Electric vehicle ordered charging optimization scheduling method and device

Abstract

The invention discloses an electric vehicle ordered charging optimization scheduling method and device, and relates to the technical field of electric power scheduling, and the method comprises the following steps: calculating and obtaining a battery charge state at a trip end moment based on a probability distribution condition of daily mileage; according to the probability distribution condition of the initial charging moment, the initial charging moment is obtained through random extraction; on the basis of the charging power corresponding to each electric vehicle, the charging starting moment and the battery charge state at the travel ending moment, calculating to obtain a charging load corresponding to each electric vehicle; based on the charging loads corresponding to the electric vehicles, a charging load curve is obtained through accumulation; and carrying out charging optimization scheduling based on the charging load curve. Based on the charging time and the charging parameters of the electric vehicle, statistical analysis is performed, the charging load curve is obtained, and the charging load demand is predicted, so that a data basis is provided for charging optimization scheduling, and the operation stability of a power grid is ensured not to be influenced.

Description

Technical Field

[0001] This application relates to the technical field of power dispatching, and particularly relates to an optimized dispatching method and device for orderly charging of electric vehicles. Background Art

[0002] Currently, with the development of electric vehicles, the charging demand for electric vehicles is increasing day by day. To meet the charging demand of electric vehicles, service facilities such as charging stations are gradually put into use in urban construction, which also brings new challenges to the power load and power dispatching of urban power grids.

[0003] Therefore, to meet the actual needs, an optimized dispatching technology for orderly charging of electric vehicles is provided. Summary of the Invention

[0004] Aiming at the defects existing in the prior art, the purpose of this application is to provide an optimized dispatching method and device for orderly charging of electric vehicles. Based on the charging time and charging parameters of electric vehicles, statistical analysis is carried out to obtain a charging load curve and predict the charging load demand, so as to provide a data basis for optimized charging dispatching and ensure the stability of power grid operation is not affected.

[0005] To achieve the above purpose, the technical solution adopted by this application is:

[0006] In the first aspect, this application provides an optimized dispatching method for orderly charging of electric vehicles, and the method includes the following steps:

[0007] Based on the probability distribution of daily driving mileage, calculate the state of charge of the battery at the end of the trip.

[0008] According to the probability distribution of the starting charging time, randomly select the starting charging time.

[0009] Based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate the charging load corresponding to each electric vehicle.

[0010] Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load.

[0011] Based on the charging load curve, perform optimized charging dispatching.

[0012] On the basis of the above technical solution, the method is configured with a charging load accumulation formula, and the charging load accumulation formula is:

[0013] Wherein,

[0014] P iis the charging power of the $i$-th electric vehicle, and $t$ i has a value of 0 or 1. When $t$ i has a value of 1, it indicates that the corresponding electric vehicle is charging. When $t$ i has a value of 0, it indicates that the corresponding electric vehicle is not charging.

[0015] In a second aspect, the present application provides an optimized scheduling device for the orderly charging of electric vehicles. The device includes:

[0016] A state-of-charge acquisition module, which is used to calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage;

[0017] A starting charging time acquisition module, which is used to randomly extract the starting charging time according to the probability distribution of the starting charging time;

[0018] A charging load calculation module, which is used to calculate and obtain the charging load corresponding to each electric vehicle based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle;

[0019] A charging load curve acquisition module, which is used to accumulate and obtain a charging load curve based on the charging load corresponding to each electric vehicle. The abscissa of the charging load curve is time, and the ordinate is the charging load;

[0020] A charging optimization scheduling module, which is used to perform charging optimization scheduling based on the charging load curve.

[0021] Based on the above technical solution, the device is configured with a charging load accumulation formula, and the charging load accumulation formula is:

[0022] where

[0023] $P$ i is the charging power of the $i$-th electric vehicle, and $t$ i has a value of 0 or 1. When $t$ i has a value of 1, it indicates that the corresponding electric vehicle is charging. When $t$ i has a value of 0, it indicates that the corresponding electric vehicle is not charging.

[0024] Compared with the prior art, the advantages of the present application are as follows:

[0025] Based on the charging time and charging parameters of electric vehicles, the present application conducts statistical analysis to obtain a charging load curve and predict the charging load demand, thereby providing a data basis for charging optimization scheduling and ensuring that the stability of the power grid operation is not affected. Brief Description of the Drawings

[0026] To more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0027] Figure 1 It is a flowchart of the steps of the method for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application;

[0028] Figure 2 It is a flowchart of the technical principle of the method for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application;

[0029] Figure 3 It is a schematic diagram of the charging load prediction curve of 2000 electric vehicles in the method for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application;

[0030] Figure 4 It is a flowchart of the parallel selection genetic algorithm in the method for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application;

[0031] Figure 5 It is the orderly charging load curve in the method for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application;

[0032] Figure 6 It is a structural block diagram of the device for optimizing the orderly charging and dispatching of electric vehicles in the embodiments of the present application. Detailed implementation manners

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0034] The following will further elaborate on the embodiments of the present application in conjunction with the accompanying drawings.

