Power grid interactive scheduling method, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power grid interactive scheduling method and device, computer equipment, a readable storage medium and a program product, and is applied to the technical field of big data, and the method comprises the steps: obtaining the arrival residual electric quantity of target electric equipment at a charging position, and obtaining the system operation information of a power supply grid system correspondingly deployed at the charging position; according to the reached residual electric quantity and the charging and discharging scheduling information, predicting the charging and discharging state of the target electric equipment to obtain predicted charging and discharging information; predicting a power supply state according to the system operation information and the interactive scheduling information to obtain predicted power supply state information; optimizing the operation cost according to the predicted charging and discharging information and the predicted power supply state information to obtain target charging and discharging scheduling information and target interaction scheduling information; and according to the target charging and discharging scheduling information and the target interaction scheduling information, scheduling the target electric equipment and the power supply grid system respectively. By adopting the method, the operation cost of a power supply grid system can be reduced.

Description

Technical Field

[0001] The present application relates to the field of big data technology, and in particular, to a power grid interaction scheduling method, a computer device, a computer-readable storage medium, and a computer program product. Background Art

[0002] With the rising fuel prices, people's demand for electric devices is increasing. Then, the power supply grid system available for charging electric devices is becoming more and more popular. Currently, when the power supply grid system powers an electric device, only the charging efficiency of the electric device is usually considered, resulting in a high operating cost of the power supply grid system. Summary of the Invention

[0003] Based on this, in view of the above technical problems, it is necessary to provide a power grid interaction scheduling method, device, computer device, computer-readable storage medium, and computer program product that can reduce the operating cost of the power supply grid system.

[0004] In a first aspect, the present application provides a power grid interaction scheduling method, including:

[0005] Obtaining the remaining power when the target electric device arrives at the charging position, and obtaining the system operation information of the power supply grid system deployed corresponding to the charging position;

[0006] Predicting the charge and discharge state of the target electric device according to the remaining power when arriving and the charge and discharge scheduling information of the target electric device to obtain predicted charge and discharge information;

[0007] Predicting the power supply state of the power supply grid system according to the system operation information and the interaction scheduling information of the power supply grid system to obtain predicted power supply state information;

[0008] Optimizing the operating cost of the power supply grid system according to the predicted charge and discharge information and the predicted power supply state information to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system;

[0009] Scheduling the target electric device according to the target charge and discharge scheduling information, and scheduling the power supply grid system according to the target interaction scheduling information.

[0010] In a second aspect, the present application further provides a power grid interaction scheduling device, including:

[0011] An obtaining module, configured to obtain the remaining power when the target electric device arrives at the charging position, and obtain the system operation information of the power supply grid system deployed corresponding to the charging position;

[0012] A prediction module, configured to predict the charge and discharge state of the target electric device according to the remaining charge upon arrival and the charge and discharge scheduling information of the target electric device, so as to obtain predicted charge and discharge information; predict the power supply state of the power grid system according to the system operation information and the interactive scheduling information of the power grid system, so as to obtain predicted power supply state information;

[0013] An optimization module, configured to optimize the operation cost of the power grid system according to the predicted charge and discharge information and the predicted power supply state information, so as to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power grid system;

[0014] A control module, configured to schedule the target electric device according to the target charge and discharge scheduling information, and schedule the power grid system according to the target interactive scheduling information.

[0015] In a third aspect, the present application further provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] Obtain the remaining charge upon arrival of the target electric device at the charging location, and obtain the system operation information of the power grid system deployed corresponding to the charging location;

[0017] Predict the charge and discharge state of the target electric device according to the remaining charge upon arrival and the charge and discharge scheduling information of the target electric device, so as to obtain predicted charge and discharge information;

[0018] Predict the power supply state of the power grid system according to the system operation information and the interactive scheduling information of the power grid system, so as to obtain predicted power supply state information;

[0019] Optimize the operation cost of the power grid system according to the predicted charge and discharge information and the predicted power supply state information, so as to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power grid system;

[0020] Schedule the target electric device according to the target charge and discharge scheduling information, and schedule the power grid system according to the target interactive scheduling information.

[0021] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0022] Obtain the remaining charge of the target electric device when it reaches the charging location, and obtain the system operation information of the power supply grid system deployed corresponding to the charging location;

[0023] Predict the charge and discharge state of the target electric device according to the remaining charge upon arrival and the charge and discharge scheduling information of the target electric device, to obtain predicted charge and discharge information;

[0024] Predict the power supply state of the power supply grid system according to the system operation information and the interactive scheduling information of the power supply grid system, to obtain predicted power supply state information;

[0025] Optimize the operating cost of the power supply grid system according to the predicted charge and discharge information and the predicted power supply state information, to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system;

[0026] Schedule the target electric device according to the target charge and discharge scheduling information, and schedule the power supply grid system according to the target interactive scheduling information.

[0027] In a fifth aspect, the present application also provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0028] Obtain the remaining charge of the target electric device when it reaches the charging location, and obtain the system operation information of the power supply grid system deployed corresponding to the charging location;

[0029] Predict the charge and discharge state of the target electric device according to the remaining charge upon arrival and the charge and discharge scheduling information of the target electric device, to obtain predicted charge and discharge information;

[0030] Predict the power supply state of the power supply grid system according to the system operation information and the interactive scheduling information of the power supply grid system, to obtain predicted power supply state information;

[0031] Optimize the operating cost of the power supply grid system according to the predicted charge and discharge information and the predicted power supply state information, to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system;

[0032] Schedule the target electric device according to the target charge and discharge scheduling information, and schedule the power supply grid system according to the target interactive scheduling information.

[0033] The above grid interaction scheduling method, device, computer device, computer-readable storage medium, and computer program product obtain the remaining power when the target electric device arrives at the charging position, and obtain the system operation information of the power supply grid system deployed corresponding to the charging position; predict the charge and discharge state of the target electric device according to the remaining power when arriving and the charge and discharge scheduling information of the target electric device to obtain predicted charge and discharge information; predict the power supply state of the power supply grid system according to the system operation information and the interaction scheduling information of the power supply grid system to obtain predicted power supply state information; optimize the operation cost of the power supply grid system according to the predicted charge and discharge information and the predicted power supply state information to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system; schedule the target electric device according to the target charge and discharge scheduling information, and schedule the power supply grid system according to the target interaction scheduling information.

[0034] In this way, the remaining power when the target electric device arrives at the charging position and the charge and discharge scheduling information of the target electric device are used as the decision basis for predicting the charge and discharge information, and the system operation information and the interaction scheduling information of the power supply grid system are used as the decision basis for predicting the power supply state information. The charge and discharge scheduling information of the target electric device and the interaction scheduling information of the power supply grid system are controllable variables, and the predicted charge and discharge information and the predicted power supply state information are factors that affect the operation cost of the power supply grid system. Therefore, the minimum operation cost of the power supply grid system can be used as the optimization target, so as to find the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system under the minimum cost of the power supply grid system. Furthermore, based on the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system, the target electric device and the power supply grid system are scheduled respectively. Therefore, the operation cost of the power supply grid system can be reduced. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description in the embodiments of the present application or related technologies. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other related drawings can be obtained without creative efforts.

[0036] Figure 1 It is an application environment diagram of the grid interaction scheduling method in an embodiment;

[0037] Figure 2 It is a flowchart of the grid interaction scheduling method in an embodiment;

[0038] Figure 3 It is a schematic flowchart of the step of predicting the charge-discharge state of a target electric device according to the remaining power upon arrival and the charge-discharge scheduling information of the target electric device to obtain predicted charge-discharge information;

[0039] Figure 4 It is a schematic flowchart of the step of optimizing the operating cost of a power supply grid system according to the predicted charge-discharge information and the predicted power supply state information in an embodiment to obtain the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system;

[0040] Figure 5 It is a schematic flowchart of the step of controlling the driving of a target electric device in an embodiment;

[0041] Figure 6 It is a structural block diagram of a power grid interactive scheduling device in an embodiment;

[0042] Figure 7 It is an internal structure diagram of a computer device in an embodiment. Detailed implementation manners

[0043] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0044] It should be noted that the information (such as system operation information and remaining power upon arrival, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by users or fully authorized by all parties, and the acquisition, transmission, storage, use and processing of relevant data all comply with the relevant provisions of national laws and regulations. For the content pushed to users (such as target charge-discharge scheduling information and target interactive scheduling information, etc.), users can refuse or can conveniently refuse content push, etc. In the embodiments of the present application, some industry-existing solutions such as certain software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of the present application, but it does not mean that the applicant has already or necessarily used this solution.

