Combined scheduling method for charging and configuration of mobile robot

Abstract

The invention relates to the field of electric power systems, and particularly discloses a combined dispatching method for charging and configuring a mobile robot, the mobile robot is suitable for charging electric vehicles in an expressway service area, and the method comprises the following steps: constructing a combined dispatching system for charging and configuring the mobile robot, the dispatching link of the combined dispatching system comprises a charging link and a distribution link; on the basis of a sequential relationship between a charging link and a distribution link, taking the optimal green power consumption capability as an upper-layer target and taking the lowest scheduling cost as a lower-layer target, constructing a combined scheduling double-layer model of charging and configuration of the mobile robot; and solving the joint scheduling double-layer model, and obtaining a scheduling scheme of the mobile robots, so that the distribution vehicle distributes the corresponding number of mobile robots to the service area with the energy supplement demand in advance according to the distribution path. The method not only can meet the urgent energy supplement demand of the vehicle owner in the peak period of the traffic flow, but also can assist the green power consumption and relieve the power grid pressure.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a joint scheduling method for charging and configuring a mobile robot. Background Art

[0002] Charging piles are used to provide energy supply (i.e. charging service) to new energy vehicles, and are necessary energy replenishment equipment for new energy vehicles. At present, although the number of existing charging piles in my country is already considerable, with the continuous increase in the number of new energy vehicles, there is still an imbalance between supply and demand, especially during peak hours and in some local areas. For example, during holidays such as May Day and National Day, there are often queues for charging and it is difficult to find a charging pile in highway service areas, which will not only affect the experience of new energy vehicle users, but also have a certain impact on the development of new energy vehicles.

[0003] To this end, charging facilities are urgently needed to keep up with the development of new energy vehicles. Summary of the invention

[0004] To this end, the present invention provides a joint scheduling method for charging and configuring a mobile robot, in an effort to solve or at least alleviate the above problems.

[0005] According to one aspect of the present invention, a joint scheduling method for charging and configuring a mobile robot is provided, wherein the mobile robot is suitable for charging electric vehicles in a highway service area, and the method comprises: constructing a joint scheduling system for charging and configuring the mobile robot, wherein the scheduling links of the joint scheduling system include a charging link and a distribution link; constructing a joint scheduling double-layer model for charging and configuring the mobile robot based on the time sequence relationship between the charging link and the distribution link, taking the optimal green electricity consumption capacity as the upper level goal and the lowest scheduling cost as the lower level goal; solving the joint scheduling double-layer model to obtain a scheduling plan for the mobile robot, so that based on the scheduling plan, a corresponding number of mobile robots can be delivered in advance by the delivery vehicles according to the delivery path to the highway service area with energy replenishment demand, wherein the scheduling plan includes a charging plan and a distribution plan for the mobile robot, wherein the charging plan includes the number of mobile robots in a charging state in each time period within the scheduling cycle, and the distribution plan includes the time and number of mobile robots delivered by each delivery vehicle according to its delivery path each time within the scheduling cycle, and the number includes the number of mobile robots delivered by each delivery vehicle to each service area in the delivery path each time according to its delivery path.

[0006] Optionally, in the joint scheduling method for charging and configuring a mobile robot according to the present invention, the joint scheduling system includes an integrated energy storage and distribution charging station for charging and distribution scheduling of mobile robots, external renewable energy and power grids for providing electrical energy to the integrated energy storage and distribution charging station, delivery vehicles for delivering mobile robots to highway service areas, and highway service areas.

[0007] Optionally, in the joint scheduling method for charging and configuring a mobile robot according to the present invention, the optimal green electricity consumption capacity is the minimum difference between the normalized external renewable energy output and the charging load of the mobile robot within the scheduling period, and a two-layer model for joint scheduling of mobile robots is constructed with the optimal green electricity consumption capacity as the upper target and the lowest scheduling cost as the lower target, including: determining the difference between the normalized external renewable energy output and the charging load of the mobile robot within the scheduling period according to the total charging power of the mobile robot in each period within the scheduling period and the output value of renewable energy, and constructing a charging scheduling model with the minimum difference as the first objective function; determining the scheduling cost within the scheduling period according to the mechanical loss cost of the distribution vehicle and the charging cost of the mobile robot within the scheduling period, and constructing a distribution scheduling model with the minimum scheduling cost as the second objective function; constructing a two-layer model for joint scheduling with the charging scheduling model as the upper model and the distribution scheduling model as the lower model.

[0008] Optionally, in the joint scheduling method for charging and configuring a mobile robot according to the present invention, the first objective function includes:

[0009]

[0010] Among them, F1 represents the difference between the external renewable energy output and the charging load of the mobile robot after normalization within the scheduling period, and L t represents the total charging power of the mobile robot during period t, Y t It represents the output value of external renewable energy in period t, and T represents the total number of periods.

[0011] Optionally, in the joint scheduling method for charging and configuring a mobile robot according to the present invention, the mechanical loss cost of the delivery vehicle includes the startup cost of the delivery vehicle and the fuel consumption cost of the delivery vehicle, and the second objective function includes:

[0012]

[0013] Among them, F2 represents the dispatching cost, N represents the total number of delivery vehicles, C0 represents the startup cost of a delivery vehicle, Z represents the cost per unit distance, and D ij represents the distance from node i to node j, X ij,n is a 0-1 variable indicating whether the delivery vehicle n travels on path ij, J represents the charging cost of the mobile robot, and I represents the node set.

[0014] Optionally, in the joint scheduling method for charging and configuring a mobile robot according to the present invention, the charging scheduling model also includes a first constraint condition corresponding to the first objective function, the first constraint condition including the energy replenishment demand constraint of the highway service area in each time period, and the energy replenishment demand constraint of the highway service area in each time period includes: the number of mobile robots in a fully charged state in each time period in the energy storage and distribution integrated charging station must meet the number of mobile robots required to be carried by all delivery vehicles departing in this time period to meet the energy replenishment demand of the highway service area at the lowest user satisfaction.

[0015] Optionally, in the joint scheduling method for charging and configuring mobile robots according to the present invention, the delivery scheduling model includes a second constraint condition corresponding to the second objective function, and the second constraint condition includes the energy replenishment demand constraint of the highway service area under each delivery path, and the energy replenishment demand constraint of the highway service area under each delivery path includes: the maximum number of mobile robots that can be carried by the delivery vehicle under any delivery path must meet the number of mobile robots required for all highway service areas included in the delivery path within the time interval of the delivery vehicle under the minimum user satisfaction.

[0016] Optionally, in the joint scheduling method for mobile robot charging and configuration according to the present invention, the first constraint condition also includes a charging power constraint, a charging pile quantity constraint, and a mobile robot battery capacity sum constraint; the second constraint condition also includes a delivery vehicle quantity constraint, a delivery time constraint, and a delivery route selection constraint.

