Scheduling Optimization Method and Device for Cooperative Operation of Independent Energy Storage and Power Grid

Abstract

The present invention relates to the technical field of power grid dispatching, and particularly to a dispatching optimization method and device for the coordination of independent energy storage and the power grid. The method includes dividing the power grid coverage area into several power grid unit areas, where each power grid unit area includes at least one power energy storage device, and setting a unique unit area identifier for each power grid unit area; obtaining the power supply and demand status vectors of each power grid unit area, where the power supply and demand status vectors include the power consumption load gap index and the corresponding unit area identifier; inputting all the power supply and demand status vectors into a preset power coordination dispatching space for optimization processing to obtain the optimal coordination dispatching plan within the power grid coverage area, and performing dispatching processing based on the optimal coordination dispatching plan. It can quickly respond to power grid load fluctuations and energy storage state changes, reduce the operation cost of the power grid, and improve the power supply quality and service level.

Description

Technical Field

[0001] The present invention relates to the technical field of composite materials, and particularly to a scheduling optimization method and device for the coordination of independent energy storage and power grid. Background Art

[0002] With the rapid development of renewable energy, the proportion of intermittent energy sources such as wind energy and solar energy has been increasing continuously, and the load volatility and uncertainty of the power grid have increased significantly. To address this challenge, independent energy storage systems have received extensive attention and development. An independent energy storage system can store excess electric energy and release it when needed to smooth load fluctuations and improve the flexibility and stability of the power system.

[0003] However, in practical applications, how to effectively integrate independent energy storage systems into the existing power network and ensure their coordinated operation to achieve the goals of optimizing resource allocation and improving economic benefits is still an urgent problem to be solved. Current scheduling methods often lack refined management of regional power load characteristics and energy storage status, making it difficult to fully exploit the potential of energy storage devices, resulting in low power grid operation efficiency, high costs, and even affecting power supply quality and service level.

[0004] Therefore, there is an urgent need to provide a scheduling optimization method and device for the coordination of independent energy storage and power grid to solve the above technical problems. Summary of the Invention

[0005] Embodiments of the present invention provide a scheduling optimization method and device for the coordination of independent energy storage and power grid, which can quickly respond to power grid load fluctuations and energy storage state changes, reduce power grid operation costs, and improve power supply quality and service level.

[0006] In a first aspect, embodiments of the present invention provide a scheduling optimization method for the coordination of independent energy storage and power grid, the method comprising:

[0007] Dividing the power grid coverage area into a plurality of power grid unit areas, each power grid unit area includes at least one power energy storage device, and setting a unique unit area identifier for each power grid unit area;

[0008] Obtaining the power supply and demand state vector of each of the power grid unit areas, the power supply and demand state vector includes an electricity load gap index and the corresponding unit area identifier;

[0009] Inputting all the power supply and demand state vectors into a preset power coordination scheduling space for optimization processing to obtain an optimal coordination scheduling plan within the power grid coverage area, and performing scheduling processing based on the optimal coordination scheduling plan.

[0010] In a second aspect, embodiments of the present invention further provide a scheduling optimization device for the coordination of independent energy storage and power grid, the device comprising:

[0011] A power grid unit division module, configured to divide the power grid coverage area into a plurality of power grid unit areas, where at least one power energy storage device is included in each power grid unit area, and a unique unit area identifier is set for each power grid unit area;

[0012] A power supply and demand status acquisition module, configured to acquire the power supply and demand status vector of each power grid unit area, where the power supply and demand status vector includes an electricity load gap index and the corresponding unit area identifier;

[0013] A collaborative scheduling optimization module, configured to input all the power supply and demand status vectors into a preset power collaborative scheduling space for optimization processing to obtain an optimal collaborative scheduling scheme within the power grid coverage area;

