Day-ahead scheduling strategy and system for electric vehicles based on conversion economic target

Abstract

The present application relates to a kind of electric vehicle day-ahead scheduling strategy based on conversion economic target, comprising the following steps: S1: obtaining basic information;S2: establishing electric vehicle charging model;S3: whether the electric vehicle scheduling time obtained above exceeds constraint condition is analyzed;At the same time, whether the calculated number of electric vehicles is greater than N is judged, if less, return to S2 continue to calculate until the scheduling plan of N electric vehicles is completed;S4: the objective function of electric vehicle day-ahead scheduling is calculated, and the optimized day-ahead scheduling plan is obtained.The present application makes full use of the flexibility of electric vehicle load, considers the interests of grid side and user side, and limits the objective function with minimum variance, reduces the negative impact of electric vehicle connected to the grid, and the technical index of grid load peak-valley difference is converted into the economic index of minimum user charging cost.The strategy guarantees the optimal economy of user, reduces the grid load peak-valley difference at the same time, and verifies the effectiveness of the strategy.

Description

Technical Field

[0001] This invention belongs to the field of electric vehicle economic dispatching technology, specifically relating to a day-ahead dispatching strategy and system for electric vehicles based on a conversion of economic objectives. Background Technology

[0002] With the development of battery technology, electric vehicles (EVs), as a green mode of transportation, have also developed rapidly, and countries around the world are vigorously promoting them. As a new type of load, EV charging behavior exhibits "multi-temporal and spatial scale discreteness." When large-scale EVs are connected to the power grid, they cause significant load impacts on the power system, seriously affecting the safety of grid operation. On the one hand, the sheer number of EVs and the fluctuating and random nature of EV load mean that a large number of EV charging loads overlap with peak electricity demand periods, achieving a "peak-on-peak" effect and greatly increasing the pressure on power system dispatching. On the other hand, as a random load, the disorderly charging of EVs places higher demands on the installed capacity of the power supply side. Insufficient installed capacity is insufficient to guarantee the normal operation of the grid during disorderly EV charging, while excessive capacity leads to increased grid operating costs, posing a trade-off between economic efficiency and reliability in grid planning. Therefore, guiding EV charging and discharging through reasonable incentives based on demand response strategies is of great significance for the safe operation of the power grid.

[0003] During daily use, the charging time of electric vehicles (EVs) overlaps with the peak electricity pricing period of the power grid, resulting in excessively high charging costs for users and increasing their overall operating expenses. Time-of-use (TOU) pricing is the most effective means of regulating EV charging behavior, altering charging and discharging patterns to shift some peak-hour charging to off-peak hours, thereby increasing electricity consumption during off-peak periods. With the development of EV-grid interaction technologies, electricity subsidies can incentivize some EVs to use vehicle-to-grid (V2G) technology to release significant amounts of electricity to the grid during peak load periods, thus shaving off peak loads and improving the reliability of the power system, while reducing power generation and operating costs. Therefore, the formulation of peak-valley charging and discharging pricing is crucial for the development of EVs. A reasonable pricing mechanism encourages EV users to actively respond to power balance during off-peak hours, ensuring the safe and efficient operation of the regional power grid and reducing the operating costs for EV users. This can further increase user acceptance of EVs and has practical significance for their widespread adoption. Therefore, how to solve the problem of economical dispatching of EVs is a critical issue that urgently needs to be addressed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and propose a day-ahead scheduling strategy for electric vehicles based on the conversion of economic objectives, so as to solve the problems mentioned in the background art.

[0005] The technical problem solved by this invention is achieved through the following technical solution:

[0006] A day-ahead scheduling strategy for electric vehicles based on a shifted economic objective, characterized by the following steps:

[0007] S1: Obtain basic information, including: grid base load, N electric vehicles, and time-of-use electricity price; simultaneously obtain Monte Carlo simulation of electric vehicle driving range, charging power, and battery capacity;

[0008] S2: Establish an electric vehicle charging model, calculate the initial charge state of the electric vehicle using the acquired basic information, obtain the electric vehicle scheduling time by combining Monte Carlo simulation of the arrival and departure times of the electric vehicle, and use CPLEX to solve the day-ahead scheduling problem of the electric vehicle.