[0045] The power grid interactive scheduling method provided by the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the target electric device 102, the power supply grid system 104, and the terminal 106 communicate with the server 108 respectively. The data storage system can store the data that the server 108 needs to process. The data storage system can be integrated on the server 108, or placed in the cloud or other network servers. The server 108 obtains the remaining power when the target electric device 102 arrives at the charging location, and obtains the system operation information of the power supply grid system 104 deployed corresponding to the charging location; predicts the charge and discharge state of the target electric device 102 according to the remaining power upon arrival and the charge and discharge scheduling information of the target electric device 102, and obtains the predicted charge and discharge information; predicts the power supply state of the power supply grid system 104 according to the system operation information and the interactive scheduling information of the power supply grid system 104, and obtains the predicted power supply state information; optimizes the operation cost of the power supply grid system 104 according to the predicted charge and discharge information and the predicted power supply state information, and obtains the target charge and discharge scheduling information of the target electric device 102 and the target interactive scheduling information of the power supply grid system 104; schedules the target electric device 102 according to the target charge and discharge scheduling information, and schedules the power supply grid system 104 according to the target interactive scheduling information. The server 108 can also push the target interactive scheduling information and the target charge and discharge scheduling information to the terminal 106. Among them, the terminal 106 can be but is not limited to various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 108 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0046] The power grid interactive scheduling method provided by the embodiments of the present application can also be applied to the following application scenarios. Among them, the power supply grid system 104 and the terminal 106 are respectively in communication with the target electric device 102. The target electric device 102 obtains the remaining power when it arrives at the charging position, and obtains the system operation information of the power supply grid system 104 deployed at the charging position. According to the remaining power when arriving and the charge-discharge scheduling information of the target electric device 102, predict the charge-discharge state of the target electric device 102 to obtain predicted charge-discharge information. According to the system operation information and the interactive scheduling information of the power supply grid system 104, predict the power supply state of the power supply grid system 104 to obtain predicted power supply state information. According to the predicted charge-discharge information and the predicted power supply state information, optimize the operation cost of the power supply grid system 104 to obtain the target charge-discharge scheduling information of the target electric device 102 and the target interactive scheduling information of the power supply grid system 104. According to the target charge-discharge scheduling information, schedule the target electric device 102. Send the target interactive scheduling information to the power supply grid system 104 for the power supply grid system 104 to schedule the power supply grid system 104 according to the target interactive scheduling information.

[0047] The power grid interactive scheduling method provided by the embodiments of the present application can also be applied to the following application scenarios. Among them, the target electric device 102 and the terminal 106 are respectively in communication with the power supply grid system 104. The power supply grid system 104 obtains the remaining power when the target electric device 102 arrives at the charging position, and obtains the system operation information of the power supply grid system 104 deployed at the charging position. According to the remaining power when arriving and the charge-discharge scheduling information of the target electric device 102, predict the charge-discharge state of the target electric device 102 to obtain predicted charge-discharge information. According to the system operation information and the interactive scheduling information of the power supply grid system 104, predict the power supply state of the power supply grid system 104 to obtain predicted power supply state information. According to the predicted charge-discharge information and the predicted power supply state information, optimize the operation cost of the power supply grid system 104 to obtain the target charge-discharge scheduling information of the target electric device 102 and the target interactive scheduling information of the power supply grid system 104. According to the target interactive scheduling information, schedule the power supply grid system 104. Send the target charge-discharge scheduling information to the target electric device 102 for the target electric device 102 to schedule the target electric device 102 according to the target charge-discharge scheduling information.

[0048] In an exemplary embodiment, as Figure 2 shown, a power grid interactive scheduling method is provided. Taking the method applied to the Figure 1 server 108 as an example, the following steps 202 to 210 are included. Among them:

[0049] Step 202: Obtain the remaining power when the target electric device arrives at the charging location, and obtain the system operation information of the power supply grid system deployed corresponding to the charging location.

[0050] Among them, the target electric device in Step 202 is an electric device participating in grid interaction, and the target electric device includes but is not limited to electric vehicles and electric bikes, etc. The system operation information is used to characterize at least one of the power supply status, power storage status, and power generation status of the power supply grid system.

[0051] Exemplarily, obtaining the remaining power when the target electric device arrives at the charging location includes: if it is detected that the target electric device arrives at the charging location, obtain the remaining power sent by the target electric device, and determine the remaining power sent by the target electric device as the remaining power when the target electric device arrives at the charging location.

[0052] Exemplarily, obtaining the system operation information of the power supply grid system deployed corresponding to the charging location includes: obtaining the system operation information sent by the power supply grid system.

[0053] Step 204: Predict the charge and discharge state of the target electric device based on the remaining power upon arrival and the charge and discharge scheduling information of the target electric device to obtain predicted charge and discharge information.

[0054] Exemplarily, Step 204 includes: determining an evaluation power based on the remaining power upon arrival, evaluating the power consumption required for charging and discharging the target electric device according to the charge and discharge scheduling information of the target electric device to obtain a consumption evaluation power; fusing the evaluation power and the consumption evaluation power to obtain predicted charge and discharge information.

[0055] Step 206: Predict the power supply state of the power supply grid system based on the system operation information and the interactive scheduling information of the power supply grid system to obtain predicted power supply state information.

[0056] Exemplarily, Step 206 includes: the power supply grid system includes a power storage system and a power generation system; predicting the output power of the power generation system based on the system operation information to obtain the predicted power generation output power of the power generation system; predicting the state of charge of the power storage system based on the predicted power generation output power to obtain the predicted state of charge of the power storage system; predicting the state of charge of the power storage system after scheduling based on the interactive scheduling information and the predicted state of charge of the power storage system to obtain the predicted scheduled state of charge of the power storage system, and determining the predicted scheduled state of charge of the power storage system as the predicted power supply state information.

[0057] Among them, when the power generation type of the power generation system belongs to the photovoltaic power generation type, the system operation information includes the power generation status information of the power generation system and the charge and discharge information of the energy storage system. The power generation status information includes the power generation environment information of the environment where the power generation system is located and the rated power generation information. The power generation environment information includes at least one of the power generation environment light intensity, the photovoltaic panel temperature, and the power generation environment temperature. The rated power generation information includes the rated power generation output power and the rated environment information. The rated environment information includes at least one of the rated light radiation density and the rated environment temperature.

[0058] Further, according to the system operation information, the output power of the power generation system is predicted to obtain the predicted power generation output power of the power generation system, including: according to the power generation environment information of the environment where the power generation system is located, the rated power generation information of the power generation system is corrected to obtain the predicted power generation output power of the power generation system.

[0059] As an embodiment, according to the power generation environment information of the environment where the power generation system is located, the rated power generation information of the power generation system is corrected to obtain the predicted power generation output power of the power generation system, including: according to the difference between the rated environment information and the power generation environment information of the power generation system, the correction coefficient corresponding to the rated power generation output power of the power generation system is determined, where the greater the difference between the rated environment information and the power generation environment information of the power generation system, the greater the determined correction coefficient; according to the correction coefficient corresponding to the rated power generation output power of the power generation system, the rated power generation information of the power generation system is corrected to obtain the predicted power generation output power of the power generation system.

[0060] Further, according to the difference between the rated environment information and the power generation environment information of the power generation system, determining the correction coefficient corresponding to the rated power generation output power of the power generation system includes: obtaining the power temperature coefficient and the temperature difference between the photovoltaic panel temperature and the rated environment temperature, multiplying the power temperature coefficient by the temperature difference to determine the power temperature value, and determining the sum value between 1 and the power temperature correction value as the power temperature correction value; determining the ratio between the power generation environment light intensity and the rated light radiation density as the light difference, and multiplying the light difference by the power temperature correction value to determine the correction coefficient corresponding to the rated power generation output power of the power generation system.

