Distributed comprehensive energy data management and application method and system

Abstract

The invention discloses a distributed comprehensive energy data management and application method and system, and belongs to the technical field of data and energy management, and the method comprises the steps: obtaining distributed comprehensive energy data; based on the calculation performance parameters and the communication performance parameters of the calculation nodes, the comprehensive energy data are distributed to the appropriate calculation nodes, and the data calculated and processed by the calculation nodes are stored in a distributed database; formulating an optimal search plan for the distributed database, and acquiring required data from the distributed database by using the plan; and on the basis of the acquired required data, constructing a load control model with the minimum cost, and on the basis of the model, acquiring a comprehensive energy management scheme with the minimum cost in the target area. The operation cost of the power system can be reduced, and the utilization efficiency of data is improved.

Description

Technical Field

[0001] The present invention relates to the field of data and energy management technology, and in particular to a distributed integrated energy data management and application method and system. Background Art

[0002] Integrated energy has become a key direction for future energy development. Integrated energy generation utilizes renewable resources such as wind, solar, and hydropower, and will deliver electricity to consumers via smart grids. However, integrated energy systems generate data in dispersed locations, resulting in massive amounts of daily power generation data. Promoting distributed energy data management has become an inherent requirement for integrated energy generation. By acquiring, managing, and applying integrated energy data, the overall efficiency of integrated energy can be improved, preventing power shortages and energy waste, while also reducing the overall operating costs of the transmission and power generation system.

[0003] Currently, energy data management still faces challenges such as slow access, delayed computation, and inconvenient queries. Applications also face issues such as insufficient data utilization and high system costs. To reduce energy data access time, lower system operating costs, and improve the efficiency of energy data query and usage, considering data scheduling strategies, search strategies, and application solutions, rationally allocating computing nodes, and developing optimal search plans and load control models can effectively improve computing efficiency, achieve accurate and rapid data searches, and reduce system costs. Therefore, in energy data management, how to formulate scheduling and search strategies and implement application solutions is a pressing issue that needs to be addressed. Summary of the Invention

[0004] The present invention provides a distributed integrated energy data management and application method and system to solve the current problems of slow call speed, slow calculation and inconvenient query in energy data management, as well as the technical problems of insufficient data utilization and high system cost in energy data application.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In one aspect, the present invention provides a distributed integrated energy data management and application method, comprising:

[0007] Obtaining distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different regions, different levels and different sources in the integrated energy power system;

[0008] Based on the computing performance parameters and communication performance parameters of the computing nodes, the distributed integrated energy data is allocated to appropriate computing nodes, and the data processed by the computing nodes is stored in a distributed database;

[0009] Formulate an optimal search plan for the distributed database, and use the optimal search plan to obtain required data from the distributed database;

[0010] Based on the acquired required data, a cost-minimizing load control model is constructed, and based on the load control model, a cost-minimizing integrated energy management solution in a target area is obtained.

[0011] Furthermore, the obtaining of distributed integrated energy data includes:

[0012] Divide each region into multiple subgrids based on grid characteristics, with each subgrid equipped with a network platform; wherein the grid characteristics include: grid topology, administrative regions, and electrical equipment;

[0013] Monitor and manage energy in the corresponding subgrids through the network platform; collect distributed integrated energy data from the corresponding regional substations through the SCADA collection servers of each platform and store them in a real-time database;

[0014] The computing console requests data from the real-time database server to obtain the collected distributed integrated energy data.

[0015] Furthermore, based on the computing performance parameters and communication performance parameters of the computing nodes, the distributed integrated energy data is allocated to appropriate computing nodes, and the data processed by the computing nodes is stored in a distributed database, including:

[0016] The computing console sends data and computing requests to the computing server;

[0017] The computing server uses an improved Min-min algorithm combined with the computing performance parameters and communication performance parameters of the computing nodes in the subgrid to distribute the distributed integrated energy data to different computing nodes to achieve optimal scheduling;

[0018] The computing node processes the data assigned to it locally, and the processed data is sent back to the computing console via the computing server.

