CN117151368A - Power conversion scheduling method and device, electronic equipment and power conversion scheduling system - Google Patents

Power conversion scheduling method and device, electronic equipment and power conversion scheduling system Download PDF

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CN117151368A
CN117151368A CN202310897497.0A CN202310897497A CN117151368A CN 117151368 A CN117151368 A CN 117151368A CN 202310897497 A CN202310897497 A CN 202310897497A CN 117151368 A CN117151368 A CN 117151368A
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vehicle
power conversion
charging
power
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向杜兵
王健健
寻其锋
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Sany Group Co Ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
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Abstract

The application relates to a power conversion scheduling method, a device, electronic equipment and a power conversion scheduling system, wherein the method comprises the following steps: determining the current arrival time of each target vehicle and the charging duration of each battery; the target vehicle is a vehicle needing to change electricity; determining the total charge amount of all batteries in each period based on the current time and the charge duration of each battery; taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; carrying out iterative solution on the two-stage robust optimization model by adopting a column-row generation algorithm, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power change result comprises a target battery matched with a target vehicle; the second phase battery change result includes a charging schedule of the target battery. Therefore, the minimum electricity cost and the minimum waiting time are comprehensively considered, the accuracy of the electricity changing scheduling decision is greatly improved, and the operation efficiency is improved.

Description

Power conversion scheduling method and device, electronic equipment and power conversion scheduling system
Technical Field
The application relates to the technical field of engineering machinery, in particular to a power conversion scheduling method, a power conversion scheduling device, electronic equipment and a power conversion scheduling system.
Background
With the development of battery technology and the popularization of electric vehicles, the use of electric vehicles for operation in the scenes of ports, mines and the like is becoming an increasingly popular choice. At present, the battery life mileage and the electricity price cost are important factors affecting the industry cost and the operation service level, and in order to reduce a plurality of problems caused by the battery life mileage, people adopt a power exchanging mode more.
However, power conversion scheduling and power conversion operation are one of challenges facing the electric automobile industry, and the traditional power conversion mode relies on manual experience seriously, so that problems of improper scheduling, low operation efficiency and the like are easy to occur.
Disclosure of Invention
In view of the above, the present application is directed to providing a power conversion scheduling method, apparatus, electronic device, and power conversion scheduling system, which can effectively improve accuracy of power conversion scheduling decision and improve operation efficiency.
The first aspect of the application provides a power conversion scheduling method which is applied to a power conversion scheduling system, wherein the power conversion scheduling system comprises power conversion equipment, and the power conversion equipment is provided with a plurality of batteries; the method comprises the following steps:
determining the current arrival time of each target vehicle and the charging duration of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required by the battery to charge from the current electric quantity to the preset electric quantity;
Determining the total charge amount of all batteries in each period based on the current time and the charge duration of each battery;
taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all the target vehicles; the power-on waiting time is the time for waiting for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time of the target vehicle; the preset constraint condition comprises: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on waiting time length does not exceed the preset waiting time length;
performing iterative solution on the two-stage robust optimization model by adopting a column-row generation algorithm, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power conversion result comprises a target battery matched with the target vehicle; the second-stage battery replacement result comprises a charging plan of the target battery.
Optionally, the determining the arrival time of each target vehicle includes:
acquiring vehicle driving data of each vehicle in a current scene, and determining the arrival time of each vehicle by combining a pre-constructed vehicle power-on arrival time model; the vehicle is a vehicle to be replaced;
and determining the vehicles with the arrival time length meeting the preset time length as the target vehicles, and determining the arrival time of each target vehicle according to the arrival time length of each vehicle.
Optionally, after the vehicle whose arrival time period satisfies the preset time period is determined as the target vehicle, the method further includes:
and generating power-change indicating information and sending the information to a driver of the target vehicle so as to indicate the current driving vehicle of the driver to need to timely change power.
Optionally, the method for constructing the vehicle power-on-station time duration model comprises the following steps:
acquiring first sample data and constructing a first sample data set; the first sample data comprise arrival time length and corresponding vehicle running data in the vehicle power change process;
and constructing a duration prediction model based on a machine learning regression prediction and deep learning method, and training the duration prediction model by using sample data in the sample data set to obtain the vehicle power-on-station duration model.