[0061] As an embodiment, according to the correction coefficient corresponding to the rated power generation output power of the power generation system, the rated power generation information of the power generation system is corrected to obtain the predicted power generation output power of the power generation system, including: multiplying the correction coefficient corresponding to the rated power generation output power of the power generation system by the rated power generation output power of the power generation system to determine the predicted power generation output power of the power generation system.

[0062] Among them, the photovoltaic panel temperature can be detected by a temperature sensor deployed on the photovoltaic panel, or can be determined by the power generation status information.

[0063] Optionally, the process of determining the temperature of the photovoltaic panel from the power generation status information includes: the power generation status information further includes the volume of the photovoltaic panel. Based on the volume of the photovoltaic panel, the light intensity of the power generation environment, and the temperature of the power generation environment, the degree to which the photovoltaic panel absorbs the ambient temperature is evaluated to obtain the temperature absorption coefficient of the photovoltaic panel. Based on the temperature absorption coefficient of the photovoltaic panel and the temperature of the power generation environment, the temperature of the photovoltaic panel is determined. Specifically, the sum of the temperature absorption coefficient of the photovoltaic panel and the temperature of the power generation environment is determined as the temperature of the photovoltaic panel.

[0064] Among them, the larger the volume of the photovoltaic panel, the smaller the evaluated temperature absorption coefficient of the photovoltaic panel; the greater the light intensity of the power generation environment, the greater the evaluated temperature absorption coefficient of the photovoltaic panel; the higher the temperature of the power generation environment, the greater the evaluated temperature absorption coefficient of the photovoltaic panel.

[0065] As an embodiment, the sum of the temperature absorption coefficient of the photovoltaic panel and the temperature of the power generation environment is determined as the temperature of the photovoltaic panel, and is expressed by the formula:

[0066]

[0067] Among them, is the temperature of the photovoltaic panel at time is the temperature of the power generation environment at time is the volume of the photovoltaic panel at time is the light intensity of the power generation environment at time

[0068] Among them, the higher the predicted power generation output, the higher the remaining stored electricity of the energy storage system characterized by the predicted state of charge of the predicted stored charge.

[0069] As an embodiment, based on the interactive scheduling information and the predicted state of charge of the stored charge, the state of charge of the energy storage system after scheduling is predicted to obtain the predicted scheduled state of charge of the energy storage system, including: the interactive scheduling information is used to characterize the charge and discharge state of the energy storage system; the charge and discharge efficiency of the energy storage system and the total power of the energy storage system are obtained; based on the interactive scheduling information, the total power of the energy storage system, and the charge and discharge efficiency, the power adjustment status of the energy storage system under interactive scheduling is evaluated to obtain the interactive adjustment power; based on the interactive adjustment power, the state of charge of the energy storage system before scheduling is adjusted to obtain the predicted scheduled state of charge of the energy storage system. Specifically, the sum value between the interactive adjustment power and the state of charge of the energy storage system before scheduling is determined as the predicted scheduled state of charge of the energy storage system.

[0070] As an embodiment, according to the interactive scheduling information, the total power of the energy storage system, and the charge and discharge efficiency, the power adjustment status of the energy storage system under interactive scheduling is evaluated to obtain the interactive adjustment power, including: The interactive scheduling information includes the second charging power and the second discharging power of the energy storage system; the charge and discharge efficiency of the energy storage system includes the second charging efficiency and the second discharging efficiency; According to the second charging power, the second charging efficiency, and the total power of the energy storage system, the power adjustment status of the energy storage system under charging scheduling is evaluated to obtain the charging adjustment power, where the higher the second charging power, the higher the charging adjustment power, the higher the second charging efficiency, the higher the charging adjustment power, and the lower the total power of the energy storage system, the higher the charging adjustment power; According to the second discharging power, the second discharging efficiency, and the total power of the energy storage system, the power adjustment status of the energy storage system under discharging scheduling is evaluated to obtain the discharging adjustment power, where the higher the second discharging efficiency, the higher the discharging adjustment power, the higher the second discharging efficiency, the lower the discharging adjustment power, and the higher the total power of the energy storage system, the lower the discharging adjustment power; The difference between the charging adjustment power and the discharging adjustment power is determined as the interactive adjustment power.

[0071] Optionally, the sum value between the interactive adjustment power and the state of charge of the energy storage system before scheduling is determined as the predicted scheduling state of charge of the energy storage system. Expressed by the formula:

[0072]

[0073] Where is the predicted scheduling state of charge of the energy storage system after scheduling at time is the state of charge of the energy storage system before scheduling at time is the second charging efficiency, is the second charging power of the energy storage system at time is the total power of the energy storage system, is the second discharging power of the energy storage system at time is the second discharging efficiency.

[0074] Step 208, according to the predicted charge and discharge information and the predicted power supply status information, optimize the operating cost of the power supply grid system to obtain the target charge and discharge scheduling information of the target electric equipment and the target interactive scheduling information of the power supply grid system.

[0075] As an embodiment, step 208 includes: predicting the operating cost of the power supply grid system based on the predicted charge-discharge information and the predicted power supply status information to obtain the predicted operating costs under each set of scheduling information, where each set of scheduling information includes a charge-discharge scheduling information and an interaction scheduling information; taking the minimum value of the predicted operating costs as the optimization objective, performing optimization among each set of scheduling information to obtain the target scheduling information, determining the charge-discharge scheduling information in the target scheduling information as the target charge-discharge scheduling information of the target electric device, and determining the interaction scheduling information in the target scheduling information as the target interaction scheduling information of the power supply grid system.

[0076] As another embodiment, step 208 includes: constructing the relationships between the charge-discharge scheduling information and the interaction scheduling information and the operating cost of the power supply grid system respectively based on the predicted charge-discharge information and the predicted power supply status information; performing optimization with the operating cost of the power supply grid system according to the relationships between the charge-discharge scheduling information and the interaction scheduling information and the operating cost of the power supply grid system respectively to obtain the target charge-discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system.

[0077] Step 210, scheduling the target electric device according to the target charge-discharge scheduling information, and scheduling the power supply grid system according to the target interaction scheduling information.

[0078] As an embodiment, scheduling the target electric device according to the target charge-discharge scheduling information includes: sending the target charge-discharge scheduling information to the target electric device for the target electric device to perform scheduling according to the target charge-discharge scheduling information; or determining the first control instruction corresponding to the target electric device according to the target charge-discharge scheduling information and sending the first control instruction to the target electric device for the target electric device to perform scheduling according to the first control instruction.

[0079] As an embodiment, scheduling the power supply grid system according to the target interaction scheduling information includes: sending the target interaction scheduling information to the power supply grid system for the power supply grid system to perform scheduling according to the target interaction scheduling information; or determining the second control instruction corresponding to the power supply grid system according to the target interaction scheduling information and sending the second control instruction to the target electric device for the power supply grid system to perform scheduling according to the second control instruction.

[0080] In the above grid interactive scheduling method, the remaining arrival power of the target electric device when it reaches the charging position and the charge-discharge scheduling information of the target electric device are used as the decision basis for predicting the charge-discharge information, and the system operation information and the interactive scheduling information of the power supply grid system are used as the decision basis for predicting the power supply state information. The charge-discharge scheduling information of the target electric device and the interactive scheduling information of the power supply grid system are controllable variables, and the predicted charge-discharge information and the predicted power supply state information are factors that affect the operating cost of the power supply grid system. Therefore, the minimum operating cost of the power supply grid system can be used as the optimization target to find the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system under the minimum cost of the power supply grid system. Then, based on the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system, the target electric device and the power supply grid system are scheduled respectively. Therefore, the operating cost of the power supply grid system can be reduced.

[0081] In an exemplary embodiment, as Figure 3 shown, to provide an accurate prediction method for the charge-discharge state of the target electric device, according to the remaining arrival power and the charge-discharge scheduling information of the target electric device, the charge-discharge state of the target electric device is predicted, and the obtained predicted charge-discharge information includes steps 302 to 308. Among them:

[0082] Step 302, obtain the loss coefficient corresponding to the target electric device during charge and discharge, where the loss coefficient is used to characterize the degree of power loss of the target electric device during charge and discharge.