[0014] A scheduling processing execution module, configured to perform actual scheduling processing based on the optimal collaborative scheduling scheme.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the method of any embodiment of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed on a computer, the computer is made to execute the method of any embodiment of the present invention.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: By refining the power grid area division, the present invention realizes the accurate grasp of the power supply and demand status. The unique identifier of each power grid unit area ensures the traceability of data and the refinement of management; at the same time, by introducing a preset power collaborative scheduling space for optimization processing, the actual situations of each power grid unit area can be comprehensively considered, the maximum potential of the energy storage device can be tapped, and an optimal collaborative scheduling scheme can be formulated; the optimal collaborative scheduling scheme not only improves the flexibility and stability of the power grid operation, but also effectively reduces the operation cost and avoids the waste of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of a scheduling optimization method for independent energy storage and power grid collaboration provided by an embodiment of the present invention;

[0020] Figure 2 It is the hardware architecture diagram of the electronic device provided by the embodiment of the present invention;

[0021] Figure 3 It is the structure diagram of the scheduling optimization device for independent energy storage and grid coordination provided by the embodiment of the present invention. Specific embodiments

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] Please refer to Figure 1 , the embodiment of the present invention provides a scheduling optimization method for independent energy storage and grid coordination, and the method includes:

[0024] Step 100: Divide the grid coverage area into several grid unit areas, each grid unit area includes at least one power energy storage device, and set a unique unit area identifier for each grid unit area;

[0025] Step 200: Obtain the power supply and demand state vectors of each of the grid unit areas, where the power supply and demand state vectors include power load gap indexes and corresponding unit area identifiers;

[0026] Step 300: Input all the power supply and demand state vectors into a preset power coordination scheduling space for optimization processing to obtain the optimal coordination scheduling plan within the grid coverage area, and perform scheduling processing based on the optimal coordination scheduling plan.

[0027] In this embodiment, by subdividing the power grid coverage area into multiple power grid unit areas and setting a unique identifier for each area, more refined management of the power system is achieved; by obtaining the power supply and demand status vectors of each unit area, including the power consumption load gap index and the corresponding unit area identifier, the difference between the power demand and supply of each area can be accurately evaluated; by simultaneously inputting all the power supply and demand status vectors into a preset power collaborative scheduling space for optimization processing to determine the optimal collaborative scheduling plan; not only the power load characteristics between different areas are considered, but also the potential of energy storage devices in suppressing load fluctuations is fully explored; by optimizing the charge and discharge strategies of energy storage devices, the peak-valley difference in the operation of the power grid can be effectively reduced, and the flexibility and stability of the system can be improved; at the same time, the data-driven decision-making process helps to improve the resource allocation efficiency, reduce the operation cost, and ensure that the power supply quality and service level are not affected.

[0028] It should be noted that in order to better manage and optimize the allocation of power resources, the entire power grid coverage area needs to be reasonably divided into several relatively independent but interconnected power grid unit areas; the basis for division includes geographical factors (such as terrain, urban distribution, etc.), power load characteristics (such as load density, load volatility, etc.), power network structure (such as substation distribution, transmission line orientation, etc.), and the distribution and capacity of energy storage devices, etc.; the goal of division is to make the power supply and demand within each power grid unit area relatively balanced, and at the same time facilitate the access and scheduling of independent energy storage systems; for example, precise division is carried out using technologies such as geographic information system (GIS) to ensure the accuracy and rationality of the division.

[0029] Within each power grid unit area, at least one power energy storage device is configured according to the power supply and demand characteristics and energy storage requirements of the area. The division of the power grid unit area takes into account the distribution factors of energy storage devices, making it more convenient for the access and scheduling of independent energy storage systems; parameters such as the type, capacity, and location of the energy storage device should be optimized according to the actual situation to ensure that it can effectively suppress load fluctuations and improve the stability and flexibility of the power system; a unique unit area identifier is set for each power grid unit area. The unit area identifier is used to represent the relative position coordinates of the power grid unit area within the power grid coverage area. By setting a unique unit area identifier for each power grid unit area, each area can be conveniently identified and located, providing convenience for subsequent power supply and demand status monitoring and collaborative scheduling, and helping to reduce the complexity and cost of power resource management; the data format of the unit area identifier can be a combination of numbers, letters, or numbers and letters; through the unit area identifier, each power grid unit area can be conveniently identified and located, providing convenience for subsequent power supply and demand status monitoring and collaborative scheduling.