Optionally, the determining the charging duration of each battery includes:
and acquiring charging basic data of each battery, and determining the charging duration of each battery by combining a pre-constructed battery charging duration estimation model.
Optionally, the method for constructing the battery charging duration estimation model includes:
acquiring second sample data and constructing a second sample data set; the second sample data comprise charging duration and corresponding charging basic data in the battery charging process;
and based on a deep learning method, performing model training by using second sample data in the second sample data set to obtain the battery charging duration estimation model.
Optionally, the method further comprises:
and sending the first-stage power conversion result and the second-stage power conversion result to the power conversion equipment so that the power conversion equipment executes a corresponding power conversion strategy according to the first-stage power conversion result and the second-stage power conversion result.
A second aspect of the present application provides a power conversion scheduling apparatus, including:
the first determining module is used for determining the arrival time of each target vehicle and the charging time of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required by the battery to charge from the current electric quantity to the preset electric quantity;
A second determining module for determining a total charge amount of all batteries in each period based on the current time and a charge duration of each battery;
the construction module is used for constructing a two-stage robust optimization model based on preset constraint conditions by taking the minimized charging cost and the vehicle battery-replacement average waiting time length as objective functions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all the target vehicles; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; the preset constraint condition comprises: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on waiting time length does not exceed the preset waiting time length;
the iteration and determination module is used for carrying out iteration solution on the two-stage robust optimization model by adopting a column-row generation algorithm to determine a first-stage power conversion result and a second-stage power conversion result; the first-stage power conversion result comprises a target battery matched with the target vehicle; the second-stage battery replacement result comprises a charging plan of the target battery.
A third aspect of the present application provides an electronic apparatus comprising:
a processor, and a memory coupled to the processor;
the memory is used for storing a computer program;
the processor is configured to invoke and execute the computer program in the memory to perform the power-save scheduling method according to the first aspect of the present application.
A fourth aspect of the application provides a power conversion scheduling system comprising a power conversion device and an electronic device according to the third aspect of the application,
the battery exchange device includes a plurality of batteries.
In the scheme of the application, the arrival time of each current target vehicle and the charging time of each battery can be determined, wherein the target vehicle is a vehicle needing to be subjected to power conversion, and the charging time of the battery is the time required by the battery to be charged from the current electric quantity to the preset electric quantity; further, based on the current time and the charging time of each battery, determining the total charge quantity of all the batteries in each period; taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; the charging cost is the sum of the total charge quantity of all batteries in each period and the power price of the corresponding period; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; then, adopting a row-column generation algorithm to carry out iterative solution on the two-stage robust optimization model, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power change result comprises a target battery matched with a target vehicle; the second-stage power conversion result comprises a charging plan of the target point eating. In this way, according to the dynamic change of the electricity price at different time periods and the uncertain demand generated by the arrival of the vehicles, a two-stage robust optimization model is constructed, the minimum electricity cost and the minimum waiting time are comprehensively considered, and the two-stage robust optimization model is optimized by using a rank generation algorithm, so that an optimized power change scheduling decision, namely a battery which is more matched with the target vehicle and a charging plan of the battery, is obtained, the accuracy of the power change scheduling decision is greatly improved, and the operation efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a power-switching scheduling method according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a power conversion scheduling device according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides a power conversion scheduling method, which can be applied to a power conversion scheduling system, wherein the power conversion scheduling system at least comprises power conversion equipment, and the power conversion equipment is provided with a plurality of batteries.
When the method is implemented, the power conversion equipment can be arranged at a power conversion target position, and the power conversion target position can be a power conversion station and is used for providing a battery required by power conversion for a vehicle needing power conversion under the current application scene.
It should be understood that the battery replacing device further includes a plurality of chargers, and the plurality of chargers are used for charging the plurality of batteries.
As shown in fig. 1, the power-saving scheduling method at least includes the following steps:
s101, determining the arrival time of each current target vehicle and the charging duration of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required for the battery to charge from the current electric quantity to the preset electric quantity.
