicle to obtain the first target restriction conditions:

[0081]

[0082]

[0083] in, and is the maximum and minimum state of charge of the j-th electric vehicle of the above aggregator i, SOC need In order to meet the minimum state of charge required by users for travel, The state of charge is SOC t Charging power at

[0084] Specifically, the first target restriction condition is used to limit the state of charge of the on-board battery of the electric vehicle to prevent the on-board battery from being overcharged or over-discharged. Further, the first target restriction condition is also used to limit the state of charge at the end of charging to meet the minimum travel requirements of the user.

[0085] Step S20312, construct the above-mentioned charging and discharging power restriction conditions corresponding to each electric vehicle to obtain the second target restriction conditions:

[0086]

[0087] Among them, η ch and η dis are the efficiency of charge and discharge respectively;

[0088] Specifically, the second target restriction condition is used to restrict the charging and discharging power of the electric vehicle to be less than the rated power.

[0089] Step S20313, construct the above-mentioned charge and discharge state restriction conditions corresponding to each electric vehicle to obtain the third target restriction condition:

[0090]

[0091] in, and is a binary variable of the charging and discharging state of the charging load of the electric vehicle;

[0092] Specifically, and A binary variable is used to represent the charging and discharging status of the electric vehicle charging load (1 for charging / discharging, 0 for not charging / discharging).

[0093] Step S20314: Combining the first target constraint condition, the second target constraint condition and the third target constraint condition to obtain the first constraint condition group.

[0094] Specifically, a first constraint condition group is constructed according to the first target constraint condition, the second target constraint condition and the third target constraint condition.

[0095] In order to construct the second constraint condition group, in an optional implementation, step S203 includes:

[0096] Step S20321, construct the reactive power balance constraint condition corresponding to the power frequency modulation system, and obtain the fourth target constraint condition:

[0097]

[0098] Among them, qf ih,t is the reactive power flowing from node i to node h at time t, is the reactive power of the generator at node i at time t, is the reactive base load of node i at time t;

[0099] Specifically, each node of the above-mentioned power grid needs to satisfy reactive power balance while satisfying active power balance. On this basis, the above-mentioned fourth target constraint condition is constructed based on the reactive power balance of each node of the power grid.

[0100] Step S20322, construct the generator power generation restriction condition corresponding to the power frequency modulation system, and obtain the fifth target restriction condition:

[0101]

[0102] in, and are the maximum and minimum active powers of the generator at node i respectively; and 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;

[0103] Specifically, the fifth target constraint condition is obtained by constraining the operating state of the generators at each node of the power grid.

[0104] Step S20323, construct the power flow calculation constraint condition corresponding to the power frequency modulation system, and obtain the sixth target constraint condition:

[0105]

[0106] Among them, V i,tis the square of the voltage amplitude of node i at time t, r ih represents the resistance of ih, x ih Represents the reactance of line ih, V i max and V i min represents the maximum and minimum values ​​of the square of the voltage amplitude at node i, Indicates the maximum apparent power of line ih;

[0107] Specifically, the linearized Distflow model is used to represent the power flow equation of the power system without considering network losses to obtain the above formula, and the node voltage and power flow constraints are further constructed based on the power flow equation to obtain the above sixth objective constraint condition.

[0108] Step S20324: construct the second constraint condition group according to the fourth target constraint condition, the fifth target constraint condition and the sixth target constraint condition.

[0109] Specifically, the fourth target constraint condition, the fifth target constraint condition, and the sixth target constraint condition are combined to construct the second constraint condition group.

[0110] In order to obtain the objective function, before the particle swarm algorithm based on random simulation solves the capacity prediction model with the output of the cost estimation model obtaining the minimum value as the objective function, the method further includes:

[0111] Step S401, constructing an alternative objective function minC total ;

[0112] Specifically, the objective function C of the optimization model that does not consider the uncertainty of user behavior is total It contains three parts, namely, power generation cost C gen 、Carbon emission cost C carbon and frequency modulation failure penalty cost 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 The coefficient representing the cost of electricity generation; c tax represents carbon tax; is the carbon emission factor of the generator at node i; c fail is the unit cost of frequency regulation failure.