[0017] According to another aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing the joint scheduling method for charging and configuring a mobile robot according to the present invention.

[0018] According to another aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the joint scheduling method for charging and configuring a mobile robot according to the present invention.

[0019] According to the joint scheduling method for charging and configuring of mobile robots of the present invention, it is proposed to use mobile robots to charge new energy vehicles for the peak demand scenario of highways. In addition, the present invention aims to maximize the green fit between the new energy output curve and the charging load of the mobile robot and minimize the sum of the mechanical cost of the vehicle and the charging cost of the mobile robot, and construct a joint scheduling double-layer model for charging and configuring of mobile robots. Therefore, the present invention not only takes into account the energy replenishment demand of new energy vehicles, but also takes into account the demand for new energy consumption. In this way, it can not only meet the urgent energy replenishment demand of car owners during peak traffic flow periods and improve the user experience, but also help green electricity consumption, alleviate the pressure of the power grid during peak electricity consumption periods, and achieve peak shaving and valley filling. In addition, based on the joint scheduling method for charging and configuring of mobile robots of the present invention, mobile robots can be deployed more efficiently under peak demand, and the charging and discharging of mobile robots can be better matched with the demand for relieving the load pressure of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0021] Figure 1 A structural block diagram of a computing device 100 according to an embodiment of the present invention is shown;

[0022] Figure 2 A flowchart of a joint scheduling method 200 for charging and configuring a mobile robot according to an embodiment of the present invention is shown;

[0023] Figure 3 A schematic diagram of a joint scheduling system for charging and configuring a mobile robot according to an embodiment of the present invention is shown;

[0024] Figure 4 A schematic diagram of cyclic delivery according to an embodiment of the present invention is shown;

[0025] Figure 5 A schematic diagram of a two-layer model for joint scheduling of mobile robot charging and configuration according to an embodiment of the present invention is shown;

[0026] Figure 6 A schematic diagram showing a joint scheduling two-layer model solving process according to an embodiment of the present invention is shown;

[0027] Figure 7A schematic diagram showing a photovoltaic daily power curve of a photovoltaic storage and distribution integrated charging station according to an embodiment of the present invention is shown;

[0028] Figure 8 A schematic diagram showing a daily power curve of an external renewable energy source according to an embodiment of the present invention;

[0029] Fig. 9 A schematic diagram of a time-of-use electricity price according to an embodiment of the present invention is shown;

[0030] Fig.10 A schematic diagram showing a service area and charging station locations according to an embodiment of the present invention;

[0031] Fig.11 A schematic diagram showing a 24-hour charging curve of a new energy vehicle in a service area and an hourly maximum load curve of a charging pile according to an embodiment of the present invention is shown;

[0032] Fig.12 A schematic diagram showing a comparison of objective functions between an experimental group and a control group according to an embodiment of the present invention is shown;

[0033] Fig.13 A schematic diagram showing a comparison between a robot charging load and a service area demand load according to an embodiment of the present invention is shown;

[0034] Fig.14 A schematic diagram of curve fitting according to an embodiment of the present invention is shown;

[0035] Fig.15 A schematic diagram showing an optimal delivery route according to an embodiment of the present invention is shown;

[0036] Fig.16 A schematic diagram showing a curve of objective function value versus number of mobile robots according to an embodiment of the present invention is shown;

[0037] Fig.17 A schematic diagram showing an objective function value-time-of-use electricity price curve according to an embodiment of the present invention is shown;

[0038] Fig.18 A schematic diagram showing a curve of objective function value-maximum vehicle load number according to an embodiment of the present invention;

[0039] Fig.19 A schematic diagram of an objective function value-service area demand curve according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0040] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] The limited charging piles in highway service areas can hardly meet the peak charging demand of new energy vehicles during holidays, and the charging service guarantee during holidays needs to be strengthened. In addition, the increased grid load caused by the charging demand of new energy vehicles has brought huge pressure to the grid. Based on this, the present invention proposes that mobile robots can be used to charge new energy vehicles. On the one hand, as a charging unit, it can meet the surplus charging demand under peak demand. On the other hand, as an energy storage unit, it can absorb green electricity and alleviate the pressure of electricity consumption.

[0042] Among them, when using mobile robots as charging facilities for new energy vehicles, there are two major challenges: how to match the charging and discharging of mobile robots with the demand for relieving grid load pressure, and how to efficiently deploy mobile robots to meet peak demand. Based on this, the present invention provides a joint scheduling method for charging and configuring mobile robots.

[0043] The combined scheduling method for charging and configuring a mobile robot of the present invention may be executed in a computing device. Figure 1 A block diagram of the physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 can be implemented as a processor. The system memory 104 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, the system memory 104 includes an operating system 105 and a program module 106, and the program module 106 includes a joint scheduling module 120, which is configured to execute the joint scheduling method 200 for charging and configuring a mobile robot of the present invention.

[0044] According to one aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 1This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to one aspect, computing device 100 has additional features or functionality. For example, according to one aspect, computing device 100 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 1 1 is illustrated by removable storage 109 and non-removable storage 110.

[0045] As stated above, according to one aspect, program modules are stored in the system memory 104. According to one aspect, the program modules may include one or more application programs, and the present invention does not limit the type of application programs, for example, the application programs may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slide show applications, drawing or computer-aided applications, web browser applications, etc.

[0046] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 1 Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in this article can be operated via a dedicated logic integrated with other components of the computing device 100 on a single integrated circuit (chip). Embodiments of the present invention can also be practiced using other technologies capable of performing logical operations (such as AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0047] According to one aspect, the computing device 100 may also have one or more input devices 112, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 114 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0048] The term computer-readable medium as used herein includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 104, removable storage 109, and non-removable storage 110 are all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, tapes, disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computer device 100. According to one aspect, any such computer storage medium can be a part of computing device 100. Computer storage media do not include carrier waves or other propagated data signals.

[0049] According to one aspect, communication media is implemented by computer readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0050] Figure 2 A flowchart of a joint scheduling method 200 for charging and configuring a mobile robot according to an embodiment of the present invention is shown. The method 200 is suitable for scheduling a mobile robot charging and configuring a mobile robot on a computing device (eg Figure 1 Executed in the computing device 100 shown.

[0051] It should be noted that the present invention is aimed at the peak demand scenario of new energy vehicle energy replenishment in the highway network during holidays. The mobile robot specifically refers to a mobile robot used to charge electric vehicles in the highway service area (for the convenience of description, it will be referred to as a robot in the following description). Furthermore, the electric vehicle can be an electric vehicle. In addition, the present application pre-constructs a joint scheduling system (or joint scheduling framework) for charging and configuring mobile robots. In some implementations, it can be a day-ahead joint scheduling system for charging and configuring mobile robots.