[0009] S3: Analyze whether the electric vehicle scheduling time obtained above exceeds the constraints. If it does, return to S1 and recalculate until all constraints are met. At the same time, determine whether the number of electric vehicles calculated is greater than N. If it is less than N, return to S2 and continue the calculation until the scheduling plan for N electric vehicles is completed.

[0010] S4: Calculate the objective function for day-ahead scheduling of electric vehicles to obtain the optimized day-ahead scheduling plan.

[0011] As a further improvement to this technical solution, the method for establishing an electric vehicle charging model includes:

[0012] First, obtain the probability density function of daily driving distance, the probability density function of electric vehicle arrival time, and the probability density function of daily departure time of electric vehicle.

[0013] Furthermore, a day-ahead scheduling model for electric vehicles is established based on the obtained functions:

[0014] S2.1: Based on the electric vehicle's mileage and battery parameters, obtain the electric vehicle's state of charge upon arrival:

[0015]

[0016] Among them, S i，0 S represents the initial charge state when the i-th electric vehicle arrives; i，end It is the charge state of the i-th vehicle when it leaves; d i E represents the mileage traveled by the i-th electric vehicle. 100 Electricity consumption per 100 kilometers for electric vehicles; C i It is the battery capacity of the i-th vehicle;

[0017] S2.2: Day-ahead scheduling predicts the daily load curve based on the historical load of a certain region. A 24-hour day in this region is divided into 96 time periods, each lasting 15 minutes, for modeling and simulation. Based on the arrival and departure time distribution of electric vehicles, 13:00 is the first time period, and the base load for the j-th (j = 1, 2, ..., 96) time period is P. bj The charging power of the i-th vehicle is P. ei Assume that the charging station provides constant power charging to electric vehicles, and only during the time interval [t] between the arrival and departure of the i-th electric vehicle. arr，i , t dep，i Optimize in ]

[0018]

[0019] Among them, P ei Let P be the charging power of the i-th electric vehicle within the scheduling period t. evci The rated charging power of the i-th electric vehicle. These are 0-1 variables corresponding to the charging state.

[0020] S2.3: Assume the load of the electric vehicle being charged in the t-th cycle is P. j If there are a total of N electric cars, then:

[0021]

[0022] Among them, the grid load P in the j-th time period sj For electric vehicle load P j and base load P bj superposition,

[0023] P sj =P j +P bj j = 1, 2, 3, ..., 96

[0024] Regarding the charging process of electric vehicles, there are

[0025]

[0026] Where η is the charging efficiency of the electric vehicle; C i P is the battery capacity of the i-th vehicle; ei S represents the charging power of the i-th electric vehicle within the scheduling period t; Δt is the time interval; S i (t) represents the charge state of the i-th vehicle at time t; S i (t-1) represents the charge state of the i-th vehicle at time t-1.

[0027] The above is the day-ahead scheduling model for electric vehicles.

[0028] As a further improvement to this technical solution, the driving distance of an electric vehicle follows a log-normal distribution, and the probability density function of the daily driving distance is f. D (d) is:

[0029]

[0030] In the formula, f D (d) represents the probability density function of daily travel distance; σ D Let be the standard deviation of daily mileage, and σ D =0.88; μ D Let μ be the expected value of the daily mileage. D =3.2; d is the daily mileage, and the unit is km.

[0031] As a further improvement to this technical solution, the probability density function of the electric vehicle's arrival time is:

[0032]

[0033] Among them, f arr (t) is the probability density function of the electric vehicle's arrival time at home, μ arr Let μ be the expected arrival time of the electric vehicle. arr =17.6; σ arr Let be the standard deviation of the arrival time of the electric vehicle, and σ arr =3.4.

[0034] As a further improvement to this technical solution, the probability density function of the daily departure time of the electric vehicle is:

[0035]

[0036] Among them, f dep (t) is the probability density function of the daily departure time of electric vehicles, μ dep Let μ be the expected departure time of the electric vehicle. dep =8.92; σ dep Let be the standard deviation of the departure time of the electric vehicle, and σ dep =3.24.

[0037] As a further improvement to this technical solution, the objective function for calculating the day-ahead scheduling strategy for electric vehicles includes both technical and economic indicators:

[0038] The economic indicators are as follows:

[0039] The objective function is to minimize the charging cost for electric vehicle users, guiding them to charge during periods of low electricity prices and unifying the load across the time scale.