[0030] In an embodiment of the present invention, the method for obtaining the power supply and demand status vectors of each of the power grid unit areas includes:

[0031] Step 210: Collect the power grid load data and energy storage device status data within each power grid unit area; among them, the power grid load data includes the real-time power consumption load, historical load curve, load prediction data, etc. of each power grid unit area, reflecting the power demand characteristics of the area; the energy storage device status data includes the current energy storage capacity, charge and discharge power, charge and discharge efficiency, remaining battery capacity (SOC), health status (SOH), etc. of the energy storage device, reflecting the operating status and regulation ability of the energy storage device; by deploying smart meters and energy storage device status monitoring sensors within each power grid unit area, the power grid load data and energy storage device status data are collected in real time; through Internet of Things (IoT) technology, the collected data is transmitted to the central data processing platform to ensure the real-time and integrity of the data.

[0032] Step 220: For each power grid unit area, input the collected power grid load data and energy storage device status data into a pre-trained power consumption demand analysis model and an energy storage device status evaluation model respectively, and output a power consumption load demand index and an energy storage device supply index; the power consumption load demand index is used to represent the difference between the power load that the power grid can provide for the power grid unit area and the power load consumed by the power grid unit area; the power consumption load demand index output by the power consumption demand analysis model can accurately represent the difference between the power supply capacity of the power grid and the power consumption load of the unit area, which helps to clarify whether the power supply in each area is sufficient; the supply index output by the energy storage device status evaluation model can reflect the regulation ability of the energy storage device, providing a quantitative basis for evaluating to what extent it can support the power supply;

[0033] Among them, the input of the power consumption demand analysis model is the power grid load data, including the real-time power consumption load, historical load curve and load prediction data, and the output is the power consumption load demand index, which is used to represent the difference between the power load that the power grid can provide for the power grid unit area and the power load consumed by the area; by analyzing the power demand characteristics of the power grid unit area, the change of the power consumption load in the future period is predicted; the difference between the power supply capacity of the power grid and the power consumption load is calculated to generate the power consumption load demand index. When the power consumption load demand index is positive, it indicates that the power supply is sufficient, and when it is negative, it indicates that the power supply is insufficient;

[0034] The input of the energy storage device status evaluation model is the energy storage device status data, including the current energy storage capacity, charge and discharge power, charge and discharge efficiency, remaining power (SOC), etc.; the output is the energy storage device supply index, which is used to characterize the power regulation ability of the energy storage device under the current status; the energy storage device status evaluation model is used to evaluate the available capacity and charge and discharge ability of the energy storage device, and determine the maximum power support it can provide; combined with the operating status of the energy storage device, the energy storage device supply index is generated; the higher the energy storage device supply index, the stronger the regulation ability of the energy storage device.

[0035] Step 230: Based on the power consumption load demand index and the energy storage device supply index output in step S220, calculate the power consumption load gap index for each grid unit area; the calculation formula is:

[0036] Power consumption load gap index = power consumption load demand index - energy storage device supply index;

[0037] The power consumption load gap index is used to characterize the power supply and demand gap situation in the grid unit area under the current status: when the power consumption load gap index is positive, it means that there is a power supply gap in this area and power needs to be allocated from the grid or other areas; when the power consumption load gap index is negative, it means that the power supply in this area is sufficient and the energy storage device can release excess electric energy to support other areas; gap calculation helps to identify which areas need additional power support and which areas can release excess electric energy, so as to achieve the optimal allocation of resources and improve the operation efficiency of the entire power system.