[0019] The computing console sends the data processed by the computing nodes to the database server, and the database server stores the data into a historical database; wherein the historical database is a distributed database.

[0020] Furthermore, the improved Min-min algorithm is combined with the computing performance parameters and communication performance parameters of the computing nodes in the subgrid to allocate the distributed integrated energy data to different computing nodes to achieve optimal scheduling, including:

[0021] In grid computing, quantify the quality of service and earliest completion time;

[0022] Based on the quantitative results of service quality and earliest completion time, the overall benefit is calculated; based on the overall benefit, tasks are scheduled, and distributed integrated energy data is allocated to different computing nodes to achieve optimal scheduling.

[0023] Furthermore, the service quality is quantified using task priority and execution cost, expressed as:

[0024] U1=a1×p+a2×c

[0025] Among them, U1 represents the quantified result of service quality; p represents the quantified and normalized task priority; c represents the normalized execution cost; a1 and a2 represent the weights corresponding to p and c respectively;

[0026] The earliest completion time is quantified using a performance function, expressed as:

[0027]

[0028] Among them, U2 represents the quantified result of the earliest completion time; t represents the completion time of a task at a certain node; t max and t min They represent the maximum and minimum completion time of the task respectively.

[0029] Furthermore, the overall benefit is expressed as:

[0030] U=b*U1+(1-b)*U2

[0031] Among them, U represents the overall benefit; b is the preset weight factor.

[0032] Furthermore, formulating an optimal search plan for the distributed database and using the optimal search plan to obtain required data from the distributed database includes:

[0033] After receiving the query request, the syntax analyzer generates a syntax tree based on the query request, and schedules the nearest node to obtain the entire data from the distributed database;

[0034] The optimizer selects the optimal query node, optimizes the query request as a whole based on the syntax tree, and generates a query command.

[0035] The query engine issues query commands, and the query tree converts the query commands into an executable search space. The improved grey wolf algorithm is used to calculate the query method with the lowest cost in the query tree and obtain the optimal query solution.

[0036] The query node executes the query command according to the optimal query solution and returns the query result.

[0037] Furthermore, the improved grey wolf algorithm adds a proportional weight to each solution generated during execution; wherein, the proportional weight of the first solution is greater than 1; the proportional weight of the second solution is between 1 and 2; the proportional weight of the third solution is 1; the proportional weight of the first solution and the proportional weight of the second solution are dynamically updated.

[0038] Furthermore, the method of constructing a cost-minimizing load control model based on the acquired required data, and obtaining a cost-minimizing integrated energy management solution in the target area based on the load control model, includes:

[0039] Determine the parameter adjustment range of the adjustable load based on the required data obtained, and build a load control model that minimizes cost;

[0040] Based on the load control model, the operating cost is used as the objective function, and the genetic algorithm is used to solve the problem to obtain the state of the adjustable load and the power consumption; wherein the objective function is expressed as: P t =P RE +P grid +P CCHP +P ESS +P AL Among them, P t represents the total operating cost; P RE represents the operating cost of renewable energy; P grid represents the electricity purchase cost of the power grid; P CCHP represents the energy cost of the trigeneration system; P ESS represents the cost of the energy storage system; P AL represents the compensation cost of adjustable load;

[0041] According to the status of adjustable loads and power consumption, a comprehensive energy management plan with the lowest cost is obtained.