[0040]

[0041] In the formula, f1 represents the economic indicator for day-ahead dispatch of electric vehicles, c(j) is the charging price for time period j, T is the division of 96 time periods, and P... ei The charging power for the i-th electric vehicle;

[0042] The technical specifications are as follows:

[0043] For the technical indicators on the grid side, the variance and peak-to-valley difference of the grid load should be considered:

[0044] a) Variance

[0045] Variance is used to describe the dispersion of power grid load; a smaller load variance indicates a smaller degree of overall load fluctuation.

[0046]

[0047]

[0048] Among them, P sj Let J be the grid load for the j-th time period. It is the average grid load within the time period T, and Var is the variance of the grid load.

[0049] b) Peak-to-valley difference

[0050] Reducing the peak-to-valley difference can optimize the load curve.

[0051] p vd =max(P sj )-min(P sj )

[0052] Among them, P sj Let p be the grid load for the j-th time period. vd This refers to the peak-to-valley difference in the power grid load.

[0053] There are different dimensions in terms of economics and technology, which can be solved by obtaining the Pareto optimal solution.

[0054] As a further improvement to this technical solution, the constraints of the day-ahead scheduling strategy for electric vehicles include:

[0055] 1) Charge state constraints of electric vehicles

[0056]

[0057] in,S and These represent the upper and lower limits of the electric vehicle battery's state of charge, respectively.

[0058] 2) User travel constraints

[0059] S i，end ≤S(j), j=t dep，i

[0060] Among them, S i，end It is the charge state of the i-th vehicle when it leaves, t dep，i It is the departure time of the i-th vehicle;

[0061] 3) Charging station capacity constraints

[0062]

[0063] Among them, C tc It is the rated power of the charging station, P ei Let be the charging power of the i-th electric vehicle within the scheduling period t.

[0064] An electric vehicle day-ahead scheduling system based on a transformed economic objective, characterized by comprising an information acquisition module, an electric vehicle charging model establishment module, a scheduling plan analysis and judgment module, and a day-ahead scheduling plan optimization module.

[0065] The information acquisition module is used to acquire the grid base load, N electric vehicles, and time-of-use electricity price; at the same time, it acquires Monte Carlo simulations of the electric vehicle driving range, charging power, and battery capacity.

[0066] The electric vehicle charging model building module is used to calculate the initial charge state of electric vehicles using the acquired basic information, and to obtain the electric vehicle scheduling time by combining the Monte Carlo simulation of the arrival and departure times of electric vehicles. CPLEX is used to solve the day-ahead scheduling problem of electric vehicles.

[0067] The scheduling plan analysis and judgment module is used to analyze whether the electric vehicle scheduling time obtained above exceeds the constraints; at the same time, it determines whether the number of electric vehicles calculated is greater than N.

[0068] The day-ahead scheduling optimization module is used to calculate the objective function of day-ahead scheduling of electric vehicles and obtain an optimized day-ahead scheduling plan.

[0069] The method for establishing the electric vehicle charging model module is as follows:

[0070] First, obtain the probability density function of daily driving distance, the probability density function of electric vehicle arrival time, and the probability density function of electric vehicle daily departure time;

[0071] Then, based on the obtained functions, a day-ahead scheduling model for electric vehicles is established. The specific steps are as follows:

[0072] S2.1: Based on the electric vehicle's mileage and battery parameters, obtain the electric vehicle's state of charge upon arrival:

[0073]

[0074] Among them, S i，0 S represents the initial charge state when the i-th electric vehicle arrives; i，end It is the charge state of the i-th vehicle when it leaves; d i E represents the mileage traveled by the i-th electric vehicle. 100 Electricity consumption per 100 kilometers for electric vehicles; C i It is the battery capacity of the i-th vehicle;

[0075] S2.2: Day-ahead scheduling predicts the daily load curve based on the historical load of a certain region. A 24-hour day in this region is divided into 96 time periods, each lasting 15 minutes, for modeling and simulation. Based on the arrival and departure time distribution of electric vehicles, 13:00 is the first time period, and the base load for the j-th (j = 1, 2, ..., 96) time period is P. bj The charging power of the i-th vehicle is P. ei Assume that the charging station provides constant power charging to electric vehicles, and only during the time interval [t] between the arrival and departure of the i-th electric vehicle. arr，i , t dep，i Optimize in ]