[0052] Next, the combined scheduling method for charging and configuring a mobile robot of the present invention is described. Figure 2As shown, the method 200 begins at 210 .

[0053] In 210, a joint dispatching system for charging and configuring a mobile robot is constructed. The joint dispatching system includes an energy storage and distribution integrated charging station for charging and dispatching the mobile robot, external renewable energy (specifically, photovoltaic power generation and wind power generation) and a power grid for providing electric energy to the energy storage and distribution integrated charging station, a delivery vehicle for delivering the mobile robot to a highway service area, and a highway service area, such as Figure 3 .

[0054] The integrated energy storage and distribution charging station (for the sake of ease of description, it will be referred to as the charging station in the following description) can be configured around the highway as a dispatching hub for charging and distribution of mobile robots. In some embodiments, the integrated energy storage and distribution charging station has a distributed photovoltaic power generation system and an energy storage system (i.e., a mobile robot). Specifically, the electricity purchased during the night period and the green electricity generated during the day are used as the main sources of charging energy. Mobile robots are used for energy storage. During the peak charging period, the integrated energy storage and distribution charging station and the power grid work together to supply power to the highway service area. This can not only achieve peak shaving and valley filling, but also fully meet the energy replenishment needs of each service area, control charging costs, and effectively solve the problems of intermittent and instability of new energy power generation, and achieve efficient utilization of new energy and reliable power supply.

[0055] Regarding the scheduling optimization of the system, an explanation is given here. It mainly includes the following processes: based on past monitoring data, experience judgment and scientific calculation, the traffic flow, demand, grid electricity price and renewable energy output of the service area are first predicted (this application will not go into details), and the input information is processed through the predicted data to solve the scheduling model constructed subsequently. After the solution, the charging station guides the charging of the mobile robot and the delivery of the delivery vehicle according to the scheduling information. Specifically, in the scheduling process, the objects associated with the mobile robot's recent scheduling mainly include the public power grid, renewable energy, energy storage and distribution integrated charging stations, delivery vehicles and highway services. Among them, in some embodiments, the scheduling process can be divided into charging and distribution links according to different functions and time sequences, that is, the scheduling link of the joint scheduling system includes charging and distribution links. It can be seen Figure 3 . Below, the charging and distribution links are explained in detail.

[0056] In the charging process, the distributed energy of the integrated energy storage and distribution charging station cannot meet all the charging needs of the mobile robot, so the public power grid and renewable energy (i.e. external renewable energy) can be linked to charge the mobile robot together (i.e. the green electricity consumed by the mobile robot is internal distributed energy and external renewable energy). At the same time, the integrated energy storage and distribution charging station adopts a self-generation and self-use mode, and the surplus is connected to the grid. When there is a surplus of photovoltaic power, it is sold to the grid at the time-of-use price to obtain more profits; when photovoltaic power cannot meet the charging needs of the mobile robot, it purchases electricity from the external network to supplement the charging needs of the robot.

[0057] In the distribution link, the demand generated by the highway service area is used as a guide, and the charging station guides the delivery route of the delivery vehicle to complete early or on-time delivery. Among them, the vehicle distribution plan is set as a circular distribution, which is divided into two cases according to the length of the round-trip time: when the round-trip time of the vehicle is greater than one research cycle, it is assumed that the time when the vehicle stops delivering is the same as the time when the service area no longer provides services. The vehicle starts at a certain moment, returns to the starting point through the optimal path distribution, and then loads the mobile robot, and then sets off along the path until it stops at a specific moment. The demand interval met by each delivery of the vehicle is from the next time period when the vehicle departs to the time period when the vehicle returns to the distribution point; when the round-trip time is less than or equal to one research cycle, it is assumed that the time when the vehicle starts delivering is the same as the time when the service area starts to generate demand, and then delivers once every research cycle, and each time it meets the service area demand within the time period of each delivery. Figure 4 shown.

[0058] It can be seen that in the scheduling process, different transport vehicle driving paths and mobile robot charging solutions need to be considered, and different transport vehicle driving paths and robot charging solutions reflect different costs and green electricity consumption capabilities. Therefore, in some embodiments, cost minimization and green electricity consumption capacity maximization can be used as two goals to construct a joint scheduling model for mobile robot charging and configuration.

[0059] In addition, there is a nested structure in the two-layer programming model, which can be used to describe the decision-making game process of two decision makers with a master-slave relationship. The upper decision maker makes the decision first, and then the lower decision maker makes the optimal decision and feeds back to the upper layer. There is a time sequence between the charging and distribution links of the mobile robot in the present invention. The mobile robot makes the charging decision first and uses it to guide the decision maker to make a distribution plan. The distribution plan in turn constrains the charging decision, which is a good match with the two-layer programming model. Based on this, according to one embodiment of the present invention, the two goals of minimizing costs and maximizing green electricity consumption capacity can be divided into two layers.

[0060] Among them, the decision variables and constraints of the upper model are related to the charging strategy of the mobile robot, and mainly solve the charging decision problem of the mobile robot; the decision variables and constraints of the lower model are related to the distribution strategy of the mobile robot, and mainly solve the path planning problem of the mobile robot. At the same time, the upper and lower models are related and constrained to each other. Regarding this, the following is specifically explained through 220.

[0061] In 220, based on the timing relationship between the charging link and the distribution link, the optimal green electricity consumption capacity is taken as the upper level goal and the lowest scheduling cost is taken as the lower level goal, and a two-layer joint scheduling model for mobile robot charging and configuration is constructed. Specifically, based on the timing relationship that the charging link precedes the distribution link, it is determined to take the optimal green electricity consumption capacity as the upper level goal and the lowest scheduling cost as the lower level goal to construct a two-layer joint scheduling model for the joint scheduling system of mobile robot charging and configuration.

[0062] Regarding the timing relationship between the charging link and the distribution link, in some embodiments, it can be identified by a preset variable. Furthermore, the value of the preset variable can be set to true, indicating that the charging link precedes the distribution link. In this way, when the value of the preset variable is true, it is determined to take the optimal green power consumption capacity as the upper-level goal and the lowest scheduling cost as the lower-level goal to construct a two-layer model for the joint scheduling of mobile robot charging and configuration. Regarding the value of the preset variable, according to an embodiment of the present invention, an interface (such as a timing option) can be provided for setting, which is not limited by the present invention.