The arrival time of the target vehicle refers to the time when the target vehicle arrives at the battery-change target position from the current position. In use, the target location may be a power exchange station, and the target vehicle may only be able to utilize a power exchange device located in the power exchange station to exchange power.
The preset electric quantity can be an electric quantity which can meet the electricity consumption requirement of a user in a battery which is manually set. For example, the preset amount of electricity may be 95% of the remaining amount (State of Charge, SOC). In implementation, the preset electric quantity can be set according to actual requirements, and is not limited herein.
Since the battery in the battery exchange device may be the battery that has just been exchanged from the vehicle, there is a case where the battery in the battery exchange device is low in power and needs to be charged, and the battery at this time is in an unavailable state. For a battery to be charged, the charging time is the time from the current electric quantity to the preset electric quantity; and for the battery with the current electric quantity reaching the preset electric quantity in the battery replacement equipment, the battery is in a state of being available at any time, and the charging time is zero.
S102, determining the total charge quantity of all batteries in each period based on the current time and the charge duration of each battery.
Since there may be different charging duration of each battery, for example, the current time is 9:00 am, the charging duration of battery a is 2 hours, the charging duration of battery B is 4 hours, and the charging duration of battery C is 9 hours, then there may be a case of battery charging in each period between 9:00 am and 6:00 pm, where battery a, battery B and battery C are simultaneously charging, 11:00 am and 1:00 pm, battery B and battery C are simultaneously charging, 1:00 pm and 6:00 pm, and battery C is charging, so the total charge amount of each period may also be different. For example, in the period of 9:00-11:00 am, the total charge amount of all the batteries is the sum of the charge amounts of battery a, battery B and battery C in this period; the total charge of all cells is the sum of the charge of cell B and cell C during this period, 11:00 a.m. to 1:00 a.m.. Therefore, the total charge quantity of all batteries in each period can be determined, and a data foundation is laid for the calculation of the subsequent charge cost.
S103, taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all target vehicles; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; the preset constraint conditions comprise: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on-off waiting time length does not exceed the preset waiting time length.
The electric quantity of the battery reaches the preset electric quantity, which means that the electric quantity of the battery is larger than or equal to the preset electric quantity. The preset charging duration and the preset waiting duration can be set according to actual requirements, and are not limited herein.
In implementation, the target battery corresponding to the target vehicle is a battery that can be used as a battery to be replaced of the target vehicle in the battery of the battery replacement device, that is, the target vehicle can use the corresponding matched target battery as a new battery.
Since the electricity prices are different in each period, charging is performed in different periods, and the resulting charging costs are also different. In implementation, the minimum charging cost can be used as a first priority optimization target, the minimum vehicle power-on average waiting time is used as a second priority optimization target, the updating decision is optimized, and whether the new decision meets the preset constraint condition is judged. After a new decision meeting the preset constraint condition is obtained, the optimized charging cost and the vehicle power-change average waiting time length can be calculated. Therefore, according to the dynamic change of the electricity price and the uncertain requirement generated by the arrival of the vehicle, the optimal charging scheme is automatically established, and the charging cost is minimized under the condition of optimal service level.
S104, carrying out iterative solution on the two-stage robust optimization model by adopting a column-row generation algorithm, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power change result comprises a target battery matched with a target vehicle; the second phase battery change result includes a charging schedule of the target battery.
For example, the battery in the battery replacing device includes a battery a, a battery B, a battery C and a battery D, after the iterative solution is performed, the target battery matched with the vehicle a can be determined to be the battery C, and the target battery matched with the vehicle B is determined to be the battery D, so that the first-stage battery replacing result is a matching relationship between the vehicle a and the battery C and a matching relationship between the vehicle B and the battery D; the second phase of battery change results in a charging schedule for battery C and a charging schedule for battery D.
Wherein, the charging schedule of the target battery at least can comprise: a time when the target battery starts charging and a time when the target battery stops charging.
In implementation, the row-generation algorithm may be executed to determine the matching relationship between the battery and the target vehicle in the first stage and determine the charging schedule of each battery in the second stage. The first stage is a main Problem (Master Problem), the main components are decision variables (matching of vehicles and batteries) of the first stage and constraint (preset constraint condition) related to the decision variables only, and a cutting plane (Cut) returned by a second stage sub-Problem (Subprobem), and the decision variables are solved by constructing an integer programming model and calling a mathematical programming solver, so that the battery matched with each target vehicle under the current condition is finally determined.