[0116] Step S402: Introduce the constraint conditions for the above uncertainty factors according to the above alternative objective function to 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 conditions and the objective function respectively, and are the changes in the charging and discharging power of the electric vehicle cluster at node i actually participating in the frequency regulation up and down at time t respectively, and are the above-mentioned frequency regulation up capacity and the above-mentioned frequency regulation down capacity that the electric vehicle cluster at node i can actually provide at time t respectively;

[0117] Specifically, applying chance-constrained programming (CCP) takes into account the uncertainty of the frequency regulation capacity available for electric vehicles. The above alternative objective function introduces the constraint conditions for the above uncertainty factors to obtain the optimization model as shown in the above formula. Pr{} represents the probability that the event in the parentheses holds; β, α i are the confidence levels of the constraint conditions 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 at node i actually participating in the frequency regulation up and down at time t.

[0118] In the above embodiment, the above optimization model is solved. Let the uncertainty factor be the 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 foreign 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 random variable ξ according to its probability distribution Φ(ξ); 2. If g(x,ξ)≤0 holds, then N'=N'+1; Repeat 1 and 2 N times. If Then the opportunity constraint holds, otherwise it does not hold.

[0124] By using the above-mentioned random simulation-based particle swarm optimization algorithm to solve the random optimization problem based on chance constraints, the response of electric vehicles to different frequency regulation instructions under a certain confidence level can be obtained, thereby optimizing the frequency regulation performance of the entire power system. The dispatching center of the power system sends frequency regulation instructions to load aggregators through AGC. Specifically, these frequency regulation instructions indicate the power that load aggregators need to increase or decrease at a specific time. Through the optimization model we proposed, the frequency regulation power that they should bear can be accurately determined according to the current status and capabilities of each load aggregator under the total AGC frequency regulation instruction. This method fully considers the uncertainty of the frequency regulation capacity that electric vehicle loads can provide, is closer to the actual situation, and can more effectively allocate frequency regulation responsibilities.

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

[0126] The embodiment of the present application also provides a scheduling device for electric vehicle frequency modulation based on user uncertainty. It should be noted that the scheduling device for electric vehicle frequency modulation based on user uncertainty in the embodiment of the present application can be used to execute the scheduling method for electric vehicle frequency modulation based on user uncertainty provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0127] The following introduces a scheduling device for electric vehicle frequency modulation based on user uncertainty provided in an embodiment of the present application.

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

[0129] A first construction unit 10 is used to construct a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0130] Specifically, in order to introduce the constraints of user behavior uncertainty on the participation of electric vehicles in frequency modulation, the present application sets three uncertainty factors: charging start time, charging end time and initial charge state, and establishes them as random variables accordingly, and then constructs the above-mentioned capacity prediction model based on the correlation between random variables and the frequency modulation capacity of electric vehicles.

[0131] A second construction unit 20 is used to construct a cost estimation model according to the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0132] Specifically, based on the above-mentioned capacity prediction model, the simulation of the operating parameters of the electric vehicle is associated, and a cost estimation model is further established based on the operating parameters. The present application sets the above-mentioned cost estimation model to take into account the power generation cost, carbon emission cost and frequency regulation failure penalty cost of the power frequency regulation system.

[0133] A third construction unit 30 is used to construct a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to limit the operating parameters of the electric vehicle, and the second restriction condition group is used to limit the operating parameters of each node of the power frequency regulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0134] Specifically, a corresponding restriction condition group is constructed for each of the above models to restrict the input parameters of the model, thereby obtaining the above first restriction condition and the above second restriction condition.

[0135] The calculation unit 40 is used to solve the capacity prediction model based on the particle swarm algorithm of random simulation under the constraints of the first constraint condition group and the second constraint condition group, taking the minimum value of the output of the cost estimation model as the objective function, obtain the operating parameters of the electric vehicle, and generate the target frequency regulation scheduling strategy according to the operating parameters;

[0136] Specifically, under the constraints of the first and second constraint groups, uncertain factors 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 algorithm, the capacity prediction model is solved with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and the target frequency modulation scheduling strategy is generated according to the operating parameters.