[0035] The embodiments of the present application provide a method and device for optimizing the orderly charging and dispatching of electric vehicles. Based on the charging time and charging parameters of electric vehicles, statistical analysis is performed to obtain a charging load curve and predict the charging load demand, thereby providing a data basis for charging optimization and dispatching and ensuring that the stability of the power grid operation is not affected.

[0036] To achieve the above technical effects, the overall idea of the present application is as follows:

[0037] An optimized scheduling method for orderly charging of electric vehicles, the method comprising the following steps:

[0038] S1. Based on the probability distribution of the daily driving mileage, calculate the state of charge of the battery at the end of the trip;

[0039] S2. According to the probability distribution of the starting charging time, randomly extract the starting charging time;

[0040] S3. Based on the charging power, starting charging time and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate the charging load corresponding to each electric vehicle;

[0041] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0042] S5. Based on the charging load curve, perform optimized charging scheduling.

[0043] The following further details the embodiments of the present application with reference to the accompanying drawings.

[0044] In a first aspect, as shown in Figures 1 to 5 An optimized scheduling method for orderly charging of electric vehicles is provided in an embodiment of the present application, the method comprising the following steps:

[0045] S1. Based on the probability distribution of the daily driving mileage, calculate the state of charge of the battery at the end of the trip;

[0046] S2. According to the probability distribution of the starting charging time, randomly extract the starting charging time;

[0047] S3. Based on the charging power, starting charging time and state of charge of the battery at the end of the trip corresponding to each electric vehicle, calculate the charging load corresponding to each electric vehicle;

[0048] S4. Based on the charging loads corresponding to each electric vehicle, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0049] S5. Based on the charging load curve, perform optimized charging scheduling.

[0050] It should be noted that the technical solution of the embodiment of the present application can be applied to a charging station configured with multiple charging piles, a charging station management platform, a charging parking lot of a building or a power grid power supply management system of a certain area, and is used to perform power scheduling according to the situation of the electric vehicles for which it is responsible for charging, so as to ensure that the stability of the power grid operation in the affiliated area is not affected.

[0051] In the embodiments of the present application, statistical analysis is performed based on the charging time and charging parameters of electric vehicles to obtain a charging load curve and predict the charging load demand, thereby providing a data basis for charging optimization scheduling and ensuring the stability of the power grid operation is not affected.

[0052] Furthermore, the method is configured with a charging load accumulation formula, and the charging load accumulation formula is:

[0053] Among them,

[0054] P i is the charging power of the i-th electric vehicle, and the value of t i is 0 or 1. When the value of t i is 1, it means the corresponding electric vehicle is charging. When the value of t i is 0, it means the corresponding electric vehicle is not charging.

[0055] As shown in the accompanying drawings of the specification Figure 2 This is the technical principle flowchart of the embodiments of the present application. The technical solution of the embodiments of the present application mainly performs charging load prediction based on the Monte Carlo method. The Monte Carlo method is also known as the statistical simulation method, which obtains an approximate answer to the problem through random sampling, associates the problem to be solved with a probability model, and uses the computer as a tool for realizing random sampling or statistical simulation. The electric vehicle charging load prediction based on Monte Carlo sampling is based on Monte Carlo random sampling. According to the real traffic travel behavior data, by imitating the driving habits of electric vehicle owners, a model with random characteristics is established to predict the charging load demand of electric vehicles.

[0056] Assume that the charging behaviors of electric vehicles are independent of each other, thus meeting the characteristics of random distribution. The Monte Carlo method is used for random sampling to determine the random numbers in each step, thereby obtaining the charging load of a single electric vehicle, and then the total charging load when a large number of electric vehicles are charging is obtained by accumulation;

[0057] Then there is the following formula:

[0058] Among them,

[0059] P i is the charging power of the i-th electric vehicle, and the value of t i is 0 or 1. When the value of t i is 1, it means the corresponding electric vehicle is charging. When the value of t i is 0, it means the corresponding electric vehicle is not charging.

[0060] Specifically, the embodiments of the present application are also configured with an electric vehicle charging load prediction simulation process, which is as follows:

[0061] Select 2,000 household pure electric vehicles as samples for charging power prediction. In the embodiment of the present application, the slow charging power of the electric vehicle is selected as 3 kW, the charging efficiency is 0.9, the power consumption per kilometer of the electric vehicle is 0.5 kWh, and the electric vehicle starts charging immediately after completing the last return trip and charges at a constant power until it reaches the fully charged state. The prediction results of the unordered charging load of 2,000 electric vehicles are obtained through simulation by MATLAB software. As shown in the Figure 3 figure in the specification, which is the charging load prediction curve of 2,000 electric vehicles.