The second stage is a sub-problem (sub-bprobtem), mainly comprising decision variables (charging plan of each battery) and uncertainty variables (vehicle arrival time) of the second stage, aiming at identifying the value of the arrival time of the target vehicle most likely to be the worst case, adding the corresponding decision variables and constraints into the main problem, in the solving process of the stage, taking the matching relation between the battery of the first stage and the target vehicle as input, converting the nonlinear mixed integer programming model into the linear mixed integer programming model by a linearization method, and calling a mathematical programming solver to solve. In the solving process, the objective function value of the charge time length and the charge cost and the average charge waiting time length are required to be continuously adjusted and calculated, and finally, the optimal charge plan of each objective battery is obtained under the condition that the matching relation between the objective battery and the objective vehicle is determined. The two stages are continuously iterated, the variables and the constraints of the main problem are continuously increased, the parameters provided for the sub problems are also changed, the upper bound and the lower bound are continuously improved until the algorithm converges, and finally, the optimal solution of the whole two-stage robust optimization model is obtained, and the solution has optimal charging cost and vehicle power-on average waiting time.
In the specific implementation, the specific implementation manner of performing iterative solution on the two-stage robust optimization model by adopting the column-row generation algorithm can refer to the related art, and is not repeated here.
In this embodiment, first, the arrival time of each current target vehicle and the charging duration of each battery may be determined, where the target vehicle is a vehicle needing to be charged, and the charging duration of the battery is the duration required for charging the battery from the current electric quantity to the preset electric quantity; further, based on the current time and the charging time of each battery, determining the total charge quantity of all the batteries in each period; taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; the charging cost is the sum of the total charge quantity of all batteries in each period and the power price of the corresponding period; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; then, adopting a row-column generation algorithm to carry out iterative solution on the two-stage robust optimization model, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power change result comprises a target battery matched with a target vehicle; the second-stage power conversion result comprises a charging plan of the target point eating. In this way, according to the dynamic change of the electricity price at different time periods and the uncertain demand generated by the arrival of the vehicles, a two-stage robust optimization model is constructed, the minimum electricity cost and the minimum waiting time are comprehensively considered, and the two-stage robust optimization model is optimized by using a rank generation algorithm, so that an optimized power change scheduling decision, namely a battery which is more matched with the target vehicle and a charging plan of the battery, is obtained, the accuracy of the power change scheduling decision is greatly improved, and the operation efficiency is improved.
In some embodiments of the present application, when determining the arrival time of each current target vehicle, vehicle driving data of each vehicle in the current scene may be first obtained, and the arrival time of each vehicle may be determined by combining with a pre-built vehicle power-up arrival time model; wherein the vehicle is a vehicle to be replaced; and further determining the vehicles with the arrival time length meeting the preset time length as target vehicles, and determining the arrival time of each target vehicle according to the arrival time length of each vehicle.
Wherein the vehicle travel data may include: static data such as vehicle identification information, loading and unloading station information, basic information of a vehicle assembled battery, vehicle operation statistics data and the like, and dynamic data such as current position information of the vehicle, SOC information of the vehicle assembled battery, driving route information, loading information, queuing information at the current power conversion equipment and the like. The vehicle identification information may be a code of the vehicle for identifying an identity of the vehicle. The vehicle-mounted battery basic information refers to battery attribute information. The vehicle operation statistical data refers to statistical information such as the recent operation time of the vehicle on a certain road section, the number of times the vehicle performs power conversion at the power conversion equipment, time and the like.
When the method is implemented, the vehicle running data of each vehicle in the current scene can be respectively input into a pre-built power-change arrival time model, and an output result is obtained, namely the arrival time of the vehicle. The arrival time of the vehicle refers to the time required for the vehicle to arrive at the power exchange station from the current position.