[0137] The control unit 50 is used to control the operation of the electric vehicle based on the target frequency regulation scheduling strategy so as to maximize the frequency regulation capacity benefit per unit cost.

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

[0139] Through the above embodiment, the first construction unit constructs a capacity prediction model, and the above capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, and the above uncertainty factors at least include the charging start time, the charging end time and the initial charge state; the second construction unit constructs a cost estimation model according to the above capacity prediction model, and the above cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the above electric vehicle; the third construction unit constructs the first restriction condition group and the second restriction condition group, and the above first restriction condition group is used to limit the operating parameters of the above electric vehicle, and the above second restriction condition group is used to limit the operating parameters of each node of the power frequency regulation system. The first constraint condition group at least includes the state of charge constraint condition, the charge and discharge power constraint condition and the charge and discharge state constraint condition, and the second constraint condition group at least includes the reactive balance constraint condition, the generator power generation constraint condition and the flow calculation constraint condition; the calculation unit solves the capacity prediction model based on the random simulation particle swarm algorithm under the constraints of the first constraint condition group and the second constraint condition group, and obtains the operation parameters of the electric vehicle, and generates the target frequency modulation scheduling strategy according to the operation parameters; the control unit controls 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. This application constructs a capacity prediction model based on the influence of uncertainty factors brought by user behavior on the participation of electric vehicle load in secondary frequency modulation, and constructs a frequency modulation cost model on this basis. Under relevant constraints, the output value of the frequency modulation cost model is minimized as the objective function through the random simulation particle swarm algorithm to solve it, and ensure that the solution is performed at the confidence level, so as to realize the participation of electric vehicles in power system frequency modulation under the interference of user uncertainty behavior. This method solves the problem that the frequency modulation strategy in the prior art does not consider the uncertainty of user behavior, resulting in deviation in frequency modulation scheduling control.

[0140] The above-mentioned dispatching device for electric vehicle frequency modulation based on user uncertainty includes a processor and a memory, and the above-mentioned first construction unit, second construction unit, third construction unit, calculation unit and control unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0141] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the scheduling accuracy of the electric vehicle frequency modulation can be improved by adjusting the kernel parameters.

[0142] The memory may include non-permanent memory in a computer-readable medium, 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] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the scheduling method for electric vehicle frequency modulation based on user uncertainty.

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

[0145] Step S201, constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0146] Step S202, constructing a cost estimation model based on the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0147] Step S203, constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency modulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0148] Step S204, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters;

[0149] Step S205: 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.

[0150] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the program executes the above-mentioned scheduling method for electric vehicle frequency modulation based on user uncertainty when running.

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

[0152] Step S201, constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0153] Step S202, constructing a cost estimation model based on the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0154] Step S203, constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency modulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0155] Step S204, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters;

[0156] Step S205: 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.

[0157] An embodiment of the present invention provides a power frequency modulation system, the power frequency modulation system comprising a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0158] Step S201, constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0159] Step S202, constructing a cost estimation model based on the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0160] Step S203, constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency modulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0161] Step S204, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters;

[0162] Step S205: 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.

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

[0164] Step S201, constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include the charging start time, the charging end time and the initial state of charge;

[0165] Step S202, constructing a cost estimation model based on the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total grid frequency regulation cost along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met;

[0166] Step S203, constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency modulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition;

[0167] Step S204, under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation is used to solve the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters;

[0168] Step S205: 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.

