Scheduling optimization information generation method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a scheduling optimization information generation method and device, equipment and a storage medium, and the method comprises the steps: determining first power scheduling data of an energy storage system, the first power scheduling data being power scheduling data measured by the energy storage system in advance; determining second power dispatching data of the energy storage system based on the first power dispatching data, wherein the second power dispatching data is a predicted value of the power dispatching data of the energy storage system at a predetermined moment; predicting a scheduling optimization index of the energy storage system according to the second power scheduling data; and determining scheduling optimization information of the energy storage system according to the scheduling optimization index. And generating a predicted value of the corresponding moment based on the power dispatching data measured by the energy storage system, and determining a dispatching optimization index of the energy storage system at the corresponding moment based on the predicted value. The optimization information for accurately guiding the energy storage system to have practicability is determined based on the optimization index, and the scheduling personnel of the corresponding energy storage system can conveniently adjust and deploy the power supply condition at the corresponding moment in time.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of power grid distribution dispatching, and in particular to a method, device, equipment and storage medium for generating dispatching optimization information. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, the traditional multi-energy system not only cannot meet the social energy demand, but also does not conform to the development purpose of energy Internet and ubiquitous Internet of Things. Due to the differences in the operating characteristics of different types of energy, generally speaking, the power grid, heat network and gas network are independently planned and operated, and there is no coordination and complementarity between the three networks, which ultimately leads to problems such as high economic cost and low energy utilization of the power system. The gap between user energy demand and the development of the power grid is slowly widening. Especially in old urban areas, rural areas and relatively backward areas, the power load is often at a peak state, and the phenomenon of power supply exceeding demand often occurs, resulting in a decline in power quality, making it impossible to meet people's needs for reliable electricity and stable power supply. With the large-scale development of mobile energy storage technology, it has become a more mainstream solution to make up for the power supply gap of the power grid by coordinating energy storage with multiple energy sources.

[0003] However, the access location and access time of multi-energy mobile energy storage devices bring more uncertainty to the power grid, affecting the smoothness of the power grid operation, making it easy for errors to occur in the dispatching and distribution of power. In addition, there is a certain uncertainty in the response time of the multi-energy mobile energy storage devices themselves, which complicates the energy dispatching of the power grid. Ultimately, the dispatching of multi-energy mobile energy storage devices is not ideal, the dispatching strategy is chaotic, the dispatching timing is delayed, and the dispatching cost is high.

[0004] Therefore, an effective solution is urgently needed to solve the above problems. Summary of the invention

[0005] In view of this, an embodiment of this specification provides a method for generating scheduling optimization information. One or more embodiments of this specification also involve a scheduling optimization information generation device, a computing device, a computer-readable storage medium and a computer program to solve the technical defects existing in the prior art.

[0006] According to a first aspect of an embodiment of this specification, a method for generating scheduling optimization information is provided, including:

[0007] Determine first power dispatching data of the energy storage system, where the first power dispatching data is power dispatching data previously measured by the energy storage system;

[0008] Determine second power dispatch data of the energy storage system based on the first power dispatch data, where the second power dispatch data is a predicted value of the power dispatch data of the energy storage system at a predetermined time;

[0009] predicting a dispatch optimization index of the energy storage system according to the second power dispatch data;

[0010] The dispatch optimization information of the energy storage system is determined according to the dispatch optimization index.

[0011] In some embodiments of the present specification, determining second power dispatching data of the energy storage system based on the first power dispatching data includes:

[0012] Determine an impact factor of the energy storage system according to the first power dispatching data;

[0013] Determining quantified data of the first power dispatching data;

[0014] The second power dispatching data is determined according to the influencing factor and the quantitative data.

[0015] In some embodiments of the present specification, determining the influencing factor of the energy storage system according to the first power dispatching data includes:

[0016] Determine the first power dispatch data at a plurality of historical moments;

[0017] Determine the maximum and minimum values ​​of the first power dispatching data at multiple historical moments;

[0018] The corresponding impact factor of the first power dispatching data at each historical moment is determined according to the maximum value and the minimum value.