[0063] Among them, in order to ensure the rationality and effectiveness of the scheduling optimization model, the present invention established the following assumptions before establishing the joint scheduling two-layer model: 1) The mobile robot in the distribution center (i.e., the energy storage and distribution integrated charging station) is charged and discharged only once a day, and the discharge is only used to provide energy for new energy vehicles; 2) The daily demand and daily renewable energy output forecast have been completed in advance; 3) The distribution vehicle sets off on time and travels at a constant speed along the road, and has sufficient kinetic energy to travel back and forth on any path; 4) The time for loading and unloading the mobile robot during distribution is negligible; 5) The demand generation time in the service area is a continuous time period.

[0064] In addition, before explaining the construction of the joint scheduling two-layer model, the sets and parameters involved in the model construction are explained here, as shown in Tables 1 and 2 below.

[0065] Table 1

[0066]

[0067] Table 2

[0068]

[0069]

[0070] Next, the construction of the joint scheduling double-layer model is described in detail. Among them, promoting the consumption of external renewable energy can be achieved by placing the robot charging load point close to the external renewable energy output point in the same period. Therefore, in order to maximize the consumption of renewable energy by the mobile robot, the waveform similarity of the two curves can be maximized, that is, the fit between the external renewable energy output and the mobile robot charging load can be maximized. Based on this, in some embodiments, the goal of optimal green power consumption capacity can be set to minimize the difference between the external renewable energy output and the mobile robot charging load within the scheduling period.

[0071] Therefore, according to one embodiment of the present invention, a joint scheduling two-layer model may be constructed in the following manner.

[0072] Firstly, according to the total charging power of the mobile robot in each period within the scheduling cycle and the output value of renewable energy, the difference between the output of external renewable energy and the charging load of the mobile robot within the scheduling cycle is determined, and the charging scheduling model is constructed with the minimization of the difference as the first objective function.

[0073] Among them, in order to make the difference between the output of external renewable energy and the charging load of the mobile robot in the scheduling period better represent the green power consumption capacity and obtain a more accurate solution, in some embodiments, when obtaining the difference between the output of external renewable energy and the charging load of the mobile robot in the scheduling period, the total charging power of the mobile robot in each period and the output value of renewable energy can be normalized. Based on this, according to one embodiment of the present invention, the first objective function can be specifically as follows.

[0074]

[0075] Where F1 represents the difference between the external renewable energy output and the mobile robot charging load after normalization during the scheduling period, and L t represents the total charging power of the mobile robot during period t (or the total load of the mobile robot being charged during period t), Y t represents the output value of external renewable energy in period t, and T represents the total number of periods. S , N, m, I, when used as the summation upper limit in the present invention, they represent the total number of elements in the set.

[0076] The total charging power L of the mobile robot in period t t , in some embodiments, can be obtained by the following formula.

[0077] Lt =P*μ t

[0078] Where P represents the charging power of a mobile robot, μ t Represents the number of mobile robots in the charging state during period t.

[0079] In addition, it should be noted that L = {L t}、Y={Y t}, L represents the set consisting of the total charging power of the mobile robot in all time periods within the scheduling cycle. In some embodiments, the scheduling cycle can be one day, and the research cycle can be 1 hour (that is, the scheduling cycle is divided into multiple research time periods by 1 hour). In this case, the value of T is 24, and L represents the set of 24 load points (corresponding to 24 hours). Similarly, Y represents the set consisting of the output values ​​of external renewable energy in all time periods within the scheduling cycle, which will not be repeated here.

[0080] Furthermore, in order to construct a charging scheduling model, it is also necessary to constrain the charging power, the number of charging piles, the service area demand, and the sum of the battery capacity of the mobile robot, including: charging power constraint, charging pile number constraint, energy replenishment demand constraint of the highway service area in each time period, and the sum of the battery capacity of the mobile robot. For the sake of ease of description, the above constraints are referred to as the first constraints. Below, each constraint in the first constraint is explained.

[0081] Charging power constraint: This constraint includes that the charging power of a single mobile robot cannot exceed its maximum charging power, and the total charging power of the mobile robots in each time period cannot be greater than the rated charging power of the charging station, as follows.

[0082] 0≤P≤P Max

[0083]

[0084] Where P Max represents the maximum charging power of the mobile robot, represents the maximum number of mobile robots in the charging state in all time periods within the scheduling cycle, that is, μ t The maximum value in a set, Indicates the maximum charging power of the charging station.

[0085] Constraint on the number of charging piles: This constraint includes that the number of mobile robots in the charging state in each time period cannot exceed the number of charging piles provided by the charging station, as follows.

[0086] μ t ≤F

[0087] Where F represents the number of fixed charging piles in the charging station.

[0088] Constraints on the recharging demand of highway service areas at different time periods: This constraint includes the number of mobile robots in a fully charged state at each time period in the energy storage and distribution integrated charging station, which must meet the number of mobile robots that all delivery vehicles departing at this time period need to carry in order to meet the recharging demand of highway service areas at the lowest user satisfaction level. That is, the fully charged robots at the charging station at time period t must meet the number of robots that the delivery vehicles carry when they depart to meet the lowest satisfaction level of the service area customers, as follows.

[0089]

[0090] In the formula, r1 represents the number of fully charged robots at the charging station, μ t represents the number of mobile robots in the charging state during period t, t s,n represents the departure time of the delivery vehicle n from the distribution center for the sth time, t x,n represents the round trip time of delivery vehicle n (in hours), X ij,n is a 0-1 variable indicating whether delivery vehicle n takes path ij. If delivery vehicle n takes path ij, then X ij,n The value of is 1. If the delivery vehicle n does not take the path ij, then X ij,n The value of X is 0, that is, ij,n is a 0-1 variable, where if delivery vehicle n moves from node i to node j, then X ij,n The value of X is 1. If the delivery vehicle n does not move from node i to node j, then X ij,n The value of S is 0. j,t represents the number of mobile robots required for service area j in time period t, β represents the minimum customer satisfaction, and M represents the total number of service areas. In addition, it should be noted that [t s,n ]、[t s,n +t x,n ]、[S j,t *β] The brackets in [β] indicate rounding down, and the nodes in this application are specifically composed of energy storage and distribution integrated charging stations and various highway service areas, and the node value corresponding to the energy storage and distribution integrated charging station is 0.

[0091] Constraint on the sum of the battery capacities of mobile robots: This constraint mainly takes into account the limited number of robots to be charged at the charging station, that is, the sum of the battery capacities is limited. Specifically, the total charging load of the mobile robots in the entire scheduling period cannot be greater than the sum of the battery capacities of the robots to be charged at the charging station. In some embodiments, it can be expressed as the following formula.

[0092]

[0093] In the formula, RG represents the sum of the battery capacities of the mobile robots to be charged, Δt represents the duration of a time period. Regarding RG, it should be explained here that the energy storage and distribution integrated charging station initially includes fully charged mobile robots and mobile robots to be charged. RG represents the sum of the battery capacities of all mobile robots to be charged in the energy storage and distribution integrated charging station.