[0076]

[0077] Among them, P ei Let P be the charging power of the i-th electric vehicle within the scheduling period t. evci The rated charging power of the i-th electric vehicle. These are 0-1 variables corresponding to the charging state;

[0078] S2.3: Assume the load of the electric vehicle being charged in the j-th cycle is P. j If there are a total of N electric cars, then:

[0079]

[0080] Among them, the grid load P in the j-th time period sj For electric vehicle load P j and base load P bj superposition,

[0081] P sj =P j +Pbj j = 1, 2, 3, ..., 96

[0082] Regarding the charging process of electric vehicles, there are

[0083]

[0084] Where η is the charging efficiency of the electric vehicle; C i P is the battery capacity of the i-th vehicle; ei S represents the charging power of the i-th electric vehicle within the scheduling period t; Δt is the time interval; S i (t) represents the charge state of the i-th vehicle at time t; S i (t-1) represents the charge state of the i-th vehicle at time t-1.

[0085] The day-ahead scheduling plan optimization module includes both technical and economic indicators:

[0086] The economic indicators are as follows:

[0087] The objective function is to minimize the charging cost for electric vehicle users, guiding them to charge during periods of low electricity prices and unifying the load across the time scale.

[0088]

[0089] In the formula, f1 represents the economic indicator for day-ahead dispatch of electric vehicles, c(j) is the charging price for time period j, T is the division of 96 time periods, and P... ei The charging power for the i-th electric vehicle;

[0090] The technical specifications are as follows:

[0091] For the technical indicators on the grid side, the variance and peak-to-valley difference of the grid load should be considered:

[0092] a) Variance

[0093] Variance is used to describe the degree of dispersion of power grid load; a smaller load variance indicates that the overall load fluctuation is smaller.

[0094]

[0095]

[0096] Among them, P sj Let j be the grid load in the j-th time period. It is the average grid load within the time period T, and Var is the variance of the grid load.

[0097] b) Peak-valley difference

[0098] Reducing the peak-to-valley difference can optimize the load curve.

[0099] p vd =max(P sj )-min(P sj )

[0100] Among them, P sj Let p be the grid load for the j-th time period. vd This refers to the peak-to-valley difference in the power grid load.

[0101] There are different dimensions in terms of economics and technology, which can be solved by obtaining the Pareto optimal solution.

[0102] The advantages and beneficial effects of this invention are as follows:

[0103] This paper proposes a day-ahead scheduling strategy and system for electric vehicles (EVs) based on a transformed economic objective. First, a Monte Carlo method is used to establish an EV state model based on the probability distribution of EV users' travel time and distance. Then, a day-ahead scheduling model minimizing user charging costs is established under time-of-use pricing. Finally, the strategy is validated using a day-ahead scheduling model for small fast-charging stations solved by CPLEX. The proposed scheduling strategy fully utilizes the flexibility of EV load, considers the interests of both the grid and user sides, and uses minimum variance to constrain the objective function, reducing the negative impact of EVs connected to the grid. The objective function transforms the technical indicator of grid load peak-valley difference into an economic indicator of minimizing user charging costs. Compared with the limitations of scheduling strategies using minimum variance as the objective function in global load optimization, this strategy reduces grid load peak-valley difference while ensuring optimal user economy, thus validating the effectiveness of the strategy. Attached Figure Description

[0104] Figure 1 This is a flowchart illustrating the solution process for the day-ahead scheduling plan of electric vehicles studied in this invention.

[0105] Figure 2 This invention compares the variances of two load curves under the economic dispatch of electric vehicles.

[0106] Figure 3 This invention compares the charging electricity price with the grid load under the economic dispatch of electric vehicles.

[0107] Figure 4 The load curve for the disordered charging mode under the economic dispatch of electric vehicles studied in this invention is shown.

[0108] Figure 5 This paper presents a comparison of load curves before and after optimization under the economic dispatching of electric vehicles, as studied in this invention. Detailed Implementation

[0109] The present invention will be further described in detail below through specific embodiments. The following embodiments are merely descriptive and not limiting, and should not be used to limit the scope of protection of the present invention.