[0042] On the other hand, the present invention also provides a distributed integrated energy data management and application system, comprising:

[0043] A data acquisition module is used to acquire distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different regions, different levels and different sources in the integrated energy power system;

[0044] A data distribution and storage module is used to distribute the distributed integrated energy data acquired by the data acquisition module to appropriate computing nodes based on the computing performance parameters and communication performance parameters of the computing nodes, and store the data processed by the computing nodes in a distributed database;

[0045] A data reading module, configured to formulate an optimal search plan for the distributed database, and obtain required data from the distributed database using the optimal search plan;

[0046] The energy management plan formulation module is used to construct a cost-minimizing load control model based on the required data obtained by the data reading module, and obtain a comprehensive energy management plan with the minimum cost in the target area based on the load control model.

[0047] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory; wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the above method.

[0048] In yet another aspect, the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the above method.

[0049] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0050] The present invention obtains distributed integrated energy data; distributes the distributed integrated energy data to appropriate computing nodes based on the computing node's computing performance parameters and communication performance parameters, and stores the data processed by the computing nodes in a distributed database; formulates an optimal search plan for the distributed database and uses the search optimal plan to obtain the required data from the distributed database; constructs a cost-minimizing load control model based on the obtained required data, and, based on the load control model, obtains the lowest-cost integrated energy management solution within the target area. This combines an improved Min-Min algorithm, an improved Gray Wolf algorithm, and a genetic algorithm to achieve optimal data scheduling, optimal database query solutions, and, through data, reduces the operating costs of the power system, improves data utilization efficiency, and reduces overall costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 This is a flow chart of a distributed integrated energy data management and application method provided by an embodiment of the present invention;

[0053] Figure 2 This is a flowchart of an optimal scheduling strategy and storage of energy data provided by an embodiment of the present invention;

[0054] Figure 3 This is a flow chart for quickly acquiring required data from a distributed database provided by an embodiment of the present invention;

[0055] Figure 4 This is a flow chart of an embodiment of the present invention for obtaining a comprehensive energy management solution with the lowest cost in a target area;

[0056] Figure 5 This is a block diagram of a distributed integrated energy data management and application system provided by an embodiment of the present invention;

[0057] Figure 6 is a block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0059] First, it should be noted that in the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplarily" is intended to present concepts in a concrete manner. In addition, in the embodiments of the present invention, the meaning of "and / or" can be both or either of the two.

[0060] First embodiment

[0061] This embodiment provides a distributed integrated energy data management and application method, which can be implemented by electronic devices. The execution process of the method is as follows: Figure 1 As shown, the following steps are included:

[0062] S1, obtaining distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different levels and different sources in different regions of the integrated energy power system;

[0063] It should be noted that, because large-scale power grids mostly adopt a hierarchical and zoned management system, the collected data may come from different levels and different sources; each region is divided into subgrids according to the characteristics of the power grid, and the power grid characteristics include factors such as the power grid topology, administrative regions and electrical equipment. Based on this, the data acquisition process of this embodiment is as follows: each subgrid is monitored and energy managed through an equal number of network platforms; the SCADA acquisition server of each regional platform, that is, the data acquisition, monitoring and control server, collects comprehensive energy data from the substation in the region and stores it in a real-time database; it should be noted that the real-time database here is different from the database for storing calculated data. The real-time database stores the collected raw data, not status data; after completing the storage of the raw data, the computing console requests data from the real-time database server to obtain the collected comprehensive energy data from different levels and different sources.

[0064] S2, based on the computing performance parameters and communication performance parameters of the computing nodes, distributes the comprehensive energy data to the appropriate computing nodes, and stores the data processed by the computing nodes in the distributed database;

[0065] Specifically, in this embodiment, the implementation process of the above S2 is as follows: Figure 2 As shown, including:

[0066] S21, the computing console sends data and computing requests to the computing server;

[0067] S22, the computing server uses the improved Min-min algorithm combined with the computing performance parameters and communication performance parameters of the sub-grid computing nodes to distribute the comprehensive energy data to different computing nodes to achieve optimal scheduling;

[0068] S23, the computing node performs computational processing on the data, and the processed data is sent back to the computing console via the computing server. The console organizes the data and sends it to the database server, which stores the data in the historical database.