[0083] Exemplarily, step 302 includes: obtaining the device information of the target electric device, where the device information is used to characterize the operating condition of the target electric device, and the device information includes at least one of the device model, device type, device brand, and device loss rate; according to the device information of the target electric device, determine the loss coefficient corresponding to the target electric device during charge and discharge, where the better the operating condition of the target electric device characterized by the device information of the target electric device, the lower the determined loss coefficient corresponding to the target electric device during charge and discharge; the lower the loss coefficient corresponding to the target electric device during charge and discharge, the lower the degree of power loss of the target electric device during charge and discharge.

[0084] Step 304, evaluate the power loss of the target electric device during charge and discharge according to the remaining arrival power and the loss coefficient, and obtain the loss evaluation power.

[0085] Among them, the loss evaluation power in step 304 refers to the remaining power of the target electric device after considering the power loss of the target electric device during charge and discharge, that is, the loss evaluation power is used to characterize the actual remaining power of the target electric device.

[0086] Exemplarily, step 304 includes: correcting the remaining power upon arrival according to the loss coefficient to obtain the loss evaluation power.

[0087] As an embodiment, correcting the remaining power upon arrival according to the loss coefficient to obtain the loss evaluation power includes: determining the product between the loss coefficient and the remaining power upon arrival as the loss evaluation power.

[0088] Optionally, after step 304, the method further includes: for the remaining power of the target electric device at any moment, determining the product between the loss coefficient and the remaining power of the target electric device at any moment as the loss evaluation power of the target electric device at any moment.

[0089] Optionally, determining the product between the loss coefficient and the remaining power of the target electric device at any moment as the loss evaluation power of the target electric device at any moment can be expressed by the formula:

[0090]

[0091] where is the loss evaluation power of the target electric device at moment is the remaining power of the target electric device at moment is the loss coefficient.

[0092] Step 306, evaluate the power consumed when charging and discharging the target electric device according to the charge and discharge scheduling information of the target electric device to obtain the consumption evaluation power.

[0093] Exemplarily, step 306 includes: obtaining the charge and discharge efficiency of the target electric device, and evaluating the power consumed when charging and discharging the target electric device according to the charge and discharge scheduling information and the charge and discharge efficiency of the target electric device to obtain the consumption evaluation power.

[0094] Furthermore, obtaining the charge and discharge efficiency of the target electric device includes: determining the charge and discharge efficiency of the target electric device according to the device information of the target electric device, where the better the device operation condition of the target electric device characterized by the device information of the target electric device, the higher the determined charge and discharge efficiency of the target electric device.

[0095] As an embodiment, the power consumption assessment power includes discharge power consumption assessment power and charging power consumption assessment power. The charge-discharge efficiency of the target electric device includes a first charging efficiency and a first discharge efficiency. The charge-discharge scheduling information of the target electric device includes a first charging power and a first discharge power. According to the charge-discharge scheduling information and the charge-discharge efficiency of the target electric device, the power required for charging and discharging the target electric device is evaluated to obtain the power consumption assessment power, including: determining the product between the first charging power and the first charging efficiency as the charging power consumption assessment power; determining the ratio between the first discharge power and the first discharge power as the discharge power consumption assessment power.

[0096] Step 308: Fuse the loss assessment power consumption and the power consumption assessment power to obtain predicted charge-discharge information.

[0097] As an embodiment, the predicted charge-discharge information includes predicted charging power consumption and predicted discharging power consumption. Step 308 includes: determining the sum of the loss assessment power consumption and the charging power consumption assessment power during the charging process of the target electric device as the predicted charging power consumption; determining the sum of the loss assessment power consumption and the discharging power consumption assessment power during the discharging process of the target electric device as the predicted discharging power consumption.

[0098] Optionally, expressing the determination of the sum of the loss assessment power consumption and the charging power consumption assessment power during the charging process of the target electric device as the predicted charging power consumption with a formula can be:

[0099]

[0100] Where is the predicted charging power consumption, is the target electric device at the first charging power when charging at time is the charging efficiency.

[0101] Optionally, expressing the determination of the sum of the loss assessment power consumption and the discharging power consumption assessment power during the discharging process of the target electric device as the predicted discharging power consumption with a formula can be:

[0102]

[0103] Where is the predicted discharging power consumption, is the target electric device at the first discharging power when discharging at time is the discharging efficiency.

[0104] In this embodiment, the loss coefficient corresponding to the target electric device during charging and discharging is obtained, where the loss coefficient is used to characterize the degree of power loss of the target electric device during charging and discharging; according to the remaining power and the loss coefficient, the power loss during charging and discharging of the target electric device is evaluated to obtain the loss evaluation power; according to the charging and discharging scheduling information of the target electric device, the power consumption required for charging and discharging the target electric device is evaluated to obtain the consumption evaluation power; the loss evaluation power and the consumption evaluation power are fused to obtain the predicted charging and discharging information. In this way, considering the influence of the different device properties and different usage conditions of the target electric device on the charging and discharging loss of the target electric device, therefore, the loss coefficient and the remaining power are used as the basis to evaluate the power loss during charging and discharging of the target electric device to obtain the loss evaluation power; based on the charging and discharging scheduling information, the power consumption required for charging and discharging the target electric device is evaluated to obtain the consumption evaluation power; finally, the loss evaluation power and the consumption evaluation power are fused to obtain the predicted charging and discharging information, so the accuracy of the charging and discharging prediction of the target electric device is improved.

[0105] In an exemplary embodiment, as Figure 4 shown, to provide a way to accurately optimize the operating cost of the power supply grid system, according to the predicted charging and discharging information and the predicted power supply state information, the operating cost of the power supply grid system is optimized to obtain the target charging and discharging scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system, including steps 402 to 404. Among them:

[0106] Step 402, obtain the first constraint condition of the target electric device and the second constraint condition of the power supply grid system, where the first constraint condition is used to constrain the charging and discharging state of the target electric device, and the second constraint condition is used to constrain the interaction state of the power supply grid system.

[0107] Among them, the first constraint condition in step 402 can be used to limit that the target electric device does not perform the charging and discharging processes simultaneously, and the first constraint condition can also be used to constrain that the interaction charge state of the target electric device after interacting with the power supply grid system is the same as the expected charge state of the target electric device before interacting with the power supply grid system.

[0108] As an embodiment, the first constraint condition includes a charging and discharging process constraint condition and an interaction charge constraint condition.

[0109] Optionally, the charging and discharging process constraint condition can be expressed by the formula:

[0110]

[0111]

[0112] Among them, is the maximum charging power of the target electric device, is the maximum discharging power of the target electric device.

[0113] Optionally, the interactive charge constraint condition can be expressed by the formula:

[0114]

[0115]

[0116]

[0117] Among them, is the state of charge of the target electric device at time is the maximum state of charge of the target electric device at time is the minimum state of charge of the target electric device at time is the state of charge of the target electric device when it reaches the charging position, is the time when the target electric device reaches the charging position, is the state of charge of the target electric device when it leaves the charging position, is the time when the target electric device leaves the charging position.

[0118] Among them, the second constraint condition in step 402 includes at least one of a power balance constraint condition, an interactive constraint condition, and an energy storage constraint condition. The power balance constraint condition is used to ensure the balance between the overall charging power and the overall discharging power of the power supply grid system (specifically, the overall charging power of the power supply grid system is equal to the overall discharging power). The interactive constraint condition is used to restrict the power supply grid system from simultaneously selling electricity and buying electricity. The energy storage constraint condition is used to restrict the energy storage system from simultaneously charging and discharging.

[0119] Optionally, the power balance constraint condition can be expressed by the formula:

[0120]

[0121] Among them, is the power generation power of the power supply grid system, is the load power of the power supply grid system, is the power of the power supply grid system to buy electricity from other power grids, is the power of the power supply grid system to sell electricity to other power grids.

[0122] Optionally, the interactive constraint condition can be expressed by the formula:

[0123]

[0124]

[0125] Among them, is the electricity purchase status variable, is the electricity sale status variable, is the maximum power for the power supply grid system to purchase electricity from other power grids, is the maximum power for the power supply grid system to sell electricity to other power grids.

[0126] Optionally, the energy storage constraint condition can be expressed by the formula:

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] Among them, is the minimum power generation of the power supply grid system, is the maximum power generation of the power supply grid system, is the minimum state of charge of the energy storage system, is the maximum state of charge of the energy storage system, is the charge state variable of the energy storage system, is the discharge state variable of the energy storage system.