After the arrival time of each vehicle is obtained, the arrival time of each target vehicle can be determined according to the arrival time of each vehicle, and then the arrival time of each target vehicle is determined according to the current time and the arrival time of each target vehicle. Therefore, the number of vehicles reaching a certain time period in the future of the power exchange station can be counted, and a better decision basis is provided for power exchange scheduling of subsequent vehicles.
In implementation, a vehicle meeting the preset duration can be screened out from the arrival duration of each vehicle to serve as a target object of current dispatching. Or screening out a preset number of vehicles with shorter duration from the arrival duration sequencing of the vehicles as target vehicles. The target vehicles are screened out according to the stress degree of the requirements, so that the electricity demand of the vehicles can be relieved to a greater extent, and a basis is provided for rationalizing the electricity exchange sequence of the vehicles.
In some embodiments of the present application, after determining, as the target vehicle, the vehicle whose arrival time period satisfies the preset time period, the power-saving scheduling method may further include: and generating power change indicating information and sending the information to a driver of the target vehicle so as to indicate the driver that the vehicle currently driven needs to be timely changed.
The power conversion information may be voice information or text information.
In the implementation process, the power-change indicating information can be sent to the dispatching center, and better decision support is never provided for the dispatching center.
In some embodiments of the present application, in order to further improve accuracy of a arrival time of a vehicle, the method for constructing a vehicle power-on arrival time model may include: acquiring first sample data and constructing a first sample data set; the first sample data comprise the arrival time length and corresponding vehicle running data in the vehicle power change process; and constructing a duration prediction model based on a machine learning regression prediction and deep learning method, and training the duration prediction model by using sample data in a sample data set to obtain a vehicle power-on-station duration model.
In practice, a first sample data set may be acquired first. After the first sample data set is acquired, data analysis mining and feature construction can be performed on each sample data set.
Specifically, the data analysis mining is to mine potential information through historical data, such as statistics of the average arrival time length through vehicle driving data, generation of vehicle driving routes through coordinates, and the like.
Feature construction mainly includes vehicle portrayal features, line and site features, dynamic features, and other features. The vehicle portrait features comprise power consumption of the vehicle in different states, average power consumption of different batteries and the rest times of a driver; the characteristics of the line and the station comprise average time consumption of different vehicles of the same line, line segmentation distance, queuing conditions of different stations at different time periods and the like; the dynamic characteristics comprise real-time coordinates of the vehicle, real-time electric quantity SOC of the vehicle and real-time distance of the vehicle from a station; other characteristics include grade of travel route, speed limit, current date, weather, etc.
And further, based on the constructed characteristics, constructing a vehicle power-on-station time period model.
Specifically, a model layer can adopt a LightGBM, XGBoost, catBoost machine learning regression prediction and deep eta deep learning model, features of vehicle driving data in a vehicle power-on process are taken as input, corresponding arrival time is taken as output for training, and a first sample data set can be utilized for training to obtain a vehicle power-on arrival time model.
In some embodiments of the present application, when determining the charging duration of each battery, charging base data of each battery may be obtained, and the charging duration of each battery may be determined in combination with a pre-built battery charging duration prediction model.
Wherein, the charging basic data of the battery may include: battery charge data (SOC time series and battery power consumption time series) of the battery every one minute during battery charging, and data such as charging power of a charger corresponding to the battery, battery capacity, and the like.
In order to further obtain a more accurate battery charging duration, the method for constructing the battery charging duration estimation model may include: acquiring second sample data and constructing a second sample data set; the second sample data comprise charging duration and corresponding charging basic data in the battery charging process; based on the deep learning method, model training is carried out by using second sample data in the second sample data set, and a battery charging duration estimated model is obtained.
Specifically, after the second sample data set is obtained, feature construction may be performed on charging base data in each second sample data, for example, the average duration of 10% of SOC increase in different SOC states, the SOC increase rate in different battery chargers, and so on.
And then, based on the constructed characteristics, constructing a battery charging duration estimation model. The model can adopt a deep AR time sequence model, takes the characteristics of charging basic data as input, takes corresponding charging time length as output, trains the model, and can obtain a battery charging time length estimation model.