[0069] It should be noted that the Min-Min algorithm is a classic algorithm in grid task scheduling. Its basic idea is to select the task with the shortest earliest completion time among the tasks to be executed for scheduling during each task scheduling process. In grid computing, service quality and earliest completion time are two extremely important indicators in the computing process. Service quality can be quantified using task priority and execution cost: U1 = a1×p+a2×c, where p represents the quantified and normalized task priority, c represents the normalized execution cost, and a1 and a2 represent their respective weights, which can be adjusted. The earliest completion time can be quantified using the performance function: Among them, t represents the completion time of a task on a certain node, tmax and t min Represent the maximum and minimum completion times for the task on any node that meets its computational requirements, respectively. The overall benefit can be expressed as: U = U1 + U2. An improvement lies in adding a weighting factor, b, to the quantization process, expressed as: U = b*U1 + (1-b)*U2. By adjusting this weighting factor, the proportions of U1 and U2 in U can be constantly adjusted, thus balancing task priority, execution cost, and execution time, achieving optimal scheduling.

[0070] S3, formulates the optimal search plan for the distributed database, and uses the optimal search plan to obtain the required data from the distributed database;

[0071] Specifically, in this embodiment, the implementation process of the above S3 is as follows: Figure 3 As shown, including:

[0072] S31, after receiving the query request, the syntax analyzer generates a syntax tree according to the query request, and schedules the nearest node to obtain the entire data from the distributed database;

[0073] S32, through the optimizer, select the optimal query node, optimize the query request as a whole according to the syntax tree, and generate a query statement;

[0074] S33, using the query engine to issue a query command, the query tree converts the query command into an executable search space, and uses the improved gray wolf algorithm to calculate the query method with the lowest cost in the query tree to obtain the optimal query solution;

[0075] S34, the query node executes the query command according to the solution and returns the query result.

[0076] By following the above steps, you can quickly obtain the required data from a large amount of data in a distributed database.

[0077] It should be noted that the Gray Wolf Optimization Algorithm is a classic solution for optimizing distributed database query processes. It simulates the social hierarchy of wolves and classifies the generated solutions into three levels: α, β, and δ. The process of searching for the required content can be expressed as follows: in, Represents the distance vector between each solution and the target solution, t represents the current number of iterations, represents the position vector of the target solution at t iterations, represents the position vector of the solution at t iterations, is a random variable that controls the search range of the algorithm, The perturbation vector of the solution. Can be expressed as Substituting the position vector of the calculated solution into the equation, we get: Where i = 1, 2, 3, j = α, β, δ, which represents the position update rule of each solution in the process of tracking the target solution. The process of each solution approaching the target solution can be expressed as: The improvement is that a variable proportional weight can be added to each solution of the algorithm, and the calculation formula is as follows: In this case, k1>1, 1<k2<2, and k3=1. In this case, the values ​​of k1 and k2 can be updated dynamically. Because each solution has a different fitness, k1 has a wider range of variation, making the α solution more influential during the update process. k2 has a smaller range of variation, but to a certain extent, it can also expand the influence of the β solution during the update process. The value of k3 is limited to 1, meaning that the δ solution maintains its original influence during the update process. This improvement makes solutions with higher fitness more influential during the search for the target solution, enabling the algorithm to converge faster and more accurately and quickly determine the target solution, allowing the algorithm to obtain the optimal query solution at the lowest cost.

[0078] S4, based on the acquired required data, constructing a cost-minimizing load control model, and obtaining a cost-minimizing integrated energy management solution in the target area based on the load control model;

[0079] Specifically, in this embodiment, the implementation process of the above S4 is as follows: Figure 4 As shown, including:

[0080] S41, determining a parameter adjustment range of the adjustable load based on the acquired energy data, and constructing a load control model of the system;

[0081] S42, setting key algorithm parameters, taking operating cost as the objective function, solving with a genetic algorithm, and obtaining the state of the adjustable load and the power consumption;

[0082] S43, obtaining a comprehensive energy management solution with minimum cost based on the obtained solution and related parameters.