[0038] Step 240: Combine the calculated power consumption load gap index with the unit area identifier of the grid unit area to create a comprehensive description of the power supply and demand status, which not only retains the spatial information but also includes the dynamic change characteristics in the time dimension; the formed power supply and demand status vector is a multi-dimensional array, where each element represents the power supply and demand status of a certain grid unit area at a specific time point;

[0039] In terms of data structure, this heterogeneous combination can be implemented by using arrays, structures, etc.; for example, in programming, a structure can be defined, which contains two member variables, namely the unit area identifier (such as a string or integer code) and the power consumption load gap index (a floating point number or integer); if the array method is used, the unit area identifier and the power consumption load gap index can be stored in the array in a certain order, such as [unit area identifier 1, power consumption load gap index 1, unit area identifier 2, power consumption load gap index 2,...], which is convenient for identification and processing in the subsequent power collaborative scheduling process;

[0040] The power supply and demand state vector after heterogeneous combination is convenient for identification and processing in the subsequent power collaborative dispatching process; when it is necessary to query, analyze or make dispatching decisions on the power supply and demand situation in a specific area, the corresponding power load gap index can be quickly located through the unit area identifier, so as to take corresponding measures; for example, when the power grid coverage area is large, the power generation plan or the charge and discharge strategy of energy storage devices can be adjusted targeted according to the power supply and demand state vectors of different regions.

[0041] By collecting various types of data on the power grid load and the state of energy storage devices, the power supply and demand situation of the region can be comprehensively reflected; the application of the power demand analysis model and the energy storage device state evaluation model can effectively predict the change of power load, evaluate the energy storage regulation ability, and scientifically obtain the supply and demand index; the calculation of the power load gap index intuitively presents the power supply and demand gap in the region, which is convenient for judging the power distribution direction; the combination of the gap index and the unit area identifier to form a multi-dimensional array, this heterogeneous combination method not only retains the spatial information but also reflects the time dynamic change, which is convenient for query, analysis and decision-making in the subsequent power collaborative dispatching, and can adjust the power generation plan and the charge and discharge strategy of energy storage devices in time according to different regional conditions, improving the overall operation efficiency of the power grid, resource utilization rate and ensuring the stability of power supply.

[0042] In an embodiment of the present invention, the power supply and demand state vectors of all grid unit areas are optimized through a preset power collaborative dispatching space to generate an optimal collaborative dispatching plan, and the power grid and energy storage system are scheduled and executed based on this plan. The specific implementation is as follows:

[0043] Step 310: Classify all the power supply and demand state vectors to obtain a set of unit areas with load surplus and a set of unit areas with load deficit; the classification principle is to divide the power supply and demand state vectors with a positive power load gap index into the set of unit areas with load deficit; the power supply and demand state vectors with a negative power load gap index are divided into the set of unit areas with load surplus; by classifying all the power supply and demand state vectors, the grid unit areas are clearly divided into two categories: load surplus and load deficit; this method helps to accurately identify which areas have power surplus or shortage, so as to provide a clear direction for subsequent dispatching decisions; the classification process makes the resource allocation more targeted, avoids waste caused by blind adjustment, and improves the operation efficiency of the entire power system.

[0044] Step 320: Arrange each grid unit area in the load surplus unit area set in ascending order according to the corresponding power consumption load gap index to obtain a load surplus unit area sequence; and sequentially label the grid unit areas in the load surplus unit area sequence as the first surplus unit area to the nth surplus unit area. By arranging the load surplus unit areas in ascending order of the power consumption load gap index, areas with smaller power surpluses are preferentially dispatched to ensure the reasonable allocation and efficient utilization of power resources. The sorting process enables the maximization of the utilization of resources in power surplus areas, avoids resource waste, and simultaneously reduces the additional load pressure on the power grid. Through sorting, the optimization algorithm can quickly locate the optimal matching object, reduce the calculation time, and improve the generation efficiency of the dispatching plan.