Of course, the present application is not limited to this, and in some other embodiments, the above-constructed features may be used as an input of the model, and output as a predicted battery SOC value at the next time. The model adopts a deep AR time sequence model, and a battery charging efficiency time sequence model is obtained through training. According to the battery charging efficiency time sequence model, the electric quantity of the battery after being charged for a certain period of time from the current SOC value can be predicted, so that the charging duration from the current electric quantity to the preset electric quantity is calculated.
In some embodiments of the present application, the power-saving scheduling method may further include: and sending the first-stage power conversion result and the second-stage power conversion result to the power conversion equipment so that the power conversion equipment executes a corresponding power conversion strategy according to the first-stage power conversion result and the second-stage power conversion result.
As an optional implementation of the disclosure, an embodiment of the present application further provides a power conversion scheduling device, as shown in fig. 2, where the device at least includes: a first determining module 201, configured to determine a current arrival time of each target vehicle and a charging duration of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required by the battery to charge from the current electric quantity to the preset electric quantity; a second determining module 202 for determining a total charge amount of all batteries in each period based on the current time and the charge duration of each battery; the construction module 203 is configured to construct a two-stage robust optimization model based on a preset constraint condition with minimized charging cost and vehicle battery-replacement average waiting time length as objective functions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all target vehicles; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; the preset constraint conditions comprise: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on waiting time length does not exceed the preset waiting time length; the iteration and determination module 204 is configured to perform iteration solution on the two-stage robust optimization model by using a column-row generation algorithm, and determine a first-stage power conversion result and a second-stage power conversion result; the first-stage power change result comprises a target battery matched with a target vehicle; the second phase battery change result includes a charging schedule of the target battery.
Optionally, in determining the arrival time of each target vehicle, the first determining module 201 may specifically be configured to: acquiring vehicle driving data of each vehicle in a current scene, and determining the arrival time of each vehicle by combining a pre-constructed vehicle power-on arrival time model; and determining the vehicles with the arrival time length meeting the preset time length as target vehicles, and determining the arrival time of each target vehicle according to the arrival time length of each vehicle.
Optionally, the power conversion scheduling device may further include a prompt module, where the prompt module may be configured to: and generating power change indicating information and sending the information to a driver of the target vehicle so as to indicate the driver that the vehicle currently driven needs to be timely changed.
Optionally, the power conversion scheduling device may further include a first model building module, where the first model building module may be configured to: acquiring first sample data and constructing a first sample data set; the first sample data comprise the arrival time length and corresponding vehicle running data in the vehicle power change process; and constructing a duration prediction model based on a machine learning regression prediction and deep learning method, and training the duration prediction model by using sample data in a sample data set to obtain a vehicle power-on-station duration model.
Optionally, in determining the charging duration of each battery, the second determining module 202 may specifically be configured to: and acquiring charging basic data of each battery, and determining the charging duration of each battery by combining a pre-constructed battery charging duration estimation model.
Optionally, the power conversion scheduling device may further include a second model building module, where the second model building module may be configured to: acquiring second sample data and constructing a second sample data set; the second sample data comprise charging duration and corresponding charging basic data in the battery charging process; based on the deep learning method, model training is carried out by using second sample data in the second sample data set, and a battery charging duration estimated model is obtained.
Optionally, the power conversion scheduling device may further include an execution module, where the execution module may be configured to: and sending the first-stage power conversion result and the second-stage power conversion result to the power conversion equipment so that the power conversion equipment executes a corresponding power conversion strategy according to the first-stage power conversion result and the second-stage power conversion result.
The specific implementation manner of the power conversion scheduling device provided by the embodiment of the present application may refer to the implementation manner of the power conversion scheduling method described in any of the foregoing embodiments, and will not be described herein again.
An embodiment of the present application further provides an electronic device, as shown in fig. 3, where the electronic device may include: a memory 301 and a processor 302; wherein the memory 301 is connected to the processor 302 and is used for storing a program; and a processor 302, configured to implement the power-saving scheduling method disclosed in any of the foregoing embodiments by running a program stored in the memory 301.
Specifically, the electronic device may further include: a bus, a communication interface 303, an input device 304 and an output device 305.
The processor 302, the memory 301, the communication interface 303, the input device 304 and the output device 305 are connected to each other by a bus. Wherein:
a bus may comprise a path that communicates information between components of a computer system.