[0110] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0111] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0112] Please see the appendix Figure 1 A day-ahead scheduling strategy for electric vehicles based on a shifted economic objective is characterized by comprising the following steps for implementing the proposed day-ahead scheduling strategy for electric vehicles based on a shifted economic objective:

[0113] S1: Obtain basic information, including: grid base load, N electric vehicles, and time-of-use electricity price; simultaneously obtain Monte Carlo simulation of electric vehicle driving range, charging power, and battery capacity;

[0114] S2: Establish an electric vehicle charging model, calculate the initial charge state of the electric vehicle using the acquired basic information, obtain the electric vehicle scheduling time by combining Monte Carlo simulation of the arrival and departure times of the electric vehicle, and use CPLEX to solve the day-ahead scheduling problem of the electric vehicle.

[0115] S3: Analyze whether the electric vehicle scheduling time obtained above exceeds the constraints. If it does, return to S1 and recalculate until all constraints are met. At the same time, determine whether the number of electric vehicles calculated is greater than N. If it is less than N, return to S2 and continue the calculation until the scheduling plan for N electric vehicles is completed.

[0116] S4: Calculate the objective function for day-ahead scheduling of electric vehicles to obtain the optimized day-ahead scheduling plan.

[0117] The time-of-use (TOU) pricing for the power grid is set for non-special loads within a certain area. Generally, the price during peak load periods is also the price during peak periods, and the price during off-peak periods is also the price during off-peak periods. Electric vehicle charging pricing adopts the domestic industrial TOU pricing model, as shown in the table below:

[0118]

[0119] With a large number of electric vehicles being connected to the power grid without proper coordination, it is necessary to analyze the driving and charging characteristics of electric vehicles in order to incorporate them into the load optimization scheduling model. Methods for establishing an electric vehicle charging model include:

[0120] First, obtain the probability density function of daily driving distance, the probability density function of electric vehicle arrival time, and the probability density function of daily departure time of electric vehicle.

[0121] Furthermore, a day-ahead scheduling model for electric vehicles is established based on the obtained functions:

[0122] S2.1: Based on the electric vehicle's mileage and battery parameters, obtain the electric vehicle's state of charge upon arrival:

[0123]

[0124] Among them, S i，0 S represents the initial charge state when the i-th electric vehicle arrives; i，end It is the charge state of the i-th vehicle when it leaves; d i E represents the mileage traveled by the i-th electric vehicle. 100 Electricity consumption per 100 kilometers for electric vehicles; C i It is the battery capacity of the i-th vehicle;

[0125] S2.2: Day-ahead scheduling predicts the daily load curve based on the historical load of a certain region. A 24-hour day in this region is divided into 96 time periods, each lasting 15 minutes, for modeling and simulation. Based on the arrival and departure time distribution of electric vehicles, 13:00 is the first time period, and the base load for the j-th (j = 1, 2, ..., 96) time period is P. bj The charging power of the i-th vehicle is P. ei Assume that the charging station provides constant power charging to electric vehicles, and only during the time interval [t] between the arrival and departure of the i-th electric vehicle. arr，i , t dep，i Optimize in ]

[0126]

[0127] Among them, P ei Let P be the charging power of the i-th electric vehicle within the scheduling period t. evci The rated charging power of the i-th electric vehicle, These are 0-1 variables corresponding to the charging state.

[0128] S2.3: Assume the load of the electric vehicle being charged in the j-th cycle is P. j If there are a total of N electric cars, then:

[0129]

[0130] Among them, the grid load P in the j-th time period sj For electric vehicle load P j and base load P bj superposition,

[0131] P sj =P j +P bj j = 1, 2, 3, ..., 96

[0132] Regarding the charging process of electric vehicles, there are

[0133]

[0134] Where η is the charging efficiency of the electric vehicle; C i P is the battery capacity of the i-th vehicle; ei S represents the charging power of the i-th electric vehicle within the scheduling period t; Δt is the time interval; S i (t) represents the charge state of the i-th vehicle at time t; S i (t-1) represents the charge state of the i-th vehicle at time t-1.

[0135] The above is the day-ahead scheduling model for electric vehicles.