[0133] Step 404: Optimize the operating cost of the power supply grid system according to the first constraint condition, the second constraint condition, the predicted charge and discharge information, and the predicted power supply status information, to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system.

[0134] Exemplarily, step 404 includes: constructing the relationship between both the predicted charge and discharge information and the predicted power supply status information and the operating cost of the power supply grid system, and optimizing the predicted charge and discharge information and the predicted power supply status information with the minimum operating cost of the power supply grid system as the optimization target according to the relationship between both the predicted charge and discharge information and the predicted power supply status information and the operating cost of the power supply grid system, the first constraint condition, and the second constraint condition, to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power supply grid system.

[0135] Among them, the operating cost of the power supply grid system consists of the equipment power purchase cost, the power consumption revenue, and the grid power purchase and sale cost. The equipment power purchase cost is used to represent the cost of the power supply grid system purchasing electricity from the target electric equipment, and the power consumption revenue is used to represent the load power consumption revenue when the power supply grid system supplies electricity; the grid power purchase and sale cost is used to represent the cost when the power supply grid system conducts power purchase and sale transactions with other power grids.

[0136] Among them, the higher the equipment power purchase cost, the higher the operating cost; the higher the power consumption revenue, the lower the operating cost; the higher the grid power purchase and sale cost, the higher the operating cost. The higher the first discharge power of the target electric equipment, the higher the equipment power purchase cost, and the higher the first charging power of the target electric equipment, the lower the equipment power purchase cost.

[0137] Optionally, the determination process of the equipment power purchase cost can be expressed by the formula:

[0138]

[0139] Among them, is the equipment power purchase cost, is the charging electricity price of the target electric equipment, is the discharging electricity price of the target electric equipment.

[0140] Optionally, the determination process of the grid power purchase and sale cost can be expressed by the formula:

[0141]

[0142] Among them, is the grid power purchase and sale cost, is the electricity price for the power supply grid system to purchase electricity from other power grids, is the electricity price for the power supply grid system to sell electricity to other power grids.

[0143] Optionally, the determination process of the power consumption revenue can be expressed by the formula:

[0144]

[0145] Among them, is the power consumption revenue, is the load power consumption electricity price.

[0146] Optionally, the determination process of the operating cost can be expressed by the formula:

[0147]

[0148] Among them, is the operating cost.

[0149] In this embodiment, the first constraint condition of the target electric device and the second constraint condition of the power grid system are obtained. The first constraint condition is used to constrain the charge and discharge state of the target electric device, and the second constraint condition is used to constrain the interaction state of the power grid system. According to the first constraint condition, the second constraint condition, the predicted charge and discharge information, and the predicted power supply state information, the operating cost of the power grid system is optimized to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power grid system. Considering that both the target electric device and the power grid system have operating limitations, the first constraint condition, the second constraint condition, the predicted charge and discharge information, and the predicted power supply state information are used as the basis, and the operating cost of the power grid system is optimized. Thus, the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power grid system that minimize the operating cost of the power grid system and do not exceed the operating limitations of the target electric device and the power grid system can be obtained. Therefore, the determination accuracy of the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power grid system is improved.

[0150] In an exemplary embodiment, as Figure 5 shown, to provide an accurate way to control the driving of the target electric device, the method further includes steps 502 to 510. Among them:

[0151] Step 502, obtain the traffic road network information of the road corresponding to the target electric device, and obtain the device operation information of the target electric device.

[0152] Among them, the traffic road network information in step 502 includes at least one of road length, traffic flow, road slope, and congestion parameter. The traffic flow is used to represent the total number of vehicles passing by in a certain period of time. The road slope is used to represent the inclination degree of the road. The congestion parameter is used to represent the probability of vehicle congestion and the severity of congestion in a certain period of time. Specifically, the higher the probability of vehicle congestion, the larger the congestion parameter, and the higher the severity of congestion, the larger the congestion parameter.

[0153] Among them, the device operation information in step 502 includes at least one of device driving speed, device weight, and device windward area.

[0154] Step 504, according to the traffic road network information, predict the road resistance suffered by the target electric device when driving on each preset driving path, and obtain the driving road resistance information corresponding to each preset driving path.

[0155] As an embodiment, step 504 includes: for each preset driving route, screening the driving road network information belonging to the preset driving route from the traffic road network information; respectively performing non-linear exponential processing on each dimension information in the driving road network information according to the importance degree of each dimension information in the driving road network information for the road resistance determination process, to obtain the road network processing information corresponding to each dimension information; according to the weight coefficients corresponding to each dimension information in the driving road network information, performing weighted fusion on the road network processing information corresponding to each dimension information, to obtain the driving road resistance information corresponding to the preset driving route, where the driving road resistance information includes the driving road resistance.

[0156] Further, respectively performing non-linear exponential processing on each dimension information in the driving road network information according to the importance degree of each dimension information in the driving road network information for the road resistance determination process, to obtain the road network processing information corresponding to each dimension information, includes: for each dimension information in the driving road network information, determining the non-linear exponent corresponding to the dimension information according to the importance degree of the dimension information for the road resistance determination process, where the higher the importance degree of each dimension information in the driving road network information for the road resistance determination process, the larger the non-linear exponent corresponding to the dimension information; taking the dimension information as the base number and the non-linear exponent corresponding to the dimension information as the power, performing exponential power operation, to obtain the road network processing information corresponding to the dimension information.

[0157] Optionally, according to the weight coefficients corresponding to each dimension information in the driving road network information, performing weighted fusion on the road network processing information corresponding to each dimension information, to obtain the driving road resistance information corresponding to the preset driving route, which can be expressed by the formula as:

[0158]

[0159] Wherein, is the driving road resistance corresponding to the preset driving route, is the weight coefficient of the road length corresponding to the preset driving route, is the road length corresponding to the preset driving route, is the non-linear exponent corresponding to the road length, is the weight coefficient of the traffic flow corresponding to the preset driving route, is the traffic flow corresponding to the preset driving route, is the non-linear exponent corresponding to the traffic flow, is the weight coefficient of the road slope corresponding to the preset driving route, is the road slope corresponding to the preset driving route, is the non-linear exponent corresponding to the road slope, is the weight coefficient of the congestion parameter corresponding to the preset driving route, is the non-linear exponent corresponding to the congestion parameter.

[0160] Step 506: Optimize the road resistance suffered by the target electric device according to the driving road resistance information corresponding to each preset driving path, and obtain the target driving path corresponding to the target electric device.

[0161] Exemplarily, step 506 includes: Selecting the target driving path with the smallest driving road resistance from each preset driving path.

[0162] As an embodiment, step 506 includes: Constructing a driving path matrix with the driving positions corresponding to each preset driving path, where each vector in the driving path matrix is respectively matched with the driving position corresponding to each preset driving path, and the preset driving path corresponding to the vector can be connected end to end with the preset driving path corresponding to its adjacent vector. For example, vector A is adjacent to vector B, and the end point of the driving path A corresponding to vector A is the starting point of the driving path B corresponding to vector B; Taking the sum of the smallest driving road resistances as the optimization target, optimize the vectors in the driving path matrix in sequence to obtain the target driving path, where the target driving path includes multiple target driving sub-paths, and the multiple target driving sub-paths are connected end to end in sequence.

[0163] Optionally, the method for optimizing the vectors in the driving path matrix to obtain the target driving path can be the Dijkstra algorithm or other algorithms, which is not limited herein.

[0164] Step 508: Optimize the driving cost corresponding to the target electric device according to the device operation information and the target driving path, and obtain the target driving information corresponding to the target electric device.

[0165] As an embodiment, step 508 includes: Obtaining the driving environment information of the driving environment where the target electric device is located; Evaluating the environmental resistance suffered by the target electric device during driving according to the driving environment information to obtain the environmental resistance information; Evaluating the power consumed by the target electric device during operation according to the environmental resistance information and the device operation information to obtain the device consumption evaluation power; Evaluating the driving cost of the target electric device during operation according to the device consumption evaluation power and the target driving path to obtain the driving cost corresponding to the target electric device, and optimizing the driving cost to obtain the target driving information corresponding to the target electric device.

[0166] Wherein, the driving environment information includes at least one of the driving air density, the driving air resistance coefficient, the driving road inclination angle, and the road sliding friction coefficient.