[0045] Step 330: Use the unit area identifier to determine whether the power consumption load gap index corresponding to the grid unit area closest to the first surplus unit area in the load deficit unit area set is greater than the power consumption load gap index of the first surplus unit area, and update the load deficit unit area set and the load surplus unit area set according to the judgment result.

[0046] If it is greater, modify the power consumption load gap index corresponding to the grid unit area closest to the first surplus unit area to the sum of the two, retain the remaining power supply capacity to support subsequent matching; and delete the first surplus unit area from the load surplus unit area set, and the rankings of the remaining surplus unit areas increase sequentially (i.e., the original second surplus unit area rises to the first surplus unit area, the original third surplus unit area rises to the second surplus unit area, and so on) to ensure the maximization of the utilization of resources in power surplus areas and avoid resource waste. By dynamically updating the set, the number of load deficit and load surplus unit areas is gradually reduced, simplifying the complexity of the dispatching problem.

[0047] If it is less, modify the power consumption load gap index corresponding to the first surplus unit area to the sum of the two, re-arrange the load surplus unit area set in ascending order to obtain the latest load surplus unit area sequence to ensure the dynamic adaptability of the dispatching process; and delete the grid unit area closest to the first surplus unit area from the load deficit unit area set to accurately eliminate the power supply gap and improve the dispatching efficiency. Re-sort the load surplus unit area set to ensure the preferential dispatching of areas with smaller power surpluses and optimize the allocation order of power resources.

[0048] If they are equal, delete the grid unit area closest to the first surplus unit area from the load deficit unit area set and delete the first surplus unit area from the load surplus unit area set, and the rankings of the remaining surplus unit areas increase sequentially. By simultaneously deleting the load deficit and load surplus unit areas, the scale of the dispatching problem is gradually reduced, improving the dispatching efficiency.

[0049] By judging the relationship of the power consumption gap index between the load deficit unit area and the load surplus unit area, the precise matching between the supply and demand sides is achieved, ensuring the rational allocation of power resources; according to the matching results, the sets of load deficit unit areas and load surplus unit areas are dynamically updated to ensure the real-time performance and flexibility of the dispatching process; by modifying the power consumption gap index and reordering, the distribution of power resources is made more balanced, avoiding the imbalance between power supply and demand in local areas.

[0050] Step 340: Repeat the judgment process in Step 330 until the grid unit areas in the set of load surplus unit areas and / or the set of load deficit unit areas are cleared; through the iterative matching process, the power supply and demand gap within the grid coverage area is gradually eliminated to ensure the global balance of power supply and demand; repeating the matching process can maximize the utilization of power resources in the load surplus area, reduce the power gap in the load deficit area, and improve the overall operation efficiency of the power grid; the iterative matching process can respond in real time to the changes in the grid load and energy storage state to ensure the dynamic adaptability and robustness of the dispatching plan.

[0051] Step 350: Based on the clearing process, at least two grid unit areas participating in the addition are respectively used as mutually cooperative dispatching objects to generate the optimal cooperative dispatching plan within the grid coverage area; based on at least two grid unit areas participating in the addition during the clearing process as mutually cooperative dispatching objects, the resource complementarity between different regions is realized, forming a cooperative effect, and enhancing the stability and reliability of the overall power system; the generated optimal cooperative dispatching plan not only guides the specific dispatching operation, but also provides a scientific decision-making basis for the management level to help it better plan the future development direction; corresponding warning information (such as power consumption load deficit, energy storage load surplus or self-sufficiency in power consumption) is generated according to different situations, enabling relevant departments to prepare countermeasures in advance to further ensure the quality and service level of power supply.