[0019] In some embodiments of the present specification, determining the second power dispatching data according to the influencing factor and the quantitative data includes:

[0020] The second power dispatching data is determined according to the influencing factor corresponding to each historical moment and the first power dispatching data at multiple historical moments.

[0021] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0022] If the first power dispatching data includes charging and / or discharging distance data of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for reducing the charging and / or discharging distance of the mobile energy storage device is generated.

[0023] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0024] If the first power dispatching data includes data on the energy density of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for improving the energy density of the mobile energy storage device is generated.

[0025] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0026] If the first power dispatching data includes data on the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for improving the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device is generated.

[0027] According to a third aspect of an embodiment of this specification, a scheduling optimization information generating device is provided, which is applicable to a multi-energy mobile energy storage system, including:

[0028] A data acquisition module, used to determine first power dispatching data of the energy storage system, where the first power dispatching data is the power dispatching data previously measured by the energy storage system;

[0029] A data processing module, used to determine second power dispatching data of the energy storage system based on the first power dispatching data, the second power dispatching data being a predicted value of the power dispatching data of the energy storage system at a predetermined time;

[0030] A state prediction module, used to predict a dispatch optimization index of the energy storage system according to the second power dispatch data;

[0031] The dispatch information generation module is used to determine the dispatch optimization information of the energy storage system according to the dispatch optimization index.

[0032] According to a third aspect of an embodiment of this specification, a computing device is provided, including:

[0033] Memory and processor;

[0034] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the above-mentioned power system dispatch optimization information generation method are implemented.

[0035] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided, which stores computer-executable instructions, and when the instructions are executed by a processor, the steps of the above-mentioned power system dispatch optimization information generation method are implemented.

[0036] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned method for generating power system dispatch optimization information.

[0037] One embodiment of this specification implements a method for generating dispatch optimization information, generates a prediction value at a corresponding moment based on the power dispatch data measured by the energy storage system, and determines the dispatch optimization index of the energy storage system at the corresponding moment based on the prediction value. Based on the optimization index, practical optimization information is determined to accurately guide the energy storage system, so that the dispatch personnel of the corresponding energy storage system can adjust and deploy the power supply status at the corresponding moment in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of a method for generating scheduling optimization information provided by an embodiment of this specification;

[0039] Figure 2 It is a structural schematic diagram of a scheduling optimization information generating device provided by an embodiment of this specification;

[0040] Figure 3 It is a structural block diagram of a computing device provided by an embodiment of this specification. DETAILED DESCRIPTION

[0041] Many specific details are described in the following description to facilitate a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar generalizations without violating the connotation of this specification, so this specification is not limited to the specific implementation disclosed below.

[0042] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0043] It should be understood that although the terms first, second, etc. may be used to describe various information in one or more embodiments of this specification, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0044] The technical solution provided in this specification aims to solve the problems existing in the traditional sending-end power grid system based on mobile energy storage devices for power supply, such as slow response speed, delayed scheduling timing and high scheduling cost for mobile energy storage devices.

[0045] like Figure 1 As shown, in order to solve the above problems, a first aspect of an embodiment of this specification provides a method for generating scheduling optimization information, including:

[0046] Step S1, determining first power dispatching data of the energy storage system, where the first power dispatching data is power dispatching data previously measured by the energy storage system;

[0047] Step S2, determining second power dispatching data of the energy storage system based on the first power dispatching data, the second power dispatching data being a predicted value of the power dispatching data of the energy storage system at a predetermined time;

[0048] Step S3, predicting a dispatch optimization index of the energy storage system according to the second power dispatch data;

[0049] Step S4: determining the dispatch optimization information of the energy storage system according to the dispatch optimization index.

[0050] In the embodiments of this specification, the energy storage system refers to a sending-end power grid system having multiple mobile energy storage devices. The sending-end power grid refers to a power grid that outputs electricity to the outside, or is called a power generation grid; the mobile energy storage device includes mobile energy storage devices of various energy types.