[0062] It can be seen from the charging load prediction curve of the electric vehicle that the daily charging load of the electric vehicle shows obvious peak-valley characteristics. In the time period from 16:00 to 24:00, a large number of electric vehicles are charging, but in the time period from 6:00 to 10:00, the charging load demand of the electric vehicle is relatively low, which is extremely closely related to the living and driving habits of the vehicle owners. The peak-valley difference of the unordered charging load curve of the electric vehicle is very large, which poses a hazard to the stable operation of the power system. If the charging of the electric vehicle is not optimized and scheduled, it will cause large power fluctuations in the power grid and affect the normal operation of other electrical equipment in the power system. Therefore, it is necessary to optimize and schedule the electric vehicle to reduce the impact on the power grid caused by its unordered charging.

[0063] Specifically, the embodiment of the present application is also configured with an electric vehicle optimization scheduling process based on the genetic algorithm, which is specifically as follows:

[0064] In the 1970s, the genetic algorithm was first proposed by John Holland in the United States. Influenced by Darwin's theory of biological evolution, the genetic algorithm realizes random search and optimization by imitating the evolution of biological populations. The genetic algorithm adopts the principle of survival of the fittest, converts the solution of the problem into phenomena such as gene crossover and mutation in the process of biological evolution through coding, and makes the individuals in the population evolve through gene mutation, so that the population can better adapt to the environment. According to the principle of survival of the fittest, the genetic algorithm finds the optimal solution through the fitness function, and only retains the relatively optimal solution as the basis for genetic operations to find a better solution, and so on in a cycle until the optimal solution is found.

[0065] The embodiment of the present application solves the optimization problem with multiple objectives and multiple constraints, and there may be a phenomenon of conflicting objectives during the operation. In order to achieve the purpose of simultaneously achieving the optimal solution for multiple objectives, the embodiment of the present application adopts the parallel selection method as the method for solving multiple objectives.

[0066] The basic idea of the parallel selection genetic algorithm is to equally divide all individuals in the population into different groups according to the number of objective functions. Different groups correspond to different objective functions. The individuals with high fitness for the objective function are selected from each group, and these selected individuals are combined into a new group. Operations such as crossover and mutation are performed in the new group to make it more adaptable to the objective function. This process is repeated until the optimal solution that meets all objective functions is generated. As shown in the accompanying drawings of the specification Figure 4 which is the flowchart of the parallel selection genetic algorithm.

[0067] Specifically, the embodiment of the present application is also configured with an electric vehicle charging optimization scheduling modeling process, which is as follows:

[0068] First, price analysis of time-of-use electricity price strategy:

[0069] Taking the actual daily load data of a certain regional power grid as a condition, it is assumed that a total of 2,000 electric vehicles participate in the optimized charging. Before optimization, the unordered charging electricity price of electric vehicles is 1 yuan / kWh, and the minimum charging electricity price cannot be lower than the comprehensive power generation cost electricity price of 0.25 yuan / kWh, and at the same time cannot be higher than 2 yuan / kWh. By referring to the reference literature, the price elasticity matrix of the peak-valley-flat periods of electric vehicles can be obtained, which is as follows:

[0070]

[0071] Second, modeling of orderly charging of electric vehicles:

[0072] Using the parallel selection method to solve, an optimized scheduling model of electric vehicle charging load with multiple objectives and multiple constraints is obtained. The result of the orderly charging load curve guided by the electricity price of electric vehicles is as shown in the accompanying drawings of the specification Figure 5 shown;

[0073] At the same time, the electricity prices of each period of valley-flat-peak after iterative optimization are obtained, as shown in Table 2:

[0074]

[0075] Table 2

[0076] Comparing the results before and after optimization, as shown in Table 3 below:

[0077]

[0078] Table 3

[0079] From the analysis of the load optimization results obtained from the simulation, it can be seen that due to the implementation of the time-of-use electricity price strategy, more electric vehicles are connected to the grid for charging during the valley period and the normal period, resulting in an increase in the load of the grid during the valley and normal periods; fewer electric vehicles are connected during the peak period, and the grid load decreases. This optimized scheduling strategy for electric vehicles, on the one hand, guides electric vehicle owners to change their original charging habits and switch to an orderly charging method, which plays a great role in peak shaving and valley filling. Moreover, the optimized grid load curve fits better with the wind and light output curves, reducing the occurrence of abandoned electricity, and at the same time maintaining the stability of the grid operation; on the other hand, users can incur less charging costs by choosing to charge their electric vehicles during the valley period and normal times. Using the time-of-use electricity price strategy to guide electric vehicle owners to participate in charging optimization scheduling safeguards the interests of multiple parties.