[0136] As a further improvement to this technical solution, the driving distance of an electric vehicle follows a log-normal distribution, and the probability density function of the daily driving distance is f. D (d) is:

[0137]

[0138] In the formula, f D (d) represents the probability density function of daily travel distance; σ D Let be the standard deviation of daily mileage, and σ D =0.88; μ D Let μ be the expected value of the daily mileage. D =3.2; d is the daily mileage, and the unit is km.

[0139] As a further improvement to this technical solution, the probability density function of the electric vehicle's arrival time is:

[0140]

[0141] Among them, f arr (t) is the probability density function of the electric vehicle's arrival time at home, μ arr Let μ be the expected arrival time of the electric vehicle.arr =17.6; σ arr Let be the standard deviation of the arrival time of the electric vehicle, and σ arr =3.4.

[0142] As a further improvement to this technical solution, the probability density function of the daily departure time of the electric vehicle is:

[0143]

[0144] Among them, f dep (t) is the probability density function of the daily departure time of electric vehicles, μ dep Let μ be the expected departure time of the electric vehicle. dep =8.92; σ dep Let be the standard deviation of the departure time of the electric vehicle, and σ dep =3.24.

[0145] As a further improvement to this technical solution, the objective function for calculating the day-ahead scheduling strategy for electric vehicles includes both technical and economic indicators:

[0146] The economic indicators are as follows:

[0147] The objective function is to minimize the charging cost for electric vehicle users, guiding them to charge during periods of low electricity prices and unifying the load across the time scale.

[0148]

[0149] In the formula, f1 represents the economic indicator for day-ahead dispatch of electric vehicles, c(j) is the charging price for time period j, T is the division of 96 time periods, and P... ei The charging power for the i-th electric vehicle;

[0150] The technical specifications are as follows:

[0151] For the technical indicators on the grid side, the variance and peak-to-valley difference of the grid load should be considered:

[0152] a) Variance

[0153] Variance is used to describe the dispersion of power grid load; a smaller load variance indicates a smaller degree of overall load fluctuation.

[0154]

[0155]

[0156] Among them, P sj Let j be the grid load in the j-th time period. It is the average grid load within the time period T, and Var is the variance of the grid load.

[0157] However, for the power grid, using variance alone may not be sufficient to optimize the load curve. Reducing variance may only reduce the overall fluctuation of the load curve, but local load curves may still exhibit significant fluctuations.

[0158] Please see the appendix Figure 2 This shows a comparison of the variance between the load curve for a specific day in March and the monthly average load curve for a certain region. The variance of line A is 59517 kW. 2 The variance of line B is 73889kW. 2 The two load curves are roughly similar in shape. Although line A has a smaller variance, the load curve has not improved significantly, but rather exhibits large load fluctuations in the short term. Therefore, the changes in grid load should be considered only in the technical specifications.

[0159] b) Peak-to-valley difference

[0160] Reducing the peak-to-valley difference can optimize the load curve.

[0161] p vd =max(P sj )-min(P sj )

[0162] Among them, P sj Let p be the grid load for the j-th time period. vd This refers to the peak-to-valley difference in the power grid load.

[0163] If the objective function on the grid side is to minimize the peak-valley load difference, the objective function becomes a multi-objective problem with different dimensions in terms of economics and technology, which can be solved by obtaining the Pareto optimal solution.

[0164] Electricity price distribution follows load distribution, with the highest price during peak load and the lowest price during off-peak load. Please refer to the appendix. Figure 3 As shown.

[0165] Since users charge during periods of low electricity prices, they are charged during off-peak hours. Therefore, guiding users to charge at the lowest possible cost can increase load during off-peak hours, thereby reducing the peak-to-valley difference. In this way, the technical indicators can also be achieved through the transformed economic objective function, greatly simplifying the model.

[0166] Therefore, to address the shortcomings of the objective function that minimizes variance, the objective function of the day-ahead scheduling model is to minimize the charging cost for users. The transformed economic objective function takes into account the technical indicators of the load, which simplifies the solution of the scheduling model compared with the multi-objective function.

[0167] As a further improvement to this technical solution, the constraints of the day-ahead scheduling strategy for electric vehicles include:

[0168] 1) Charge state constraints of electric vehicles

[0169]

[0170] in, S and These represent the upper and lower limits of the electric vehicle battery's state of charge, respectively.