[0051] The first power dispatching data refers to the relevant power data corresponding to the energy storage system outputting power to the outside, which has been measured and is used to measure its power supply capacity. In the embodiment of this specification, the power dispatching data includes: the discharge distance of the mobile energy storage device, the charging distance of the mobile energy storage device, the multi-energy energy density of the mobile energy storage device, the energy storage efficiency of the mobile energy storage device, the energy storage efficiency rating of the mobile energy storage device, the charging and discharging frequency of the mobile energy storage device, and the charging and discharging frequency rating of the mobile energy storage device.

[0052] The second power dispatching data refers to a predicted value of the power dispatching data generated in a predetermined manner based on the measured first power dispatching data and used to represent the energy supply state of the energy storage system at a predetermined time.

[0053] The dispatch optimization index is data used to measure the dispatch status of the energy storage system at a corresponding moment.

[0054] The dispatch optimization information is used as a reference for dispatchers of the energy storage system and is a guide for adjusting one or more adjustable data in the energy storage system.

[0055] In step S1, the power dispatching data of the energy storage system at the current moment, or the power dispatching data in a previous period of time, is first determined and used as the first power dispatching data.

[0056] In step S2, second power dispatch data for indicating the scheduled time of the energy storage system is generated according to the first power dispatch data obtained in step S1. The second power dispatch data can be generated by a corresponding machine learning model or based on a corresponding calculation method.

[0057] In step S3, based on the obtained second power data at the predetermined time, the dispatch optimization index of the energy storage system at the corresponding predetermined time is predicted.

[0058] In step S4, based on the relationship between the optimization index and the preset reference information, the optimization information for the energy storage system is determined. In some embodiments of this specification, if the value of the optimization index is greater than the value of the reference information, optimization information for increasing the value of one or more adjustable parameters of the energy storage system is generated for reference by the dispatcher of the energy storage system. In some other embodiments of this specification, if the value of the optimization index is less than or equal to the value of the reference information, optimization information for reducing the value of one or more adjustable parameters of the energy storage system is generated for reference by the dispatcher of the energy storage system.

[0059] In some embodiments of the present specification, determining second power dispatching data of the energy storage system based on the first power dispatching data includes:

[0060] Determine an impact factor of the energy storage system according to the first power dispatching data;

[0061] Determining quantified data of the first power dispatching data;

[0062] The second power dispatching data is determined according to the influencing factor and the quantitative data.

[0063] In some embodiments of the present specification, determining the influencing factor of the energy storage system according to the first power dispatching data includes:

[0064] Determine the first power dispatch data at a plurality of historical moments;

[0065] Determine the maximum and minimum values ​​of the first power dispatching data at multiple historical moments;

[0066] The corresponding impact factor of the first power dispatching data at each historical moment is determined according to the maximum value and the minimum value.

[0067] In some embodiments of the present specification, determining the second power dispatching data according to the influencing factor and the quantitative data includes:

[0068] The second power dispatching data is determined according to the influencing factor corresponding to each historical moment and the first power dispatching data at multiple historical moments.

[0069] In this embodiment, the first power dispatching data includes the first power dispatching data at multiple historical moments. For example, the first power dispatching data at multiple historical moments are as follows:

[0070]

[0071] Among them, t1, t2, ..., t r ,...,t n is a fixed time interval of n moments, where n is a natural number, n∈1,2,..., r is the rth moment, and r is a natural number, r∈{1,2,...,n};

[0072] t r The energy release distance of multi-energy mobile energy storage equipment at all times;

[0073] t r Charging distance of multi-energy mobile energy storage devices at all times;

[0074] t r Energy density of multi-energy mobile energy storage devices at all times;

[0075] t r Energy storage efficiency of multi-energy mobile energy storage devices at all times;

[0076] t r The charging and discharging frequency of multi-energy mobile energy storage devices at all times.