[0052] More specifically, in response to the set of load surplus unit areas being cleared first, at least two grid unit areas participating in the addition are respectively used as mutually cooperative dispatching objects to generate the optimal cooperative dispatching plan within the grid coverage area, and warning information about the power consumption load deficit in the grid coverage area is generated and displayed, which can enable grid managers to be aware of the possible insufficient power supply situation in advance; it is beneficial to take timely measures, such as increasing the power generation power, adjusting the discharge strategy of energy storage equipment or conducting inter-regional power allocation, so as to ensure the stable operation of the power grid;

[0053] In response to the fact that the set of load deficit unit areas is cleared first, at least two grid unit areas participating in the addition are respectively used as objects for collaborative scheduling with each other, an optimal collaborative scheduling plan within the grid coverage area is generated, and a warning message about the surplus of the electricity storage load in the grid coverage area is generated and displayed; grid managers can make reasonable plans for energy storage devices based on this information. For example, they can adjust the charging strategy of energy storage devices or consider using the excess electric energy in other forms, such as participating in the electricity market trading, etc., to improve the overall economic benefits of the grid;

[0054] In response to the fact that the set of load deficit unit areas and the set of load surplus unit areas are cleared simultaneously, at least two grid unit areas participating in the addition are respectively used as objects for collaborative scheduling with each other, an optimal collaborative scheduling plan within the grid coverage area is generated, and information about self-sufficiency in electricity consumption in the grid coverage area is generated and displayed; this enables grid managers to intuitively understand the overall operating state of the grid, helps to enhance the transparency of grid operation, and is of great significance for the daily management, planning, and response to emergencies of the grid.

[0055] By judging the sequence of clearing of different sets, it is possible to flexibly respond to changes in the power supply and demand state of the grid, ensuring the timeliness and effectiveness of the scheduling plan; generating corresponding warning information or self-sufficiency information according to the clearing situation helps dispatchers to timely understand the grid state and take targeted measures to improve the transparency and controllability of grid operation; this process realizes the refined management and collaborative scheduling of the power supply and demand state of the grid, helps to optimize resource allocation, and improves the overall performance and economic benefits of the grid.

[0056] In this embodiment, through classification, sorting, and matching processing, precise power allocation between load deficit unit areas and load surplus unit areas is realized to optimize resource allocation; through the iterative matching and clearing process, an optimal collaborative scheduling plan is quickly generated to improve the scheduling efficiency; warning information is generated according to the clearing situation to provide real-time decision support for grid operation, enhancing power supply quality and stability; comprehensively considering the power supply and demand states of each grid unit area, global power supply and demand balance within the grid coverage area is achieved, reducing operating costs.

[0057] As Figure 2 、 Figure 3 shown, an embodiment of the present invention provides a scheduling optimization device for the coordination of independent energy storage and the grid. The device embodiment can be implemented by software, or by hardware, or by a combination of software and hardware. From the hardware level, as Figure 2 shown, it is a hardware architecture diagram of an electronic device where the scheduling optimization device for the coordination of independent energy storage and the grid provided by an embodiment of the present invention is located. Except for Figure 2In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device where the device is located in the embodiments usually may further include other hardware, such as a forwarding chip responsible for processing packets, etc. Taking software implementation as an example, as Figure 3 shown, as a logically meaningful device, it is formed by the CPU of the electronic device where it is located reading the corresponding computer program in the non-volatile memory into the memory and running it.

[0058] Such as Figure 3 shown, a dispatching optimization device for independent energy storage and grid coordination provided by the present invention includes:

[0059] A grid unit division module 400, configured to divide the grid coverage area into several grid unit areas, each grid unit area includes at least one power energy storage device, and set a unique unit area identifier for each grid unit area;

[0060] A power supply and demand state acquisition module 402, configured to acquire the power supply and demand state vector of each grid unit area, and the power supply and demand state vector includes an electricity load gap index and the corresponding unit area identifier;

[0061] A collaborative dispatching optimization module 404, configured to input all the power supply and demand state vectors into a preset power collaborative dispatching space for optimization processing to obtain an optimal collaborative dispatching plan within the grid coverage area;

[0062] A dispatching processing execution module 406, configured to perform actual dispatching processing based on the optimal collaborative dispatching plan.