[0080] Second, as shown in Figure 6 the embodiments of the present application provide an optimized scheduling device for the orderly charging of electric vehicles, which includes:

[0081] A state of charge acquisition module for calculating and obtaining the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage;

[0082] A starting charging time acquisition module for randomly extracting the starting charging time according to the probability distribution of the starting charging time;

[0083] A charging load calculation module for calculating and obtaining the charging load corresponding to each electric vehicle based on the charging power, starting charging time, and state of charge of the battery at the end of the trip corresponding to each electric vehicle;

[0084] A charging load curve acquisition module for accumulating to obtain a charging load curve based on the charging load corresponding to each of the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load;

[0085] A charging optimization scheduling module for performing charging optimization scheduling based on the charging load curve.

[0086] It should be noted that the technical solutions of the embodiments of the present application can be applied to a charging station equipped with multiple charging piles, a charging station management platform, a charging parking lot of a building, or a power grid power supply management system in a certain area, and are used to perform power dispatching according to the situation of the electric vehicles under its charge responsibility to ensure that the stability of the power grid operation in the affiliated area is not affected.

[0087] In the embodiments of the present application, statistical analysis is performed based on the charging time and charging parameters of electric vehicles to obtain a charging load curve and predict the charging load demand, thereby providing a data basis for charging optimization scheduling and ensuring that the stability of the power grid operation is not affected.

[0088] Further, the device is configured with a charging load accumulation formula, and the charging load accumulation formula is:

[0089] Wherein,

[0090] P i is the charging power of the i-th electric vehicle, and the value of t i is 0 or 1. When the value of t i is 1, it means that the corresponding electric vehicle is charging. When the value of t i is 0, it means that the corresponding electric vehicle is not charging.

[0091] It should be noted that the electric vehicle orderly charging optimization scheduling device mentioned in the embodiments of the present application is similar in terms of technical problems, technical solutions, and technical effects to the technical principle of the electric vehicle orderly charging optimization scheduling method mentioned in the first aspect, and will not be elaborated here.

[0092] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application. Unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0093] It should be noted that in the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article, or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or device including the said element.

[0094] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined in the embodiments of the present application can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown in the embodiments of the present application, but rather to the broadest scope consistent with the principles and novel features claimed in the embodiments of the present application.

Claims

1. An optimized scheduling method for the orderly charging of electric vehicles, characterized in that, The method includes the following steps: Based on the probability distribution of the daily driving mileage, calculate and obtain the state of charge of the battery at the end of the trip; According to the probability distribution of the starting charging time, randomly extract and obtain the starting charging time; Based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip, calculate and obtain the charging load corresponding to each electric vehicle; Based on the charging loads corresponding to each of the electric vehicles, accumulate to obtain a charging load curve, where the abscissa of the charging load curve is time and the ordinate is the charging load; Based on the charging load curve, perform charging optimization scheduling.

2. The optimized scheduling method for the orderly charging of electric vehicles according to claim 1, wherein The method is configured with a charging load accumulation formula, and the charging load accumulation formula is: Among them, P i is the charging power of the i-th electric vehicle, t i has a value of 0 or 1. When t i has a value of 1, it means the corresponding electric vehicle is charging. When t i has a value of 0, it means the corresponding electric vehicle is not charging.

3. An optimized scheduling device for orderly charging of electric vehicles, characterized in that, The device includes: A state of charge acquisition module, which is used to calculate and obtain the state of charge of the battery at the end of the trip based on the probability distribution of the daily driving mileage; A starting charging time acquisition module, which is used to randomly extract and obtain the starting charging time according to the probability distribution of the starting charging time; A charging load calculation module, which is used to calculate and obtain the charging load corresponding to each electric vehicle based on the charging power corresponding to each electric vehicle, the starting charging time, and the state of charge of the battery at the end of the trip; A charging load curve acquisition module, which is used to accumulate to obtain a charging load curve based on the charging loads corresponding to each of the electric vehicles, where the abscissa of the charging load curve is time and the ordinate is the charging load; A charging optimization scheduling module, which is used to perform charging optimization scheduling based on the charging load curve.

4. The optimized scheduling device for orderly charging of electric vehicles according to claim 3, wherein, The device is configured with a charging load accumulation formula, and the charging load accumulation formula is: Among them, P i is the charging power of the i-th electric vehicle, t i has a value of 0 or 1. When t i has a value of 1, it means the corresponding electric vehicle is charging. When t i has a value of 0, it means the corresponding electric vehicle is not charging.

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