[0167] As an embodiment, according to the driving environment information, the environmental resistance suffered by the target electric device during driving is evaluated to obtain environmental resistance information, including: evaluating the air resistance suffered by the target electric device when driving on the target driving path according to the driving environment information and the device operation information to obtain the environmental air resistance; evaluating the ramp resistance suffered by the target electric device when driving on the target driving path according to the driving environment information and the device operation information to obtain the environmental ramp resistance; evaluating the road friction resistance suffered by the target electric device when driving on the target driving path according to the driving environment information and the device operation information to obtain the environmental friction resistance; and fusing the environmental air resistance, the environmental ramp resistance, and the environmental friction resistance to obtain the environmental resistance information.

[0168] Among them, the greater the air density suffered by the target electric device when driving on the target driving path, the greater the evaluated environmental air resistance; the greater the air resistance coefficient suffered by the target electric device when driving on the target driving path, the greater the evaluated environmental air resistance; the larger the equipment windward area corresponding to the target electric device when driving on the target driving path, the greater the evaluated environmental air resistance; the greater the equipment driving speed corresponding to the target electric device when driving on the target driving path, the greater the evaluated environmental air resistance.

[0169] Optionally, evaluating the air resistance suffered by the target electric device when driving on the target driving path according to the driving environment information and the device operation information, the environmental air resistance can be expressed by the formula:

[0170]

[0171] Among them, is the air resistance suffered by the target electric device when driving on the target driving path at time is the air density suffered by the target electric device when driving on the target driving path, is the air resistance coefficient suffered by the target electric device when driving on the target driving path, is the equipment windward area corresponding to the target electric device when driving on the target driving path, is the equipment driving speed corresponding to the target electric device when driving on the target driving path.

[0172] Among them, the greater the equipment weight of the target electric device, the greater the evaluated environmental ramp resistance; the greater the road inclination angle corresponding to the target electric device when driving on the target driving path, the greater the evaluated environmental ramp resistance.

[0173] Optionally, based on the driving environment information and the device operation information, evaluate the ramp resistance suffered by the target electric device when traveling on the target driving path. The environmental ramp resistance obtained can be expressed by the formula:

[0174]

[0175] Wherein, is the environmental ramp resistance suffered by the target electric device when traveling on the target driving path at time is the equipment weight of the target electric device, is the road inclination angle corresponding to the target electric device when traveling on the target driving path at time

[0176] Wherein, the larger the road sliding friction coefficient corresponding to the target electric device when traveling on the target driving path, the greater the evaluated road friction resistance; the greater the equipment weight of the target electric device, the greater the evaluated road friction resistance; the greater the road inclination angle corresponding to the target electric device when traveling on the target driving path, the smaller the evaluated road friction resistance.

[0177] Optionally, based on the driving environment information and the device operation information, evaluate the road friction resistance suffered by the target electric device when traveling on the target driving path. The environmental friction resistance obtained can be expressed by the formula:

[0178]

[0179] Wherein, is the environmental friction resistance suffered by the target electric device when traveling on the target driving path at time is the road sliding friction coefficient corresponding to the target electric device when traveling on the target driving path.

[0180] As an embodiment, based on the environmental resistance information and the device operation information, evaluate the power consumed by the target electric device during operation to obtain the device consumption evaluation power, including: determining the travel duration corresponding to the target electric device when traveling on the target driving path according to the device travel speed corresponding to the target electric device when traveling on the target driving path and the road length of the target driving path; evaluating the power consumed by the target electric device during operation according to the environmental resistance information, the device travel speed corresponding to the target electric device when traveling on the target driving path, and the travel duration corresponding to the target electric device when traveling on the target driving path to obtain the device consumption evaluation power.

[0181] Among them, the greater the environmental resistance characterized by the environmental resistance information, the greater the evaluation power consumed by the device, the greater the device driving speed corresponding to the target electric device traveling on the target driving path, the greater the evaluation power consumed by the device, and the longer the driving duration corresponding to the target electric device traveling on the target driving path, the greater the evaluation power consumed by the device.

[0182] As an embodiment, the environmental air resistance, the environmental ramp resistance, and the environmental friction resistance are integrated to obtain environmental resistance information, including: evaluating the environmental traction force received by the target electric device when traveling on the target driving path according to the device driving speed corresponding to the target electric device traveling on the target driving path and the device weight of the target electric device to obtain the environmental traction force; integrating the environmental traction force, the environmental air resistance, the environmental ramp resistance, and the environmental friction resistance to obtain environmental resistance information. Specifically, the sum of the environmental traction force, the environmental air resistance, the environmental ramp resistance, and the environmental friction resistance is determined as the environmental resistance information.

[0183] Further, evaluating the environmental traction force received by the target electric device when traveling on the target driving path according to the device driving speed corresponding to the target electric device traveling on the target driving path and the device weight of the target electric device to obtain the environmental traction force includes: evaluating the degree of speed change of the target electric device when traveling on the target driving path according to the device driving speed corresponding to the target electric device traveling on the target driving path to obtain the speed change rate of the target electric device when traveling on the target driving path; determining the product of the speed change rate of the target electric device when traveling on the target driving path and the device weight of the target electric device as the environmental traction force.

[0184] Optionally, expressing the sum of the environmental traction force, the environmental air resistance, the environmental ramp resistance, and the environmental friction resistance as the environmental resistance information with a formula can be:

[0185]

[0186] Among them, is the environmental resistance information received by the target electric device when traveling on the target driving path at time.

[0187] Among them, the greater the device driving speed corresponding to the target electric device traveling on the target driving path,

[0188] Optionally, evaluating the power consumed by the target electric device during operation according to the environmental resistance information, the device driving speed corresponding to the target electric device traveling on the target driving path, and the driving duration corresponding to the target electric device traveling on the target driving path to obtain the evaluation power consumed by the device, which can be expressed by the formula:

[0189]

[0190] Among them, is the device consumption evaluation power when the target electric device travels on the target travel path at a certain moment, is the environmental resistance information suffered by the target electric device when traveling on the target travel path at a certain moment, is the travel duration corresponding to when the target electric device travels on the target travel path.

[0191] As an embodiment, according to the device consumption evaluation power and the target travel path, the travel cost during the operation of the target electric device is evaluated to obtain the travel cost corresponding to the target electric device, including: according to the target travel path, the time cost during the operation of the target electric device is evaluated to obtain the travel time cost corresponding to the target electric device; according to the device consumption evaluation power, the energy consumption cost when the target electric device travels on the target travel path is evaluated to obtain the energy consumption cost corresponding to the target electric device; the travel time cost and the energy consumption cost are fused to obtain the travel cost corresponding to the target electric device. Specifically, the sum of the travel time cost and the energy consumption cost is determined as the travel cost corresponding to the target electric device.

[0192] As an embodiment, according to the target travel path, the time cost during the operation of the target electric device is evaluated to obtain the travel time cost corresponding to the target electric device, including: according to the device travel speed corresponding to when the target electric device travels on the target travel path and the road length of the target travel path, the travel duration corresponding to when the target electric device travels on the target travel path is determined; the product between the time coefficient and the travel duration corresponding to when the target electric device travels on the target travel path is determined as the travel time cost corresponding to the target electric device.

[0193] Optionally, the product between the time coefficient and the travel duration corresponding to when the target electric device travels on the target travel path is determined as the travel time cost corresponding to the target electric device, which can be expressed by the formula:

[0194]

[0195] Among them, is the travel time cost corresponding to the target electric device, is the time coefficient, is the road length of the target travel path, is the device travel speed corresponding to when the target electric device travels on the target travel path.

[0196] As an embodiment, the energy consumption cost of a target electric device when traveling on a target travel path is evaluated according to the device consumption evaluation power, and the energy consumption cost corresponding to the target electric device is obtained, including: obtaining the component consumption evaluation power of the auxiliary components of the target electric device, where the auxiliary components are components that implement the auxiliary functions of the target electric device, for example, lights, air conditioners, stereos, etc.; fusing the component consumption evaluation power and the device consumption evaluation power to obtain the total consumption power; and determining the product of the electricity price and the total consumption power as the energy consumption cost corresponding to the target electric device.