[0169] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0170] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions 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] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0176] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0177] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0178] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0179] 1) The scheduling method of electric vehicle frequency regulation based on user uncertainty of the present application firstly constructs a capacity prediction model, the above-mentioned capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, the above-mentioned uncertainty factors at least include the charging start time, the charging end time and the initial charge state; then, a cost estimation model is constructed according to the above-mentioned capacity prediction model, the above-mentioned cost estimation model is used to simulate the change trend of the total cost of grid frequency regulation with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the above-mentioned electric vehicle; then, a first restriction condition group and a second restriction condition group are constructed, the above-mentioned first restriction condition group is used to limit the operating parameters of the above-mentioned electric vehicle, and the above-mentioned second restriction condition group is used to limit the power frequency regulation The operating parameters of each node of the system, the above-mentioned first constraint condition group at least includes charge state constraint conditions, charge and discharge power constraint conditions and charge and discharge state constraint conditions, and the above-mentioned second constraint condition group at least includes reactive power balance constraint conditions, generator power generation constraint conditions and power flow calculation constraint conditions; then, under the constraints of the above-mentioned first constraint condition group and the above-mentioned second constraint condition group, the particle swarm algorithm based on random simulation solves the above-mentioned capacity prediction model with the minimum value of the output of the above-mentioned cost estimation model as the objective function, and obtains the operating parameters of the above-mentioned electric vehicle, and generates a target frequency regulation scheduling strategy according to the above-mentioned operating parameters; finally, the operation of the above-mentioned electric vehicle is controlled based on the above-mentioned 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 uncertainty factors brought by user behavior on the participation of electric vehicle load in secondary frequency regulation, and on this basis constructs a frequency regulation cost model. Under relevant constraints, the output value of the frequency regulation cost model is minimized as the objective function through a particle swarm algorithm of random simulation to solve the problem. The solution is guaranteed to be solved at a confidence level, and electric vehicles are allowed to participate in power system frequency regulation under the interference of user uncertainty behavior. This method solves the problem that the frequency regulation strategy in the prior art does not consider the uncertainty of user behavior, resulting in deviations in frequency regulation dispatching control.

[0180] 2) The scheduling device for electric vehicle frequency regulation based on user uncertainty of the present application, the first construction unit constructs a capacity prediction model, the above-mentioned capacity prediction model is used to simulate the correlation between the frequency regulation capacity of the electric vehicle and the uncertainty factors, and the above-mentioned uncertainty factors at least include the charging start time, the charging end time and the initial charge state; the second construction unit constructs a cost estimation model according to the above-mentioned capacity prediction model, and the above-mentioned cost estimation model is used to simulate the change trend of the total cost of power grid frequency regulation with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the above-mentioned electric vehicle; the third construction unit constructs a first restriction condition group and a second restriction condition group, the above-mentioned first restriction condition group is used to limit the operating parameters of the above-mentioned electric vehicle, and the above-mentioned second restriction condition group is used to limit the electric vehicle. The operating parameters of each node of the power frequency regulation system, the first restriction condition group at least includes the state of charge restriction condition, the charge and discharge power restriction condition and the charge and discharge state restriction condition, and the second restriction condition group at least includes the reactive power balance restriction condition, the generator power generation restriction condition and the power flow calculation restriction condition; under the restriction of the first restriction condition group and the second restriction condition group, the calculation unit solves the capacity prediction model based on the particle swarm algorithm of random simulation with the output of the cost estimation model as the objective function to obtain the minimum value, obtain the operating parameters of the electric vehicle, and generate the target frequency regulation scheduling strategy according to the operating parameters; 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 uncertainty factors brought by user behavior on the participation of electric vehicle load in secondary frequency regulation, and on this basis constructs a frequency regulation cost model. Under relevant constraints, the output value of the frequency regulation cost model is minimized as the objective function through a particle swarm algorithm of random simulation to solve the problem. The solution is guaranteed to be solved at a confidence level, and electric vehicles are allowed to participate in power system frequency regulation under the interference of user uncertainty behavior. This method solves the problem that the frequency regulation strategy in the prior art does not consider the uncertainty of user behavior, resulting in deviations in frequency regulation dispatching control.

[0181] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for dispatching electric vehicle frequency modulation based on user uncertainty, characterized in that: include: Constructing a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include charging start time, charging end time and initial charge state; Constructing a cost estimation model according to the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid along with the frequency regulation load of the electric vehicle when the travel demand of the electric vehicle is met; Constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency regulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition; Under the constraints of the first constraint condition group and the second constraint condition group, the particle swarm algorithm based on random simulation solves the capacity prediction model with the minimum value of the output of the cost estimation model as the objective function to obtain the operating parameters of the electric vehicle, and generates a target frequency modulation scheduling strategy according to 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.