[0063] In the embodiments of the present invention, the grid unit division module 400 can be used to execute step 100 in the above method embodiments, the power supply and demand state acquisition module 402 can be used to execute step 200 in the above method embodiments, and the collaborative dispatching optimization module and the dispatching processing execution module can be used to execute step 300 in the above method embodiments. Through the collaborative work of the above modules, the device can effectively integrate multiple independent energy storage systems into the existing power network, ensure good collaborative operation between the two, so as to achieve the goal of optimizing resource allocation and improving economic benefits, not only improving the power grid operation efficiency, reducing costs, but also enhancing power supply reliability and user experience.

[0064] It can be understood that the structure schematically shown in the embodiments of the present invention does not constitute a specific limitation on a dispatching optimization device for independent energy storage and grid coordination. In other embodiments of the present invention, a dispatching optimization device for independent energy storage and grid coordination may include more or fewer components than shown, or combine certain components, or split certain components, or have different component arrangements. The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0065] Regarding the information interaction, execution process, etc. among the various modules within the above-mentioned device, since they are based on the same concept as the method embodiments of the present invention, the specific content can be referred to the descriptions in the method embodiments of the present invention, and will not be elaborated herein.

[0066] An embodiment of the present invention further provides an electronic device, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, a scheduling optimization method for independent energy storage and grid coordination in any embodiment of the present invention is implemented.

[0067] An embodiment of the present invention further provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the processor is caused to execute a scheduling optimization method for independent energy storage and grid coordination in any embodiment of the present invention.

[0068] Specifically, a system or device equipped with a storage medium can be provided. Software program codes for implementing the functions in any of the above embodiments are stored on the storage medium, and the computer (or CPU or MPU) of the system or device is caused to read and execute the program codes stored in the storage medium.

[0069] In this case, the program codes read from the storage medium itself can implement the functions in any of the above embodiments. Therefore, the program codes and the storage medium storing the program codes constitute a part of the present invention.

[0070] Embodiments of the storage medium for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program codes can be downloaded from a server computer via a communication network.

[0071] Furthermore, it should be clear that not only can the functions in any of the above embodiments be implemented by executing the program codes read by a computer, but also by causing an operating system operating on the computer based on the instructions of the program codes to complete part or all of the actual operations.

[0072] In addition, it can be understood that the program codes read from the storage medium are written into the memory provided in an expansion board inserted into the computer or into the memory provided in an expansion module connected to the computer, and then based on the instructions of the program codes, the CPUs, etc. installed on the expansion board or the expansion module are caused to execute part and all of the actual operations, thereby implementing the functions in any of the above embodiments.

[0073] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising said element.

[0074] Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes various media that can store program codes, such as ROM, RAM, magnetic disks or optical discs.