[0094] At this point, the charging scheduling model is composed of the first objective function and the first constraint condition.

[0095] Then, according to the mechanical loss cost of the distribution vehicles and the charging cost of the mobile robot during the scheduling period, the scheduling cost within the scheduling period is determined, and the distribution scheduling model is constructed with the lowest scheduling cost as the second objective function.

[0096] According to one embodiment of the present invention, the mechanical wear and tear cost of the delivery vehicle includes the startup cost of the delivery vehicle and the fuel consumption cost of the delivery vehicle. Therefore, in some embodiments, the second objective function may be specifically the following formula.

[0097]

[0098] In the formula, F2 represents the total dispatching cost of the mobile robot, N represents the total number of delivery vehicles, C0 represents the startup cost of a delivery vehicle, Z represents the fuel consumption cost per unit distance, and D ij represents the distance from node i to node j, and J represents the total charging cost of the mobile robot.

[0099] About X ij,n , as shown in the following formula:

[0100]

[0101] Regarding the charging cost of the mobile robot, according to one embodiment of the present invention, it is the difference between the cost of purchasing electricity from the external network when the internal photovoltaic output is insufficient and the income obtained by the charging station from selling electricity to the external network when the internal photovoltaic output is surplus, which can be specifically expressed as the following formula.

[0102]

[0103]

[0104]

[0105] In the formula, J 1,t , J 2,t They represent the cost of purchasing electricity from the outside and the income from selling electricity to the outside during period t, respectively. t represents the time-of-use electricity price during period t, E t Represents the internal distributed energy output during period t.

[0106] Furthermore, in order to construct a distribution scheduling model, it is also necessary to constrain the delivery time of the delivery vehicles, the route selection of the delivery vehicles, the service area demand, and the number of delivery vehicles, including the delivery time constraint of the delivery vehicles, the delivery route selection constraint of the delivery vehicles, the energy replenishment demand constraint of the highway service area under each delivery route, and the number of delivery vehicles. For the convenience of description, the above constraints are referred to as the second constraints. Below, each constraint in the second constraint is explained.

[0107] Delivery time constraint of delivery vehicles: This constraint specifically prohibits premature delivery and delayed delivery. In some embodiments, it can be expressed as the following formula.

[0108]

[0109]

[0110]

[0111] a2∈{N|a 2,max =t2-t1}

[0112] In the formula, t s,n represents the time when the delivery vehicle n departs from the energy storage and delivery integrated charging station for the sth time. Specifically, t s,n =t2-a2 represents the time when the delivery vehicle n with a round trip time less than or equal to one research period departs from the energy storage and delivery integrated charging station at s=a2 (i.e., the a2th delivery), t s,n =t2-a1t x,n represents the time when a delivery vehicle n with a round trip time greater than one study period departs from the energy storage and delivery integrated charging station at s = a1 (i.e., the a1th delivery), t x,n represents the round trip time of delivery vehicle n, t2 represents the time when the service area no longer generates demand, t1 represents the time when the service area starts to generate demand, V represents the driving speed of the delivery vehicle, a 1,max represents the maximum value of the number of deliveries a1 for a delivery vehicle whose round trip time is greater than one research period, a 2,max It represents the maximum value of the number of deliveries a2 of the delivery vehicle whose round trip time is less than or equal to one research period, and N represents a natural number.

[0113] Distribution route selection constraints for delivery vehicles: For all delivery vehicles, once the delivery vehicle is started, it must start from the starting point and return to the starting point, and each service area must be served once. Therefore, the constraints include that the starting point and the end point of the delivery vehicle are both energy storage and distribution integrated charging stations, the number of times the delivery vehicle enters and exits the highway service area is the same, and the delivery vehicle can only pass through any highway service area once, as follows.

[0114]

[0115]

[0116]

[0117]

[0118] In the formula, X 0j,n is a 0-1 variable, where if the delivery vehicle n moves from the energy storage and distribution integrated charging station to the service area j, then X 0j,n The value of is 1. If the delivery vehicle n does not move from the energy storage and distribution integrated charging station to the service area j, then X 0j,n The value of X is 0; i0,n is a 0-1 variable, where if the delivery vehicle n moves from the service area i to the energy storage and distribution integrated charging station, then X i0,n The value of is 1. If the delivery vehicle n does not move from the service area i to the energy storage and distribution integrated charging station, the value is 0; X ik,n is a 0-1 variable, where if delivery vehicle n moves from node i to node k, then X ik,n The value is 1. If the delivery vehicle n does not move from node i to node k, then X ik,n The value is 0; X kj,n is a 0-1 variable, where if delivery vehicle n moves from node k to service point j, then X kj,n The value is 1. If the delivery vehicle n does not move from node k to node j, then X kj,n The value is 0.

[0119] Constraints on the replenishment demand of highway service areas under each delivery route: This constraint includes that the maximum number of mobile robots that can be carried by a delivery vehicle under any delivery route must meet the number of mobile robots required for the minimum user satisfaction of all highway service areas included in the delivery route within the time interval of the delivery vehicle. That is, when the vehicle cannot meet all the needs of the service area, it must at least meet the minimum customer satisfaction. Therefore, on a route, the maximum number of robots that a vehicle can carry must at least meet the sum of the corresponding demands of all service areas corresponding to the minimum satisfaction within the time interval of the vehicle's travel, as follows.

[0120]

[0121] Where U n Represents the number of mobile robots corresponding to the maximum load of delivery vehicle n.

[0122] Number constraint of delivery vehicles: This constraint includes that the number of delivery routes cannot exceed the number of delivery vehicles. Specifically, when each vehicle is responsible for one route, the number of routes after path planning cannot exceed the number of existing transport vehicles at the charging station. In some embodiments, it can be expressed as the following formula.

[0123]

[0124] At this point, the distribution scheduling model is composed of the second objective function and the second constraint condition.

[0125] Finally, the charging scheduling model is used as the upper model and the distribution scheduling model is used as the lower model to construct a two-layer model for the joint scheduling of mobile robot charging and configuration. Figure 5 .

[0126] At this point, the construction of the joint scheduling double-layer model for charging and configuring the mobile robot is completed. Then, entering 230, the joint scheduling double-layer model is solved to obtain a scheduling plan for the mobile robot, so that according to the scheduling plan, the corresponding number of mobile robots can be delivered in advance by the delivery vehicles according to the delivery path to the highway service area with the energy replenishment demand.