2. The method according to claim 1, characterized in that: Build a capacity forecasting model, including: Constructing the first target formula The first target formula is used to simulate the change trend of the state of charge of the electric vehicle with the charging power, wherein SOC i,j,t+1 and SOC i,j,t are the state of charge of the j-th electric vehicle of aggregator i at time t+1 and time t, Δt is the charging time, η ch and η dis are the charge and discharge efficiency, and are the charging and discharging power of the j-th electric vehicle of the aggregator i at time t, E i,j represents the capacity of the j-th EV of aggregator i; Constructing the second target formula The second target formula is used to simulate the changing trend of the charging power of the electric vehicle with the state of charge of the electric vehicle, wherein SOC t is the state of charge; Constructing the third target formula The third objective formula is used to simulate the variation trend of the aggregated electric vehicle load of the aggregator with the state of charge of the electric vehicle, wherein: is the aggregated electric vehicle load of the aggregator i at time t, is the charge state set of electric vehicles of the aggregator i; Constructing the fourth goal formula And the fifth goal formula The fourth objective formula is used to simulate the change trend of the upward 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 change trend of the downward frequency regulation capacity of the electric vehicle with the load of the aggregated electric vehicle, wherein, and are the upper frequency regulation capacity and the lower frequency regulation capacity of the electric vehicle respectively, and are the maximum values ​​of charge and discharge power respectively; The capacity prediction model is obtained by combining the first target formula, the second target formula, the third target formula, the fourth target formula and the fifth target formula.

3. The method according to claim 2, characterized in that Before constructing the first target formula, the method further includes: Constructing the Sixth Target Formula The sixth objective formula is the probability density function of the charging start time, where μ t is the expected charging start time, σ t is the variance of the charging start time, t arrive is the charging start time; Constructing the Seventh Goal Formula The seventh objective formula is the probability density function of the daily mileage of the electric vehicle, where μ b is the expected daily mileage, σ b is the variance of the daily mileage, s is the daily mileage; Constructing the eighth goal formula The eighth target 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, SOC m is the maximum available power of the electric vehicle, s max The maximum mileage that the electric vehicle can travel when fully charged; Constructing the Ninth Goal Formula The ninth target formula is the probability density function of the charging end time, where: is the expected end time of charging, σ l is the variance of the charging end time, E battery is the battery capacity of the electric vehicle, t leave is the charging end time.

4. The method according to claim 2, characterized in that: A cost estimation model is constructed according to the capacity prediction model, including: Constructing the Tenth Target Formula The tenth objective formula is used to simulate the change trend of the active power of the node in the power frequency modulation system with the aggregated temperature control load and the aggregated electric vehicle load, where E N is the set of nodes in the power grid; pf ih,t is the active power flowing from node i to node h at time t, is the active power of the generator at node i at time t, is the active base load of node i at time t, is the frequency modulation power change instruction of node i at time t; is the remaining capacity after frequency regulation failure at node i at time t; Constructing the Eleventh Target Formula Twelfth Target Formula and the thirteenth goal formula The eleventh target formula is used to simulate the changing trend of power generation cost with the active power, the twelfth target formula is used to simulate the changing trend of carbon emission cost with the active power, and the thirteenth target formula is used to simulate the changing trend of frequency regulation failure cost with the remaining capacity of frequency regulation failure, wherein C gen is the power generation cost, C carbon is the carbon emission cost, C fail is the frequency modulation failure cost, a i and b i is the preset coefficient of the power generation cost, c tax represents the tax per unit of carbon emissions, c fail is the frequency regulation failure cost per unit flux; Construct the fourteenth target formula C total =C gen +C carbon +C fail The fourteenth objective formula is used to simulate the changing trend of the total cost of the power grid frequency regulation with the power generation cost, the carbon emission cost and the frequency regulation failure cost, wherein C total is the total cost of frequency regulation of the power grid; The cost estimation model is obtained by combining the tenth target formula, the eleventh target formula, the twelfth target formula, the thirteenth target formula and the fourteenth target formula.