[0197] Optionally, the product of the electricity price and the total consumption power is determined as the energy consumption cost corresponding to the target electric device, and can be expressed by the formula:

[0198]

[0199] Where is the energy consumption cost corresponding to the target electric device, is the electricity price, is the component consumption evaluation power.

[0200] As an embodiment, the travel cost is optimized to obtain the target travel information corresponding to the target electric device, including: taking the minimum travel cost as the optimization target, optimizing the device travel speed of the target electric device to obtain the target travel speed of the target electric device, and determining the target travel speed as the target travel information corresponding to the target electric device.

[0201] Step 510, perform travel control on the target electric device according to the target travel path and the target travel information.

[0202] Exemplarily, step 510 includes: sending the target travel path and the target travel information to the target electric device for the target electric device to perform travel control on the target electric device according to the target travel path and the target travel information; or, determining a first control instruction according to the target travel path and the target travel information, and sending the first control instruction to the target electric device for the target electric device to perform travel control on the target electric device according to the first control instruction.

[0203] In this embodiment, by obtaining the traffic road network information of the road corresponding to the target electric device and the device operation information of the target electric device; according to the traffic road network information, predicting the road resistance suffered by the target electric device when driving on each preset driving path respectively, and obtaining the driving road resistance information corresponding to each preset driving path; according to the driving road resistance information corresponding to each preset driving path, optimizing the road resistance suffered by the target electric device to obtain the target driving path corresponding to the target electric device; according to the device operation information and the target driving path, optimizing the driving cost corresponding to the target electric device to obtain the target driving information corresponding to the target electric device; according to the target driving path and the target driving information, controlling the driving of the target electric device, considering the influence of the road resistance corresponding to each preset driving path on the driving of the target electric device, so as to take the road resistance as the optimization target, so that the obtained target driving path has the smallest road resistance, and based on the target driving path and the device operation information, and taking the driving cost as the optimization target, so that the obtained target driving information has the smallest driving cost. Therefore, it is ensured that the operation control of the target electric device is the most fuel-efficient and the corresponding driving route belongs to the optimal route, so as to ensure that the target electric device can have more energy participating in the power supply grid system, and further reduce the operation cost of the power supply grid system.

[0204] As a detailed embodiment, obtain the remaining power when the target electric device arrives at the charging position, and obtain the system operation information of the power supply grid system deployed at the charging position; obtain the loss coefficient corresponding to the target electric device during charge and discharge, where the loss coefficient is used to characterize the degree of power loss of the target electric device during charge and discharge; evaluate the power loss during charge and discharge of the target electric device according to the remaining power upon arrival and the loss coefficient to obtain the loss evaluation power; evaluate the power to be consumed during charge and discharge of the target electric device according to the charge and discharge scheduling information of the target electric device to obtain the consumption evaluation power; fuse the loss evaluation power and the consumption evaluation power to obtain the predicted charge and discharge information; predict the output power of the power generation system according to the system operation information to obtain the predicted power generation output power of the power generation system.

[0205] Further, according to the predicted power generation output, the state of charge of the energy storage system is predicted to obtain the predicted state of charge of the energy storage; according to the interactive scheduling information and the predicted state of charge of the energy storage, the state of charge of the energy storage system after scheduling is predicted to obtain the predicted scheduled state of charge of the energy storage system, and the predicted scheduled state of charge is determined as the predicted power supply state information; the first constraint condition of the target electric device and the second constraint condition of the power supply grid system are obtained, wherein the first constraint condition is used to constrain the charge and discharge state of the target electric device, and the second constraint condition is used to constrain the interactive state of the power supply grid system; according to the first constraint condition, the second constraint condition, the predicted charge and discharge information, and the predicted power supply state information, the operating cost of the power supply grid system is optimized to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system.

[0206] Further, the traffic road network information corresponding to the driving road of the target electric device is obtained, and the device operation information of the target electric device is obtained; for each preset driving path, the driving road network information belonging to the preset driving path is screened from the traffic road network information; according to the importance degree of each dimension information in the driving road network information for the road resistance determination process, the non-linear exponential processing is respectively performed on each dimension information in the driving road network information to obtain the road network processing information corresponding to each dimension information; according to the weight coefficient corresponding to each dimension information in the driving road network information, the road network processing information corresponding to each dimension information is weighted and fused to obtain the driving road resistance information corresponding to the preset driving path; according to the driving road resistance information corresponding to each preset driving path, the road resistance suffered by the target electric device is optimized to obtain the target driving path corresponding to the target electric device; the driving environment information of the driving environment where the target electric device is located is obtained; according to the driving environment information, the environmental resistance suffered by the target electric device during driving is evaluated to obtain the environmental resistance information; according to the environmental resistance information and the device operation information, the power consumed by the target electric device during operation is evaluated to obtain the device consumption evaluation power; according to the device consumption evaluation power and the target driving path, the driving cost of the target electric device during operation is evaluated to obtain the driving cost corresponding to the target electric device, and the driving cost is optimized to obtain the target driving information corresponding to the target electric device; according to the target driving path and the target driving information, the driving control of the target electric device is performed.

[0207] Thus, by taking the minimum operating cost of the power supply grid system as the optimization objective, the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system that can minimize the cost of the power supply grid system are found. Furthermore, based on the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system, the target electric device and the power supply grid system are scheduled respectively, which can reduce the operating cost of the power supply grid system. Further, the loss evaluation power consumption and the consumption evaluation power are fused to obtain the predicted charge-discharge information, so the accuracy of the charge-discharge prediction of the target electric device is improved; the operating cost of the power supply grid system is optimized, so that the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system that can minimize the operating cost of the power supply grid system and do not exceed the operating limits of the target electric device and the power supply grid system can be obtained by optimization, improving the determination accuracy of the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system; by taking the driving cost as the optimization objective, the obtained target driving information is the one with the minimum driving cost, ensuring that the operation control of the target electric device is the most cost-saving in terms of driving cost and the corresponding driving route belongs to the optimal route, ensuring that more energy of the target electric device can participate in the power supply grid system, and further reducing the operating cost of the power supply grid system.

[0208] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0209] Based on the same inventive concept, an embodiment of the present application further provides a grid interactive scheduling device for implementing the above-mentioned grid interactive scheduling method. The solution provided by this device to solve the problem is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the grid interactive scheduling device provided below can refer to the limitations on the grid interactive scheduling method in the above text, and will not be repeated here.

[0210] In an exemplary embodiment, as Figure 6As shown, a power grid interactive scheduling device 600 is provided, including: an acquisition module 602, a prediction module 604, an optimization module 606, and a control module 608, where:

[0211] The acquisition module 602 is configured to acquire the remaining power when the target electric device arrives at the charging location, and acquire the system operation information of the power supply grid system deployed corresponding to the charging location;

[0212] The prediction module 604 is configured to predict the charge-discharge state of the target electric device according to the remaining power upon arrival and the charge-discharge scheduling information of the target electric device, so as to obtain predicted charge-discharge information; predict the power supply state of the power supply grid system according to the system operation information and the interactive scheduling information of the power supply grid system, so as to obtain predicted power supply state information;

[0213] The optimization module 606 is configured to optimize the operation cost of the power supply grid system according to the predicted charge-discharge information and the predicted power supply state information, so as to obtain the target charge-discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system;

[0214] The control module 608 is configured to schedule the target electric device according to the target charge-discharge scheduling information, and schedule the power supply grid system according to the target interactive scheduling information.

[0215] In one embodiment, the prediction module 604 is further configured to acquire a loss coefficient corresponding to the charge-discharge of the target electric device, where the loss coefficient is used to characterize the degree of power loss during the charge-discharge of the target electric device; evaluate the power loss during the charge-discharge of the target electric device according to the remaining power upon arrival and the loss coefficient, so as to obtain the loss evaluation power; evaluate the power consumption required during the charge-discharge of the target electric device according to the charge-discharge scheduling information of the target electric device, so as to obtain the consumption evaluation power; fuse the loss evaluation power and the consumption evaluation power to obtain the predicted charge-discharge information.