[0171] 2) User travel constraints

[0172] S i，end ≤S(j), j=t dep，i

[0173] Among them, S i，end It is the charge state of the i-th vehicle when it leaves, t dep，i It is the departure time of the i-th vehicle;

[0174] 3) Charging station capacity constraints

[0175]

[0176] Among them, C tc It is the rated power of the charging station, P ei Let be the charging power of the i-th electric vehicle within the scheduling period t;

[0177] Without an electric vehicle scheduling strategy, electric vehicles will charge at a constant power as soon as they arrive at a charging station, which is a traditional disordered charging mode.

[0178] Please see the appendix Figure 4 The blue curve represents the base load excluding electric vehicle load, while the red curve represents the total load including unordered charging load from electric vehicles. It can be seen that the superposition of unordered charging load and base load increases peak power consumption and widens the load peak-to-valley difference.

[0179] This patent establishes a day-ahead electric vehicle scheduling model under the time-of-use pricing system, with the objective function of minimizing the charging cost for electric vehicle users. Simulation results are attached. Figure 5 As shown.

[0180] As can be seen from the figure, the load peak will not increase under the day-ahead dispatch strategy because the electricity price is higher during the peak load period. By guiding users to charge during the low electricity price period through dispatch, the charging load is transferred over time, the load valley value increases, thereby reducing the peak-valley difference of the grid load, which verifies the effectiveness of the proposed strategy.

[0181] The above case studies demonstrate that, compared to traditional disordered charging of electric vehicles, the day-ahead scheduling strategy based on the economic objective of conversion proposed in this patent can guide users to charge during off-peak hours, maximizing user benefits and reducing the peak-to-valley load difference in the power grid. Only the objective function of minimizing variance has limitations in the local optimization of the load curve; optimizing the objective function of minimizing the peak-to-valley difference ignores the economics of electric vehicle scheduling. Compared to multi-objective functions, the proposed objective function of minimizing the charging cost for electric vehicle users is simplified while achieving good optimization results, proving the rationality and superiority of the proposed day-ahead scheduling strategy for electric vehicles.

[0182] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0183] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

[0187] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A day-ahead scheduling method for electric vehicles based on a shifted economic objective, characterized in that: Including the following step: S1: Obtain basic information, including: grid base load, N electric vehicles, and time-of-use electricity price; simultaneously obtain Monte Carlo simulation of electric vehicle driving range, charging power, and battery capacity; S2: Establish an electric vehicle charging model, calculate the initial charge state of the electric vehicle using the acquired basic information, obtain the electric vehicle scheduling time by combining Monte Carlo simulation of the arrival and departure times of the electric vehicle, and use CPLEX to solve the day-ahead scheduling problem of the electric vehicle. S3: Analyze whether the obtained electric vehicle scheduling time exceeds the constraints. If it does, return to S1 to recalculate until all constraints are met. At the same time, determine whether the number of electric vehicles calculated is greater than N. If it is less than N, return to S2 to continue the calculation until the scheduling plan for N electric vehicles is completed. S4: Calculate the objective function for day-ahead scheduling of electric vehicles to obtain the optimized day-ahead scheduling plan; The method for establishing the electric vehicle charging model includes: First, obtain the probability density function of daily driving distance, the probability density function of electric vehicle arrival time, and the probability density function of electric vehicle daily departure time; Then, based on the obtained functions, a day-ahead scheduling model for electric vehicles is established. The specific steps are as follows: S2.1: Based on the electric vehicle's mileage and battery parameters, obtain the electric vehicle's state of charge upon arrival: in, For the first The initial charge state of the electric vehicle upon arrival; It is the first The charge state of the vehicle when it leaves; For the first The driving range of an electric vehicle; Electricity consumption per 100 kilometers for electric vehicles; It is the first The vehicle's battery capacity; S2.2: Day-ahead scheduling predicts the load curve for the current day based on the historical load of a certain region. A 24-hour day in this region is divided into 96 time periods, each lasting 15 minutes, for modeling and simulation. Based on the arrival and departure time distribution patterns of electric vehicles, 13:00 is the first time period, and so on. The base load for each time period is , =1, 2..., 96, No. The charging power of the vehicle is Assuming the charging station provides constant power charging to the electric vehicle, and only on the first... The time between the arrival and departure of the electric vehicles Optimize in the process. in, scheduling period Inner The charging power of an electric vehicle For the first The rated charging power of an electric vehicle These are 0-1 variables corresponding to the charging state; S2.3: Assume the first The load capacity of an electric vehicle charged in one cycle is There are a total of If there are 10 electric vehicles, then: Among them, the Power grid load during a given time period For electric vehicle load and basic load superposition, Regarding the charging process of electric vehicles, there are in, It refers to the charging efficiency of electric vehicles; It is the first The vehicle's battery capacity; scheduling period Inner The charging power of an electric vehicle; For time intervals; Indicates the first vehicle number The charge state at any given moment; Indicates the first vehicle number The charge state at any given moment; The objective function for calculating the day-ahead scheduling of electric vehicles includes both technical and economic indicators. The objective function transforms the technical indicator of the peak-valley difference of the power grid load into the economic indicator of minimizing the user's charging cost. The economic indicators refer to user charging costs; the technical indicators refer to the variance and peak-valley difference of the power grid load. The objective function is to minimize the charging cost for electric vehicle users, guiding them to charge during periods of low electricity prices and unifying the load across the time scale. In the formula, This indicates the economic indicators for the day-ahead dispatch of electric vehicles. for Electricity price for charging during certain time periods It is divided into 96 time periods. For the first The charging power of an electric vehicle; By obtaining The optimal solution is used to solve the objective function.