5. The method according to claim 2, characterized in that: Construct the first set of constraints, including: Construct the state of charge constraint conditions corresponding to each electric vehicle to obtain the first target constraint conditions in, and is the maximum and minimum state of charge of the j-th electric vehicle of the aggregator i, SOC need In order to meet the minimum state of charge required by users for travel, The state of charge is SOC t Charging power at Construct the charging and discharging power restriction conditions corresponding to each electric vehicle to obtain the second target restriction conditions Among them, η ch and η dis are the efficiency of charge and discharge respectively; Construct the charging and discharging state constraint conditions corresponding to each electric vehicle to obtain the third target constraint condition in, and is a binary variable of the charging and discharging state of the charging load of the electric vehicle; 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.

6. The method according to claim 4, characterized in that Construct the second set of constraints, including: Construct the reactive power balance constraint condition corresponding to the power frequency modulation system to obtain the fourth target constraint condition Among them, qf ih,t is the reactive power flowing from node i to node h at time t, is the reactive power of the generator at node i at time t, is the reactive base load of node i at time t; Construct the generator power generation constraint condition corresponding to the power frequency regulation system to obtain the fifth target constraint condition in, and are the maximum and minimum active powers of the generator at node i respectively; and 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; Construct the power flow calculation constraint condition corresponding to the power frequency modulation system to obtain the sixth target constraint condition Among them, V i,t is the square of the voltage amplitude of node i at time t, r ih represents the resistance of the circuit ih, x ih Represents the reactance of line ih, V i max and V i min represents the maximum and minimum values ​​of the square of the voltage amplitude at node i, Indicates the maximum apparent power of line ih; The second constraint group is constructed according to the fourth target constraint, the fifth target constraint and the sixth target constraint.

7. The method according to claim 6, characterized in that Before solving the capacity prediction model by using the particle swarm algorithm based on random simulation and taking the minimum value of the output of the cost estimation model as the objective function, the method further includes: Construct alternative objective function minC total ; According to the alternative objective function, the restriction condition on the uncertainty factor is introduced to obtain the objective function: in, The objective function C total The minimum value when the confidence level is greater than or equal to β. Pr{} represents the probability of the event in the brackets, β, α i are the confidence of the constraint condition and the objective function, respectively. and are the actual charging and discharging power changes of the electric vehicle cluster participating in the up and down frequency modulation at time t, and They are respectively the upward frequency regulation capacity and the downward frequency regulation capacity that can be actually provided by the electric vehicle cluster at the node i at time t.

8. A dispatching device for electric vehicle frequency modulation based on user uncertainty, characterized in that: The device comprises: A first construction unit is used to construct a capacity prediction model, wherein the capacity prediction model is used to simulate the correlation between the frequency modulation capacity of the electric vehicle and uncertainty factors, wherein the uncertainty factors at least include a charging start time, a charging end time, and an initial state of charge; A second construction unit is used to construct a cost estimation model according to the capacity prediction model, wherein the cost estimation model is used to simulate the change trend of the total frequency regulation cost of the power grid with the frequency regulation load of the electric vehicle under the condition of meeting the travel demand of the electric vehicle; a third construction unit, for constructing a first restriction condition group and a second restriction condition group, wherein the first restriction condition group is used to restrict the operating parameters of the electric vehicle, and the second restriction condition group is used to restrict the operating parameters of each node of the power frequency regulation system, wherein the first restriction condition group at least includes a state of charge restriction condition, a charge and discharge power restriction condition, and a charge and discharge state restriction condition, and the second restriction condition group at least includes a reactive balance restriction condition, a generator power generation restriction condition, and a power flow calculation restriction condition; a computing unit, configured to solve the capacity prediction model based on a particle swarm algorithm of random simulation under the constraints of the first constraint condition group and the second constraint condition group, taking the minimum value of the output of the cost estimation model as the objective function, obtain the operating parameters of the electric vehicle, and generate a target frequency modulation scheduling strategy according to the operating parameters; A control unit is used to control the operation of the electric vehicle based on the target frequency regulation scheduling strategy so as to maximize the frequency regulation 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, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A power frequency modulation 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 are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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