The processor 302 may be a general-purpose processor, such as a general-purpose Central Processing Unit (CPU), microprocessor, etc., or may be an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs in accordance with aspects of the present application. But may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Processor 302 may include a host processor and may also include a baseband chip, modem, etc.
The memory 301 stores programs for implementing the technical scheme of the present application, and may also store an operating system and other critical services. In particular, the program may include program code including computer-operating instructions. More specifically, the memory 301 may include read-only memory (ROM), other types of static storage devices that may store static information and instructions, random access memory (random access memory, RAM), other types of dynamic storage devices that may store information and instructions, disk storage, flash, and the like.
The input device 304 may include means for receiving data and information entered by a user, such as a keyboard, mouse, camera, scanner, light pen, voice input device, touch screen, pedometer, or gravity sensor, among others.
The output device 305 may include means, such as a display screen, printer, speakers, etc., that allow information to be output to a user.
The communication interface 303 may include devices using any transceiver or the like to communicate with other devices or communication networks, such as ethernet, radio Access Network (RAN), wireless Local Area Network (WLAN), etc.
The processor 302 executes the program stored in the memory 301 and invokes other devices, which can be used to implement the steps of the power-saving scheduling method provided in the above embodiment of the present application.
Embodiments of the present application also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a computer, causes the computer to perform the power-change scheduling method of any of the above embodiments.
Embodiments of the present application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the power-on scheduling method according to any of the above embodiments.
The embodiment of the application also provides a power conversion scheduling system which can comprise power conversion equipment and the electronic equipment according to any embodiment. The battery replacing device comprises a plurality of batteries.
It will be appreciated that the specific examples herein are intended only to assist those skilled in the art in better understanding the embodiments of the present disclosure and are not intended to limit the scope of the present application.
It should be understood that, in various embodiments of the present disclosure, the sequence number of each process does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
It will be appreciated that the various embodiments described in this specification may be implemented either alone or in combination, and are not limited in this regard.
Unless defined otherwise, all technical and scientific terms used in the embodiments of this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this specification belongs. The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to limit the scope of the description. The term "and/or" as used in this specification includes any and all combinations of one or more of the associated listed items. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It will be appreciated that the processor of the embodiments of the present description may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be implemented by integrated logic circuits of hardware in a processor or instructions in software form. The processor may be a general purpose processor, a Digital signal processor (Digital SignalProcessor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The methods, steps and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present specification may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
It will be appreciated that the memory in the embodiments of this specification may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), or a flash memory, among others. The volatile memory may be Random Access Memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present specification.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described system, apparatus and unit may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present specification may be integrated into one processing unit, each unit may exist alone physically, or two or more units may be integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present specification may be essentially or portions contributing to the prior art or portions of the technical solutions may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk, or an optical disk, etc.
The foregoing is merely specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope disclosed in the present disclosure, and should be covered by the scope of the present disclosure. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. The power conversion scheduling method is characterized by being applied to a power conversion scheduling system, wherein the power conversion scheduling system comprises power conversion equipment, and the power conversion equipment is provided with a plurality of batteries; the method comprises the following steps:
determining the current arrival time of each target vehicle and the charging duration of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required by the battery to charge from the current electric quantity to the preset electric quantity;
determining the total charge amount of all batteries in each period based on the current time and the charge duration of each battery;
taking the minimized charging cost and the vehicle power-on average waiting time as objective functions, and constructing a two-stage robust optimization model based on preset constraint conditions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all the target vehicles; the power-on waiting time is the time for waiting for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time of the target vehicle; the preset constraint condition comprises: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on waiting time length does not exceed the preset waiting time length;
Performing iterative solution on the two-stage robust optimization model by adopting a column-row generation algorithm, and determining a first-stage power conversion result and a second-stage power conversion result; the first-stage power conversion result comprises a target battery matched with the target vehicle; the second-stage battery replacement result comprises a charging plan of the target battery.
2. The method of claim 1, wherein the determining the current arrival time of each target vehicle comprises:
acquiring vehicle driving data of each vehicle in a current scene, and determining the arrival time of each vehicle by combining a pre-constructed vehicle power-on arrival time model; the vehicle is a vehicle to be replaced;
and determining the vehicles with the arrival time length meeting the preset time length as the target vehicles, and determining the arrival time of each target vehicle according to the arrival time length of each vehicle.