[0216] In one embodiment, the power supply grid system includes a power storage system and a power generation system; the prediction module 604 is further configured to predict the output power of the power generation system according to the system operation information, so as to obtain the predicted power generation output power of the power generation system; predict the state of charge of the power storage system according to the predicted power generation output power, so as to obtain the predicted state of charge of the power storage system; predict the state of charge of the power storage system after scheduling according to the interactive scheduling information and the predicted state of charge of the power storage system, so as to obtain the predicted scheduled state of charge of the power storage system, and determine the predicted scheduled state of charge as the predicted power supply state information.

[0217] In one embodiment, the optimization module 606 is further configured to obtain a first constraint condition of the target electric device and a second constraint condition of the power grid system, where the first constraint condition is used to constrain the charge and discharge state of the target electric device, and the second constraint condition is used to constrain the interaction state of the power grid system; according to the first constraint condition, the second constraint condition, the predicted charge and discharge information, and the predicted power supply state information, optimize the operating cost of the power grid system to obtain the target charge and discharge scheduling information of the target electric device and the target interaction scheduling information of the power grid system.

[0218] In one embodiment, the acquisition module 602 is further configured to obtain the traffic road network information of the road corresponding to the target electric device, and obtain the device operation information of the target electric device; the prediction module 604 is further configured to predict the road resistance suffered by the target electric device when driving on each preset driving path according to the traffic road network information, and obtain the driving road resistance information corresponding to each preset driving path; the optimization module 606 is further configured to optimize the road resistance suffered by the target electric device according to the driving road resistance information corresponding to each preset driving path, and obtain the target driving path corresponding to the target electric device; according to the device operation information and the target driving path, optimize the driving cost corresponding to the target electric device to obtain the target driving information corresponding to the target electric device; the control module 608 is further configured to perform driving control on the target electric device according to the target driving path and the target driving information.

[0219] In one embodiment, the prediction module 604 is further configured to, for each preset driving path, screen out the driving road network information belonging to the preset driving path from the traffic road network information; perform non-linear exponential processing on each dimension information in the driving road network information according to the importance of each dimension information in the process of determining the road resistance, and obtain the network processing information corresponding to each dimension information; according to the weight coefficients corresponding to each dimension information in the driving road network information, perform weighted fusion on the network processing information corresponding to each dimension information to obtain the driving road resistance information corresponding to the preset driving path.

[0220] In one embodiment, the optimization module 606 is further configured to obtain the driving environment information of the driving environment where the target electric device is located; evaluate the environmental resistance suffered by the target electric device during driving according to the driving environment information, and obtain the environmental resistance information; evaluate the power consumed by the target electric device during operation according to the environmental resistance information and the device operation information, and obtain the device consumption evaluation power; evaluate the driving cost during the operation of the target electric device according to the device consumption evaluation power and the target driving path, obtain the driving cost corresponding to the target electric device, and optimize the driving cost to obtain the target driving information corresponding to the target electric device.

[0221] Each module in the above grid interaction scheduling device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to each of the above modules.

[0222] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 7 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a grid interaction scheduling method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0223] Those skilled in the art can understand that Figure 7 the structure shown in

[0224] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0225] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0226] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0227] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0228] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0229] The above embodiments only express several implementation manners of this application, and the description is relatively specific and detailed. However, it should not be construed as a limitation on the patent scope of this application. It should be noted that for those of ordinary skill in the art, without departing from the concept of this application, several modifications and improvements can still be made, and these all belong to the protection scope of this application. Therefore, the protection scope of this application shall be subject to the appended claims.

Claims

1. A power grid interactive dispatching method, characterized in that: The method comprises: Obtaining the remaining power of the target electric device when it arrives at the charging location, and obtaining system operation information of the power supply grid system deployed corresponding to the charging location; Predicting the charge and discharge state of the target electric device according to the reached remaining power and the charge and discharge scheduling information of the target electric device to obtain predicted charge and discharge information; Predicting the power supply status of the power supply grid system according to the system operation information and the interactive scheduling information of the power supply grid system to obtain predicted power supply status information; Optimizing the operating cost of the power supply grid system according to the predicted charging and discharging information and the predicted power supply status information to obtain target charging and discharging scheduling information of the target electric device and target interactive scheduling information of the power supply grid system; The target electric device is scheduled according to the target charge and discharge scheduling information, and the power supply grid system is scheduled according to the target interaction scheduling information.

2. The method according to claim 1, characterized in that The predicting the charge and discharge state of the target electric device according to the reached remaining power and the charge and discharge scheduling information of the target electric device to obtain the predicted charge and discharge information includes: Obtaining a loss coefficient corresponding to the target electric device when charging and discharging, wherein the loss coefficient is used to characterize the degree of power loss when the target electric device is charging and discharging; According to the reached remaining power and the loss coefficient, the power loss of the target electric device during charging and discharging is evaluated to obtain the loss evaluation power; According to the charging and discharging scheduling information of the target electric device, the power required to be consumed when the target electric device is charged and discharged is evaluated to obtain the consumption evaluation power; The loss assessment electric quantity and the consumption assessment power are integrated to obtain predicted charge and discharge information.

3. The method according to claim 1, characterized in that The power supply grid system includes a power storage system and a power generation system; the power supply state of the power supply grid system is predicted according to the system operation information and the interactive scheduling information of the power supply grid system to obtain the predicted power supply state information, including: Predicting the output power of the power generation system according to the system operation information to obtain the predicted power output power of the power generation system; According to the predicted power generation output power, the charge state of the power storage system is predicted to obtain a predicted storage charge state; According to the interactive scheduling information and the predicted storage charge state, the charge state of the power storage system after scheduling is predicted to obtain the predicted scheduling charge state of the power storage system, and the predicted scheduling charge state is determined as the predicted power supply state information.

4. The method according to claim 1, characterized in that: The optimizing the operation cost of the power supply grid system according to the predicted charge and discharge information and the predicted power supply state information to obtain the target charge and discharge scheduling information of the target electric device and the target interactive scheduling information of the power supply grid system includes: Acquire a first constraint condition of the target electric device and a second constraint condition of the power supply grid system, wherein the first constraint condition is used to constrain the charging and discharging state of the target electric device, and the second constraint condition is used to constrain the interaction state of the power supply grid system; According to the first constraint condition, the second constraint condition, the predicted charging and discharging information and the predicted power supply status information, the operating cost of the power supply grid system is optimized to obtain the target charging and discharging scheduling information of the target electric equipment and the target interactive scheduling information of the power supply grid system.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Acquire the traffic network information of the road corresponding to the target electric device, and acquire the device operation information of the target electric device; According to the traffic network information, the road resistance encountered by the target electric device when traveling on each preset driving path is predicted respectively, so as to obtain the driving road resistance information corresponding to each preset driving path; According to the driving road resistance information corresponding to each of the preset driving paths, optimizing the road resistance corresponding to the target electric device to obtain a target driving path corresponding to the target electric device; Optimizing the driving cost corresponding to the target electric device according to the device operation information and the target driving path to obtain target driving information corresponding to the target electric device; The target electric device is controlled to travel according to the target travel path and the target travel information.

6. The method according to claim 5, characterized in that The method of predicting the road resistance encountered by the target electric device when traveling on each preset driving path according to the traffic network information to obtain the driving resistance information corresponding to each preset driving path includes: For each preset driving route, filtering the driving road network information belonging to the preset driving route from the traffic road network information; According to the importance of each dimension of information in the driving road network information to the road resistance determination process, nonlinear exponential processing is performed on each dimension of information in the driving road network information to obtain road network processing information corresponding to each dimension of information; According to the weight coefficient corresponding to each dimension of information in the driving road network information, the road network processing information corresponding to each dimension of information is weighted and fused to obtain the driving road resistance information corresponding to the preset driving path.

7. The method according to claim 5, characterized in that The optimizing the driving cost corresponding to the target electric device according to the device operation information and the target driving path to obtain the target driving information corresponding to the target electric device includes: Acquiring driving environment information of the driving environment in which the target electric device is located; According to the driving environment information, the environmental resistance to which the target electric device is subjected during driving is evaluated to obtain environmental resistance information; According to the environmental resistance information and the equipment operation information, the power consumed by the target electric equipment during operation is evaluated to obtain the equipment consumption evaluation power; According to the evaluated power consumption of the device and the target driving path, the driving cost of the target electric device during operation is evaluated to obtain the driving cost corresponding to the target electric device, and the driving cost is optimized to obtain the target driving information corresponding to the target electric device.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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