[0127] The scheduling plan includes the charging plan and delivery plan of the mobile robot. Furthermore, the charging plan of the mobile robot includes the number of mobile robots in the charging state in each time period within the scheduling cycle, and the delivery plan of the mobile robot includes the time and number of mobile robots delivered by each delivery vehicle according to its delivery path each time within the scheduling cycle. The number of mobile robots delivered by each delivery vehicle according to its delivery path each time within the scheduling cycle includes the number of mobile robots delivered by each delivery vehicle to each service area in the delivery path each time according to its delivery path.

[0128] That is to say, the distribution plan of the mobile robot specifically includes the distribution path, the time when each distribution vehicle distributes the mobile robot each time (i.e., the time when each distribution vehicle departs from the energy storage distribution integrated charging station each time), and the number of mobile robots distributed to each service area in its distribution path by each distribution vehicle each time. It should be noted that in this embodiment, each distribution vehicle is responsible for one distribution path, and distributes cyclically according to the distribution path within the scheduling cycle.

[0129] Furthermore, in some embodiments, after obtaining the dispatching scheme, a dispatching instruction is generated based on the dispatching scheme, and the dispatching instruction is sent to the energy storage and distribution integrated charging station, so that the energy storage and distribution integrated charging station charges a corresponding number of mobile robots in each time period based on the dispatching instruction, and a number of delivery vehicles deliver a corresponding number of mobile robots to a highway service area with energy replenishment demand in advance according to their delivery routes. The dispatching instruction includes the dispatching scheme.

[0130] In addition, regarding the joint scheduling two-layer model, according to an embodiment of the present invention, the NSGA-II multi-objective genetic algorithm can be used to solve it, see Figure 6 Among them, the fitness function is first constructed according to the objective function and constraints, and then the initial population is non-dominated sorted to determine the parent population, and then a set of Pareto solutions are obtained. Regarding the solution of the joint scheduling two-layer model, I will not go into details here. The NSGA-II algorithm is relatively mature, and you can refer to its relevant description for details.

[0131] The above is the joint scheduling method for charging and configuring mobile robots of the present invention. Below, an example is taken of a highway network radiating from a region as the center. Among them, in order to verify the rationality and effectiveness of the model proposed by the present invention, the present invention will set up two comparison schemes, as shown in Table 3 below. Under the same background, only cost minimization and curve fitting optimization are considered. In the case of only considering the optimal curve fitting, the distribution strategy part cannot be solved due to the lack of objective function. Therefore, the distribution strategy in this scenario is the optimal distribution strategy in the original model. After the optimal solution is obtained by genetic algorithm, it is compared with the optimal solution in the original model and analyzed. Next, this example is described in detail.

[0132] Table 3

[0133]

[0134] 1. Parameter settings

[0135] (1) Parameters of energy storage and distribution integrated charging station

[0136] Based on the various requirements of the present invention for green electricity consumption and charging robot distribution, and considering the actual situation in my country, a charging station integrating photovoltaic storage and distribution is set up. The photovoltaic daily output of the power station adopts the typical daily output data of distributed photovoltaic power stations. The daily output curve of the power plant and related parameters are as follows: Figure 7 and as shown in Table 4 below.

[0137] Table 4

[0138]

[0139] The power plant is connected to the external grid and adopts a model of self-generation for self-use and surplus power being connected to the grid. Surplus electricity is fed into the external grid to earn electricity charges. When self-generated electricity cannot meet all the charging needs of the charging robot, electricity is purchased from the external grid to meet the demand.

[0140] (2) External renewable energy generation

[0141] This example uses the renewable energy output power of a power plant in a certain region on a certain day in August as the external renewable energy daily output to solve the waveform similarity problem. Renewable energy includes wind power and photovoltaic power. The specific values ​​are as follows: Figure 8 shown.

[0142] (3) Time-of-use electricity price

[0143] In this example, the scheduling period is 1 day, and 1 hour is set as the research period. Fig. 9 Time-of-use electricity prices shown.

[0144] (4) Location and distance of service area

[0145] Use Python and Baidu Map API to crawl the longitude and latitude of the road network service area and the distance between each point, and take the actual photovoltaic power station coordinates in the area as the location of the photovoltaic storage and distribution integrated charging station. The service area location is as follows: Fig.10 shown.

[0146] (5) Service area demand for robots

[0147] Combined with relevant data, we can get the curve of the 24-hour charging capacity of new energy vehicles and the hourly maximum load of charging piles in the service area during the May Day holiday, such as Fig.11 shown.

[0148] It can be seen that the distribution of charging capacity in 24 hours a day during the May Day holiday generally presents an "M" type distribution. The basic trend is that the number of vehicles passing through the highway gradually increases after 5 o'clock every day, and the charging capacity begins to rise. The charging peak comes at 10-15 o'clock, and the highest charging capacity of the day is reached at 14 o'clock. After 17 o'clock, the power begins to decline, and the vehicle leaves the highway, and the charging capacity gradually decreases; and according to the current hourly maximum load of the charging pile infrastructure, it is found that during the holiday period of 9-22 hours, the existing service area fixed charging piles cannot meet the charging needs of all new energy vehicles, so mobile robots are needed to make up for the surplus energy replenishment demand caused by the holiday peak. Based on the above content, the surplus new energy vehicle demand under the holiday peak demand is calculated according to the national highway network data, and then allocated to each service area after a certain proportion of scaling, and the demand matrix of each service area in the road network is obtained, in which the minimum customer satisfaction β is 0.8.

[0149] (6) Delivery vehicle related parameters

[0150] Based on the above description and combined with the demand matrix, the vehicle delivery related parameters are shown in Table 5 below.

[0151] Table 5

[0152]

[0153] 2. Results Analysis

[0154] Through calculation, the sum of the absolute values ​​of the minimum normalized difference of the optimal solution is 0.22, and the minimum cost is 72782.27. Comparing the fitting value and cost of the above optimal solution with the other two control groups, we can get Fig.12 .

[0155] The results show that, considering single-objective optimization, each retained objective is further optimized compared with the optimal solution of the original model, but the function value of another ignored objective function increases significantly, and the objective function value of the optimal solution of the original model is less than the average of the objective function values ​​of the other two schemes. This shows that the original model has made a good trade-off between the dual objectives of fitting the curve and the minimum function, so that the dual objective solutions are at the better end, which meets the definition of Pareto optimal solution.

[0156] The output robot charging load curve in this example and the demand load corresponding to the distribution line are as follows: Fig.13 Among them, because the robots participating in charging at 10 o'clock cannot meet the required load value of the service area, the fully charged robots in the night period are called in to participate in the dispatch and delivery.