3. The method according to claim 2, wherein after the vehicle whose arrival time period satisfies a preset time period is determined as the target vehicle, the method further comprises:
and generating power-change indicating information and sending the information to a driver of the target vehicle so as to indicate the current driving vehicle of the driver to need to timely change power.
4. The method of claim 2, wherein the vehicle power-on-station duration model construction method comprises:
acquiring first sample data and constructing a first sample data set; the first sample data comprise arrival time length and corresponding vehicle running data in the vehicle power change process;
and constructing a duration prediction model based on a machine learning regression prediction and deep learning method, and training the duration prediction model by using sample data in the sample data set to obtain the vehicle power-on-station duration model.
5. The method of claim 1, wherein the determining the charge duration of each battery comprises:
and acquiring charging basic data of each battery, and determining the charging duration of each battery by combining a pre-constructed battery charging duration estimation model.
6. The method of claim 5, wherein the method for constructing the battery charge duration estimation model comprises:
acquiring second sample data and constructing a second sample data set; the second sample data comprise charging duration and corresponding charging basic data in the battery charging process;
and based on a deep learning method, performing model training by using second sample data in the second sample data set to obtain the battery charging duration estimation model.
7. The method according to claim 1, wherein the method further comprises:
and sending the first-stage power conversion result and the second-stage power conversion result to the power conversion equipment so that the power conversion equipment executes a corresponding power conversion strategy according to the first-stage power conversion result and the second-stage power conversion result.
8. A power conversion scheduling device, characterized by comprising:
the first determining module is used for determining the arrival time of each target vehicle and the charging time of each battery; the target vehicle is a vehicle needing to change electricity; the charging time of the battery is the time required by the battery to charge from the current electric quantity to the preset electric quantity;
a second determining module for determining a total charge amount of all batteries in each period based on the current time and a charge duration of each battery;
the construction module is used for constructing a two-stage robust optimization model based on preset constraint conditions by taking the minimized charging cost and the vehicle battery-replacement average waiting time length as objective functions; the charging cost is the sum of the products of the total charge amount of all batteries in each period and the electricity price of the corresponding period; the vehicle power-on average waiting time is the average value of the power-on waiting time of all the target vehicles; the power-on waiting time is the time when the target vehicle waits for the corresponding matched target battery to be charged to the preset electric quantity from the arrival time; the preset constraint condition comprises: the electric quantity of the battery reaches the preset electric quantity, the continuous charging time length meets the preset charging time length, and the vehicle power-on waiting time length does not exceed the preset waiting time length;
The iteration and determination module is used for carrying out iteration solution on the two-stage robust optimization model by adopting a column-row generation algorithm to determine a first-stage power conversion result and a second-stage power conversion result; the first-stage power conversion result comprises a target battery matched with the target vehicle; the second-stage battery replacement result comprises a charging plan of the target battery.
9. An electronic device, comprising:
a processor, and a memory coupled to the processor;
the memory is used for storing a computer program;
the processor is configured to invoke and execute the computer program in the memory to perform the power-change scheduling method of any of claims 1-7.
10. A power-exchanging dispatching system is characterized by comprising power-exchanging equipment and the electronic equipment as claimed in claim 9,
the battery exchange device includes a plurality of batteries.
CN202310897497.0A 2023-07-20 2023-07-20 Power conversion scheduling method and device, electronic equipment and power conversion scheduling system Pending CN117151368A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117656922A (en) * 2023-12-26 2024-03-08 三一重型装备有限公司 Battery replacement control method and device, electronic equipment and vehicle
CN118569621A (en) * 2024-08-05 2024-08-30 深圳市菲尼基科技有限公司 Information processing method of power exchange station and related equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117656922A (en) * 2023-12-26 2024-03-08 三一重型装备有限公司 Battery replacement control method and device, electronic equipment and vehicle
CN118569621A (en) * 2024-08-05 2024-08-30 深圳市菲尼基科技有限公司 Information processing method of power exchange station and related equipment

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