2. The day-ahead scheduling method for electric vehicles based on a changing economic objective, as described in claim 1, is characterized in that: The driving distance of electric vehicles follows a log-normal distribution, and the probability density function of daily driving distance is... for: In the formula, The probability density function representing the daily travel distance; Let be the standard deviation of daily mileage, and ; This is the expected value of the daily driving mileage, and ; Daily mileage, in units of .

3. The day-ahead scheduling method for electric vehicles based on a shifted economic objective, as described in claim 1, is characterized in that: The probability density function of the electric vehicle's arrival time is: in, Let be the probability density function of the arrival time of the electric vehicle. Let be the expected arrival time of the electric vehicle, and =17.6; Let be the standard deviation of the arrival time of the electric vehicle, and =3.

4.

4. The day-ahead scheduling method for electric vehicles based on a changing economic objective, as described in claim 1, is characterized in that: The probability density function for the daily departure time of the electric vehicle is: in, Let be the probability density function of the daily departure time of electric vehicles. Let be the expected value of the departure time of the electric vehicle, and =8.92; Let be the standard deviation of the departure time of the electric vehicle, and =3.

24.

5. The day-ahead scheduling method for electric vehicles based on a shifted economic objective, as described in claim 1, is characterized in that: The constraints of the day-ahead scheduling strategy for electric vehicles include: 1) Charge state constraints of electric vehicles in, and These represent the upper and lower limits of the electric vehicle battery's state of charge, respectively. The electric vehicle's state of charge; 2) User travel constraints in, It is the first The charge state of the vehicle when it leaves. It is the first The vehicle's departure time; 3) Charging station capacity constraints in, This is the rated power of the charging station. scheduling period Inner The charging power of an electric vehicle.

6. A day-ahead dispatching system for electric vehicles based on a shifted economic objective, characterized in that: This system is used to implement the scheduling method according to any one of claims 1-5, and includes an information acquisition module, an electric vehicle charging model establishment module, a scheduling plan analysis and judgment module, and a day-ahead scheduling plan optimization module. The information acquisition module is used to acquire the grid base load, N electric vehicles, and time-of-use electricity price; at the same time, it acquires Monte Carlo simulations of the electric vehicle driving range, charging power, and battery capacity. The electric vehicle charging model building module is used to calculate the initial charge state of electric vehicles using the acquired basic information, and to obtain the electric vehicle scheduling time by combining the Monte Carlo simulation of the arrival and departure times of electric vehicles. CPLEX is used to solve the day-ahead scheduling problem of electric vehicles. The scheduling plan analysis and judgment module is used to analyze whether the electric vehicle scheduling time obtained above exceeds the constraints; at the same time, it determines whether the number of electric vehicles calculated is greater than N. The day-ahead scheduling optimization module is used to calculate the objective function of day-ahead scheduling of electric vehicles and obtain an optimized day-ahead scheduling plan.

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