[0077] Further, the influence factor of the energy storage system at each historical moment is determined according to the first power dispatching data at multiple historical moments. The calculation is as follows:

[0078]

[0079] Among them, L FH,max , L FH,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the energy release distance of the multi-energy mobile energy storage device at the moment;

[0080] L FGQ,max , L FGQ,min are t1, t2, ..., t r ,...,t nThe maximum and minimum charging distances of multi-energy mobile energy storage devices at the moment;

[0081] M NL,max , M NL,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of energy density of multi-energy mobile energy storage devices at the moment;

[0082] η CF,max , η CF,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the energy storage efficiency of multi-energy mobile energy storage equipment at the moment;

[0083] f CS,max , f CS,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the charging and discharging frequency of the multi-energy mobile energy storage device at the moment.

[0084] Further, based on the first power dispatching data of multiple historical moments, the quantized data of the first power dispatching data of each historical moment is determined. In this embodiment, the quantized data is the corresponding normalized value. The calculation method for the first power dispatching data at each moment is as follows:

[0085]

[0086] Where: t r Normalized value of the energy discharge distance of the mobile energy storage device in the energy storage system at any moment;

[0087] t r Normalized value of the charging distance of the mobile energy storage device in the energy storage system at that moment;

[0088] t r Normalized value of energy density of mobile energy storage device in energy storage system at any moment;

[0089] t r Normalized value of energy storage efficiency of mobile energy storage equipment in energy storage system at any moment;

[0090] t r Normalized value of the charging and discharging frequency of the mobile energy storage device in the energy storage system at any moment;

[0091] f CS,NIt is the rated value of charging and discharging frequency of multi-energy mobile energy storage equipment;

[0092] η CF,N Energy storage efficiency rating for multi-energy mobile energy storage devices;

[0093] L FH,max , L FH,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the energy discharge distance of the mobile energy storage device in the energy storage system at the moment;

[0094] L FGQ,max , L FGQ,min are t1, t2, ..., t r ,...,t n The maximum and minimum charging distances of mobile energy storage devices in the energy storage system at the moment;

[0095] M NL,max , M NL,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the energy density of the mobile energy storage device in the energy storage system at the moment;

[0096] η CF,max , η CF,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the energy storage efficiency of the mobile energy storage device in the energy storage system at the moment;

[0097] f CS,max , f CS,min are t1, t2, ..., t r ,...,t n The maximum and minimum values ​​of the charging and discharging frequency of the mobile energy storage device in the energy storage system at the moment; finally, the quantitative data corresponding to the first power dispatching data is obtained, as shown below:

[0098]

[0099] Furthermore, after obtaining the quantitative data of the first power dispatching data at multiple historical moments and the impact factor corresponding to each historical moment, the predicted value of the power dispatching data of the energy storage system at the next moment corresponding to the multiple historical moments is determined according to the quantitative data of the first power dispatching data and the impact factor corresponding to each historical moment, and the calculation method is as follows:

[0100]

[0101] in, t r+1 Prediction of energy release distance of multi-energy mobile energy storage equipment at all times, t r+1 Prediction of charging distance of multi-energy mobile energy storage equipment at any time, t r+1 Predicted value of energy density of multi-energy mobile energy storage equipment at all times, t r+1 Prediction of energy storage efficiency of multi-energy mobile energy storage equipment at all times, t r+1 Prediction of the charging and discharging frequency of multi-energy mobile energy storage devices at all times.

[0102] Furthermore, after determining the second power dispatching data of the energy storage system, the corresponding dispatching optimization index can be further calculated. The corresponding specific calculation method is as follows:

[0103]

[0104] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0105] If the first power dispatching data includes charging and discharging distance data of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for reducing the charging and discharging distance of the mobile energy storage device is generated.

[0106] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0107] If the first power dispatching data includes data on the energy density of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for improving the energy density of the mobile energy storage device is generated.

[0108] In some embodiments of this specification, determining the dispatch optimization information of the energy storage system according to the dispatch optimization index includes:

[0109] If the first power dispatching data includes data on the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for improving the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device is generated.