[0157] Curve fitting diagram Fig.14 As shown in the figure, based on the requirements of green power consumption, cost and service area demand, a large number of mobile robots choose to consume wind power at night to prepare for the surplus demand of the service area during the day; during the 10-13 period, the output of renewable energy is at normal times, while the time-of-use electricity price is at its peak. In order to reduce costs, the number of mobile robots charged during this period has decreased, with the lowest point at 11 o'clock; in the afternoon, the demand decreases, and in order to fit the renewable energy curve, the number of mobile robots charged during this period has increased; during the night period from 18:00 to 20:00, the output of renewable energy coincides with the peak period of time-of-use electricity price, but considering the cost, the number of mobile robots charged under this charging scheme has declined significantly after reaching the peak at 21:00.

[0158] The optimal delivery route for the best solution is Fig.15As shown in the figure, 15 transport vehicles only form 11 transport routes, and the demand satisfaction rate is 99.16%. Due to the matching of transportation time and delivery volume constraints, the round-trip time is between 0 and 4 hours. Therefore, when the vehicle departs, it is necessary to consider the demand of the service area during the round-trip period. The starting departure time of the delivery route has a tolerance rate, and departure in a period earlier than the starting delivery time will not affect the output result (the length of the period depends on the minimum value of the minute digit corresponding to each delivery time).

[0159] In addition, in terms of absorbing green electricity, the total daily load of the mobile robots in the charging station in this example is 56,100 kW, of which an additional 52,006 kW of external grid electricity is absorbed, and the internal photovoltaic absorption rate reaches 100%, indicating that the mobile robots not only absorb most of the internal photovoltaic power, but also promote the absorption of external new energy.

[0160] For the extra fully charged robots in one day, the charging station can choose to store electricity to cope with the peak demand of the next day. The vehicles can be delivered in advance to avoid the peak period and cause delays in arrival. They can also be used as backup energy to prevent emergencies and alleviate emergency needs. Assuming that all mobile robots are charging new energy vehicles at an electricity price of 1.5 yuan / kWh, the income from charging services and selling surplus electricity to the external network minus the fuel cost and charging costs, the total income per day can be calculated to be about 11,370 yuan, which proves that mobile robots can not only help the power grid to reduce peaks and fill valleys and absorb green electricity, but also bring huge profits to charging manufacturers, with good economic benefits.

[0161] 3. Sensitivity analysis

[0162] After obtaining the results, in order to test the stability of the optimal solution, this example will conduct a sensitivity analysis on four variables: the number of mobile robots, peak and valley electricity prices, the number of robots with the maximum vehicle load, and service area demand, to see the impact of these four parameters on the model output results.

[0163] (1) Number of mobile robots

[0164] The number of mobile robots represents the charging station's ability to absorb green electricity and meet demand. Corresponding changes in the number of charging robots will affect the robot's charging and discharging strategy and distribution strategy. In order to explore the impact of changes in the number of robots on the output results, the number of charging robots is changed in units of 100 within the range of [900-1500], and the changes in the normalized difference sum and the minimum cost are recorded to obtain Fig.16 .

[0165] As can be seen from the figure, ① the optimal fitting value shows a downward trend with the increase in the number of mobile robots. As the number of robots involved in charging increases, charging companies can arrange the charging of mobile robots in each period more flexibly according to the service area demand and the green electricity consumption demand, making the fit better. ② The minimum cost shows an upward trend with the increase in mobile robots. This is because as the number of robots to be charged increases, the number of mobile robots participating in the optimization scheduling to consume green electricity and meet demand also increases. The increase in the number of mobile robots participating in the scheduling brings high charging costs, but there is a certain conflict between the goals of green electricity consumption and cost, so the optimal solution obtained will fluctuate. ③ The number of mobile robots has an effective solution only between [900-1500], and after the mobile robots change to more than 1100, the objective function value is insensitive to the change in the number of mobile robots. This is because the total demand in the service area is only about 880. Even if a certain number of robots are added later to meet the green electricity consumption, the increase ratio will be reduced, and the corresponding impact on the objective function value will be lower.

[0166] (2) Peak and valley time-of-use electricity prices

[0167] Distributed photovoltaic power stations cannot meet the charging needs of mobile robots, and they will purchase electricity from the external network to make up for the surplus charging needs during certain periods of time. The peak and valley time-of-use electricity prices of the external network will affect the charging cost, thereby changing the robot's charging, discharging and distribution strategies. In order to explore the sensitivity of time-of-use electricity prices to the objective function value, the following will modify the time-of-use electricity price, gradually narrow the peak-valley price difference, and record the minimum value of the objective function of each optimal solution to obtain Fig.17 .

[0168] As can be seen from the figure, ① as the peak-valley price difference narrows, the minimum normalized difference gradually increases. ② as the peak-valley price difference narrows, the minimum cost fluctuates slightly within the range of [70000-80000] and remains basically stable. This is because after the peak-valley price difference narrows, the cost difference between charging during peak hours and during valley hours also gradually narrows. The probability of charging robots at charging stations during valley hours becomes smaller, and the probability of charging robots during peak hours will be higher. Therefore, the peak-shaving and valley-filling effect of the power grid becomes smaller. When the average daily electricity price remains unchanged, the cost difference between the various schemes is not large, but the green electricity absorption capacity is significantly deteriorated.

[0169] (3) Maximum vehicle load

[0170] The maximum number of robots loaded by a vehicle will constrain the transportation route and affect the transportation strategy. Taking 1 vehicle as a unit, the transportation vehicle changes in the interval [15, 25]. The changes in the values ​​of each objective function are as follows: Fig.18 shown.

[0171] It can be seen from the figure that: ① The minimum cost and the best fit value fluctuate within a certain range as the maximum load of the transport vehicle changes. According to the observation results, as the number of robots with the maximum load of the vehicle increases, the best fit value shows an increasing trend, and the minimum cost shows a decreasing trend. Especially in the value range of [18,19], [22,23], the variation range of the two objective function values ​​further increases. ② The model has a solution only when the maximum load of the vehicle is between 16 and 30.

[0172] After analysis, when the vehicle is in the interval [16,19], the number of vehicle starts increases due to the decrease in vehicle load, and the high vehicle startup cost causes a sharp increase in the total cost; when the maximum vehicle load is in the interval [20,22], the number of vehicles required for timely delivery remains unchanged due to insufficient vehicle load, but as the maximum vehicle load increases, the system will change the delivery plan and choose a route with lower delivery cost, so that more mobile robots are required to meet the constraints in the same time period, so the optimal fit value will increase; when the maximum vehicle load is in the interval [23,25], only 9 delivery routes are formed, and the vehicle startup cost and transportation cost will both decrease significantly, but it will be the same as the above situation, resulting in greater demand at the same time, so the optimal fit value will increase significantly; when the maximum vehicle load is reduced to less than 16 units, due to the mismatch between transportation time and transportation demand constraints, no effective solution can be obtained.