[0075] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dispatch optimization method for coordinated independent energy storage and power grid, characterized in that: The method comprises: Divide the grid coverage area into a number of grid unit areas, each grid unit area includes at least one power energy storage device, and set a unique unit area identifier for each grid unit area; Collect grid load data and energy storage equipment status data in each grid unit area; For each grid unit area, the collected grid load data and energy storage device status data are respectively input into the pre-trained power demand analysis model and energy storage device status assessment model, and the power load demand index and energy storage device supply index are output; the power load demand index is used to characterize the difference between the power load that the grid can provide for the grid unit area and the power load consumed by the grid unit area; Based on the power load demand index and the energy storage device supply index, the power load gap index of the grid unit area is calculated; when the power load gap index is positive, it means that there is a power supply gap in the grid unit area and power needs to be allocated from other areas; when the power load gap index is negative, it means that the power supply in the area is sufficient and the energy storage device can release excess power to support other areas; Heterogeneously combining the power load gap index with the corresponding unit area identifier to obtain a power supply and demand state vector of the power grid unit area; Input all power supply and demand state vectors into a preset power coordinated dispatching space for optimization processing, obtain the optimal coordinated dispatching scheme within the power grid coverage area, and perform dispatching processing based on the optimal coordinated dispatching scheme; The method for performing optimization processing in a preset power coordinated dispatching space includes: Classifying all the power supply and demand state vectors to obtain a load surplus unit area set and a load deficit unit area set; Arrange the grid unit areas in the load surplus unit area set in ascending order according to the corresponding electricity load gap index to obtain a load surplus unit area sequence; and mark the grid unit areas in the load surplus unit area sequence from the first surplus unit area to the nth surplus unit area in sequence; Using the unit area identifier, determine whether the power load gap index corresponding to the power grid unit area closest to the first surplus unit area in the load deficit unit area set is greater than the power load gap index of the first surplus unit area, and update the load deficit unit area set and the load surplus unit area set according to the determination result; Repeat the determination process until the grid unit areas in the load surplus unit area set and / or the load deficit unit area set are cleared; Based on the zeroing process, at least two grid unit areas participating in the addition are respectively taken as mutual collaborative scheduling objects to generate an optimal collaborative scheduling plan within the grid coverage area.

2. The dispatch optimization method for independent energy storage and power grid coordination according to claim 1, characterized in that: The unit area identifier is used to represent the relative position coordinates of the power grid unit area within the power grid coverage area.

3. The dispatch optimization method for independent energy storage and power grid coordination according to claim 1, characterized in that: The judgment process is as follows: If it is greater, the power load gap index corresponding to the grid unit area closest to the first surplus unit area is modified to the sum of the two; and the first surplus unit area is deleted from the load surplus unit area set, and the rankings of the remaining surplus unit areas are increased in sequence; If it is less than, the power load gap index corresponding to the first surplus unit area is modified to the sum of the two, and the load surplus unit area set is re-arranged in ascending order; and the grid unit area closest to the first surplus unit area is deleted from the load deficit unit area set; If they are equal, the grid unit area closest to the first surplus unit area is deleted from the load deficit unit area set, and the first surplus unit area is deleted from the load surplus unit area set, and the remaining surplus unit areas are ranked in ascending order.

4. The dispatch optimization method for independent energy storage and power grid coordination according to claim 3, characterized in that: In response to the load surplus unit area set being cleared first, generating and displaying warning information of power load deficit in the area covered by the power grid; In response to the load-deficit unit area set being cleared first, generating and displaying warning information of the power storage load surplus in the area covered by the power grid; In response to the simultaneous clearing of the load-deficit unit area set and the load-surplus unit area set, information on electricity self-sufficiency in the area covered by the power grid is generated and displayed.

5. The dispatch optimization method for coordinated independent energy storage and power grid according to claim 1, characterized in that: The grid load data includes real-time power load, historical load curve and load forecast data; the energy storage device status data includes current energy storage capacity, charge and discharge power, charge and discharge efficiency and remaining power.

6. A dispatch optimization device for independent energy storage and power grid coordination, the device is applied to the dispatch optimization method for independent energy storage and power grid coordination as claimed in claim 1, characterized in that: The device comprises: A grid unit division module, used to divide the grid coverage area into a plurality of grid unit areas, each grid unit area includes at least one power energy storage device, and a unique unit area identifier is set for each grid unit area; A power supply and demand state acquisition module, used to acquire a power supply and demand state vector of each power grid unit area, wherein the power supply and demand state vector includes a power load gap index and a corresponding unit area identifier; The coordinated dispatch optimization module is used to input all power supply and demand state vectors into the preset power coordinated dispatch space for optimization processing to obtain the optimal coordinated dispatch solution within the power grid coverage area; The scheduling processing execution module is used to perform actual scheduling processing based on the optimal collaborative scheduling solution.

7. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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