[0110] If the energy storage system scheduling optimization index is obtained If it is greater than or equal to 0.629, it is considered that at t r+1The mobile energy storage equipment of the energy storage system responds promptly at all times, ensuring the effective response of the mobile energy storage equipment during grid dispatching, and improving the power distribution efficiency. At this time, the charging distance, discharging distance, energy density, energy storage efficiency and charging and discharging frequency of the mobile energy storage equipment of the energy storage system can be kept unchanged; if the scheduling optimization index of the energy storage system is obtained If it is less than 0.629, it is considered that at t r+1 The mobile energy storage device responds slowly at all times. At this time, the charging and discharging distance of the mobile energy storage device can be reduced, the energy density, energy storage efficiency and charging and discharging frequency can be improved, and the corresponding timeliness of the mobile energy storage device of the energy storage system can be improved.

[0111] One embodiment of this specification implements a method for generating dispatch optimization information, generates a prediction value at a corresponding moment based on the power dispatch data measured by the energy storage system, and determines the dispatch optimization index of the energy storage system at the corresponding moment based on the prediction value. Based on the optimization index, practical optimization information is determined to accurately guide the energy storage system, so that the dispatch personnel of the corresponding energy storage system can adjust and deploy the power supply status at the corresponding moment in a timely manner.

[0112] like Figure 2 As shown, according to the third aspect of the embodiment of this specification, a scheduling optimization information generation device is provided, which is applicable to a multi-energy mobile energy storage system, including:

[0113] The data acquisition module 1 is used to determine the first power dispatching data of the energy storage system, where the first power dispatching data is the power dispatching data previously measured by the energy storage system;

[0114] A data processing module 2 is used to determine second power dispatching data of the energy storage system based on the first power dispatching data, where the second power dispatching data is a predicted value of the power dispatching data of the energy storage system at a predetermined time;

[0115] A state prediction module 3, used to predict a dispatch optimization index of the energy storage system according to the second power dispatch data;

[0116] The dispatch information generation module 4 is used to determine the dispatch optimization information of the energy storage system according to the dispatch optimization index.

[0117] The above is a schematic scheme of a scheduling optimization information generation device of this embodiment. It should be noted that the technical scheme of the scheduling optimization information generation device and the technical scheme of the above-mentioned scheduling optimization information generation method belong to the same concept, and the details not described in detail in the technical scheme of the scheduling optimization information generation device can be found in the description of the technical scheme of the above-mentioned scheduling optimization information generation method.

[0118] Figure 3The block diagram of a computing device 300 according to an embodiment of the present specification is shown. The components of the computing device 300 include but are not limited to a memory 310 and a processor 320. The processor 320 is connected to the memory 310 via a bus 530, and the database 350 is used to store data.

[0119] The computing device 300 also includes an access device 340 that enables the computing device 300 to communicate via one or more networks 360. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 540 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that is wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a world-wide interoperability for microwave access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0120] In one embodiment of the present specification, the above components of the computing device 300 and Figure 3 Other components not shown in the figure may also be connected to each other, for example, via a bus. It should be understood that Figure 3 The computing device structure block diagram shown is only for the purpose of illustration, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0121] The computing device 300 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smart phone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other types of mobile devices, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 300 may also be a mobile or stationary server.

[0122] Among them, the processor 320 is used to execute the following computer executable instructions, which, when executed by the processor, implement the steps of the above-mentioned scheduling optimization information generation method. The above is a schematic scheme of a computing device of this embodiment. It should be noted that the technical scheme of the computing device and the technical scheme of the above-mentioned scheduling optimization information generation method belong to the same concept. The details not described in detail in the technical scheme of the computing device can be found in the description of the technical scheme of the above-mentioned scheduling optimization information generation method.

[0123] An embodiment of the present specification further provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the above-mentioned scheduling optimization information generation method.

[0124] The above is a schematic scheme of a computer-readable storage medium of this embodiment. It should be noted that the technical scheme of the storage medium and the technical scheme of the above-mentioned scheduling optimization information generation method belong to the same concept, and the details not described in detail in the technical scheme of the storage medium can be referred to the description of the technical scheme of the above-mentioned scheduling optimization information generation method.