[0173] (4) Service area requirements

[0174] The service area demand is a constraint condition for both the upper and lower layers of the model, and affects the charging and delivery decisions at the same time. The service area demand will gradually increase / decrease by 20% in the range of [0.2, 0.6] and then be rounded down (when the service area demand is equal to 0, the value of the service area demand will no longer be reduced). The changed service area demand is input and the change in the objective function value is observed, as shown in the figure below. Fig.19 shown.

[0175] As can be seen from the figure, ① With the increase in demand, both the optimal fitting value and the minimum cost show an upward trend, among which the increase between -60% and -40% is significantly larger. This is because the smaller the demand scale, the fewer robots that form the transportation route and the rigid requirement for charging within a specific time, the more flexible the charging strategy, and the higher the curve fitting degree. Although the increase in the number of transportations has caused certain fluctuations in the variable cost of distribution, the proportion of variable costs in the total cost is small, and the overall cost has declined due to the influence of charging costs and vehicle startup costs. When the demand in the service area is reduced to 40%, some service areas with less demand no longer need to arrange vehicles for delivery because the demand is 0, so the total cost drops significantly. ② The model has a solution only between -60% and +20%. When the demand in the service area increases to 40% or more, an effective solution cannot be obtained due to the mismatch between the transportation time and the transportation demand constraints.

[0176] In summary, the present invention aims at the demand for mobile robots during peak hours on highways, and constructs a two-layer optimization scheduling model from the perspective of mobile robots as energy storage devices and charging devices to obtain scheduling solutions. In this way, it can not only meet the urgent energy replenishment needs of car owners during peak traffic hours, but also help to absorb green electricity, alleviate the pressure on the power grid during peak electricity consumption, and fill the valley for the power grid. At the same time, it can also effectively solve the drawbacks of instability of renewable energy power supply, which has great economic and environmental benefits.

[0177] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present invention, or some aspects or parts of the method and apparatus of the present invention may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0178] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0179] It should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0180] In addition, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, ranking or in any other manner.

[0181] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A combined scheduling method for charging and configuring a mobile robot, wherein the mobile robot is suitable for charging electric vehicles in a highway service area, the method comprising: Constructing a joint dispatching system for charging and configuring the mobile robot, wherein the dispatching links of the joint dispatching system include a charging link and a distribution link; Based on the temporal relationship between the charging and distribution links, a two-layer model for joint scheduling of mobile robot charging and configuration is constructed with the optimal green electricity consumption capacity as the upper-level goal and the lowest scheduling cost as the lower-level goal. The joint scheduling two-layer model is solved to obtain a scheduling plan for the mobile robots, so that based on the scheduling plan, the delivery vehicles can pre-deliver a corresponding number of mobile robots to highway service areas with energy replenishment needs along the delivery path. The scheduling plan includes a charging plan and a delivery plan for the mobile robots. The charging plan includes the number of mobile robots in a charging state in each time period within a scheduling cycle. The delivery plan includes the time and number of mobile robots delivered by each delivery vehicle along its delivery path each time within the scheduling cycle. The number includes the number of mobile robots delivered by each delivery vehicle to each service area in the delivery path each time according to its delivery path.

2. The method of claim 1, wherein: The joint dispatching system includes an energy storage and distribution integrated charging station for charging and dispatching mobile robots, an external renewable energy source and a power grid that provides electric energy to the energy storage and distribution integrated charging station, a delivery vehicle for delivering mobile robots to highway service areas, and a highway service area.

3. The method according to claim 1 or 2, wherein: The optimal green power consumption capacity is the minimum difference between the normalized external renewable energy output and the charging load of the mobile robot during the scheduling period. The optimal green power consumption capacity is the upper level goal and the lowest scheduling cost is the lower level goal. A two-level joint scheduling model of the mobile robot is constructed, including: According to the total charging power of the mobile robot in each period of the scheduling cycle and the output value of the renewable energy, the difference between the normalized external renewable energy output and the charging load of the mobile robot in the scheduling cycle is determined, and the minimization of the difference is taken as the first objective function to construct a charging scheduling model; According to the mechanical loss cost of the delivery vehicles and the charging cost of the mobile robots within the dispatching period, the dispatching cost within the dispatching period is determined, and the delivery dispatching model is constructed with the lowest dispatching cost as the second objective function; The charging scheduling model is used as the upper model, and the distribution scheduling model is used as the lower model to construct the joint scheduling two-layer model.

4. The method of claim 3, wherein: The first objective function includes: Among them, F1 represents the difference between the external renewable energy output and the charging load of the mobile robot after normalization within the scheduling period, and L t represents the total charging power of the mobile robot during period t, Y t It represents the output value of external renewable energy in period t, and T represents the total number of periods.

5. The method according to claim 3 or 4, wherein: The mechanical loss cost of the delivery vehicle includes the startup cost of the delivery vehicle and the fuel consumption cost of the delivery vehicle, and the second objective function includes: Among them, F2 represents the dispatching cost, N represents the total number of delivery vehicles, C0 represents the startup cost of a delivery vehicle, Z represents the cost per unit distance, and D ij represents the distance from node i to node j, X ij,n is a 0-1 variable indicating whether the delivery vehicle n travels on path ij, J represents the charging cost of the mobile robot, and I represents the node set.

6. The method according to any one of claims 3 to 5, wherein: The charging scheduling model also includes a first constraint condition corresponding to the first objective function, the first constraint condition including a constraint on the energy replenishment demand of the highway service area in each time period, and the constraint on the energy replenishment demand of the highway service area in each time period includes: The number of mobile robots that are fully charged in the integrated energy storage and distribution charging station at each time period must meet the number of mobile robots required to be carried by all delivery vehicles departing during that time period to meet the energy replenishment needs of the highway service area at the lowest user satisfaction.

7. The method according to any one of claims 3 to 6, wherein: The distribution scheduling model includes a second constraint condition corresponding to the second objective function, the second constraint condition includes a constraint on the energy replenishment demand of the highway service area under each distribution route, and the constraint on the energy replenishment demand of the highway service area under each distribution route includes: The maximum number of mobile robots that a delivery vehicle can carry on any delivery route must meet the number of mobile robots required for the minimum user satisfaction in all highway service areas included in the delivery route within the time interval of the delivery vehicle.

8. The method of claim 7, wherein: The first constraint conditions also include charging power constraint, charging pile quantity constraint, and mobile robot battery capacity sum constraint; the second constraint conditions also include delivery vehicle quantity constraint, delivery time constraint, and delivery route selection constraint.

9. A computing device comprising: at least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1 to 8.

10. A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device executes the method according to any one of claims 1 to 8.

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