[0125] An embodiment of the present specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is caused to execute the steps of the above-mentioned scheduling optimization information generation method.

[0126] The above is a schematic scheme of a computer program of this embodiment. It should be noted that the technical scheme of the computer program and the technical scheme of the above-mentioned scheduling optimization information generation method belong to the same concept, and the details not described in detail in the technical scheme of the computer program can be referred to the description of the technical scheme of the above-mentioned scheduling optimization information generation method.

[0127] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0128] Computer instructions include computer program codes, which may be in source code form, object code form, executable files or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program codes, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memories, read-only memories (ROM), random access memories (RAM), electric carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in computer readable media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0129] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of this specification are not limited by the order of the actions described, because according to the embodiments of this specification, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the embodiments of this specification.

[0130] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0131] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The optional embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can understand and use this specification well. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A method for generating scheduling optimization information, applicable to a multi-energy mobile energy storage system, characterized in that: include: Determine first power dispatching data of the energy storage system, where the first power dispatching data is power dispatching data previously measured by the energy storage system; Determine second power dispatch data of the energy storage system based on the first power dispatch data, where the second power dispatch data is a predicted value of the power dispatch data of the energy storage system at a predetermined time; Predicting a dispatch optimization index of the energy storage system according to the second power dispatch data; The scheduling optimization information of the energy storage system is determined according to the scheduling optimization index.

2. The method according to claim 1, characterized in that The determining the second power dispatching data of the energy storage system based on the first power dispatching data comprises: Determine an impact factor of the energy storage system according to the first power dispatching data; Determining quantified data of the first power dispatching data; The second power dispatching data is determined according to the influencing factor and the quantitative data.

3. The method according to claim 2, characterized in that The determining the influencing factor of the energy storage system according to the first power dispatching data includes: Determine the first power dispatch data at a plurality of historical moments; Determine the maximum and minimum values ​​of the first power dispatching data at the plurality of historical moments; The corresponding impact factor of the first power dispatching data at each historical moment is determined according to the maximum value and the minimum value.

4. The method according to claim 3, characterized in that The determining the second power dispatching data according to the influencing factor and the quantitative data includes: The second power dispatching data is determined according to the influencing factor corresponding to each historical moment and the first power dispatching data at the plurality of historical moments.

5. The method according to claim 1, characterized in that The determining the scheduling optimization information of the energy storage system according to the scheduling optimization index includes: If the first power dispatching data includes charging and / or discharging distance data of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for reducing the charging and / or discharging distance of the mobile energy storage device is generated.

6. The method according to claim 1, characterized in that The determining the scheduling optimization information of the energy storage system according to the scheduling optimization index includes: If the first power dispatching data includes data on the energy density of the mobile energy storage device, and the dispatching optimization index meets a preset condition, dispatching optimization information for improving the energy density of the mobile energy storage device is generated.

7. The method according to claim 1, characterized in that The determining the scheduling optimization information of the energy storage system according to the scheduling optimization index includes: If the first power dispatching data includes data on the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device, and the dispatching optimization index meets the preset conditions, dispatching optimization information for improving the energy storage efficiency and / or charging and discharging frequency of the mobile energy storage device is generated.

8. A dispatch optimization information generation device, applicable to a multi-energy mobile energy storage system, characterized in that: include: A data acquisition module, used to determine first power dispatching data of the energy storage system, wherein the first power dispatching data is the power dispatching data previously measured by the energy storage system; A data processing module, configured to determine second power dispatching data of the energy storage system based on the first power dispatching data, wherein the second power dispatching data is a predicted value of the power dispatching data of the energy storage system at a predetermined time; A state prediction module, used to predict a dispatch optimization index of the energy storage system according to the second power dispatch data; The scheduling information generation module is used to determine the scheduling optimization information of the energy storage system according to the scheduling optimization index.

9. A computing device, characterized in that include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the scheduling optimization information generation method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the scheduling optimization information generating method according to any one of claims 1 to 7.

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