[0083] It should be noted that the operating cost consists of five parts: the operating cost of renewable energy, the power purchase cost of the power grid, the energy cost of the trigeneration system, the cost of the energy storage system and the compensation cost of the adjustable load after adjustment. The objective function is: P t =P RE +P grid +P CCHP +P ESS +P AL , P t represents the total operating cost, P RE represents the operating cost of renewable energy, P gridrepresents the power purchase cost of the power grid, P CCHP represents the energy cost of the trigeneration system, P ESS represents the cost of the energy storage system, P AL Represents the compensation cost of adjustable load.

[0084] In summary, this embodiment provides a distributed integrated energy data management and application method. This method acquires distributed integrated energy data; allocates the integrated energy data to appropriate computing nodes based on the computing node's computing performance parameters and communication performance parameters, and stores the processed data in a distributed database; formulates an optimal search plan for the distributed database and uses this plan to acquire the required data from the database; constructs a cost-minimizing load control model based on the acquired data, and, based on the load control model, acquires the lowest-cost integrated energy management solution within the target area. This method, combined with an improved Min-Min algorithm, a Gray Wolf algorithm, and a genetic algorithm, achieves optimal data scheduling, optimal database query solutions, and reduces the operating costs of the power system through data, improving data utilization efficiency and lowering overall costs.

[0085] Second embodiment

[0086] This embodiment provides a distributed integrated energy data management and application system. The structure of the distributed integrated energy data management and application system is as follows: Figure 5 As shown, it includes the following modules:

[0087] A data acquisition module is used to acquire distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different regions, different levels and different sources in the integrated energy power system;

[0088] A data distribution and storage module is used to distribute the distributed integrated energy data acquired by the data acquisition module to appropriate computing nodes based on the computing performance parameters and communication performance parameters of the computing nodes, and store the data processed by the computing nodes in a distributed database;

[0089] A data reading module, configured to formulate an optimal search plan for the distributed database, and obtain required data from the distributed database using the optimal search plan;

[0090] The energy management plan formulation module is used to construct a cost-minimizing load control model based on the required data obtained by the data reading module, and obtain a comprehensive energy management plan with the minimum cost in the target area based on the load control model.

[0091] It should be noted that, for the sake of convenience, Figure 5Only the main components of the system are shown. Furthermore, the distributed integrated energy data management and application system of this embodiment corresponds to the distributed integrated energy data management and application method of the first embodiment described above. The functions implemented by each functional module in the distributed integrated energy data management and application system of this embodiment correspond one-to-one to each process step in the distributed integrated energy data management and application method of the first embodiment described above; therefore, detailed descriptions are omitted here.

[0092] Third embodiment

[0093] This embodiment provides an electronic device, such as Figure 6 As shown, the electronic device includes: a processor and a memory; wherein the processor and the memory can be connected via a communication bus; the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment described above. In addition, the electronic device may also include a transceiver; the processor and the transceiver can be connected via a communication bus, and the transceiver is used to communicate with other devices.

[0094] Next, combine Figure 6 A detailed introduction to the various components of the electronic device is given below:

[0095] Among them, the processor is the control center of the electronic device, and the electronic device may include multiple processors, each of which may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here can be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPUs), or other general-purpose processors, application specific integrated circuits (ASICs), or one or more integrated circuits configured to implement an embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor can perform various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0096] In a specific implementation, as an embodiment, the processor may include one or more CPUs, such as Figure 6 The CPU0 and CPU1 shown in FIG are, of course, only exemplary.

[0097] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0098] Optionally, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and accessed through the interface circuit ( Figure 6 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0099] The transceiver may include a receiver and a transmitter ( Figure 6 The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function. The transceiver can be integrated with the processor or exist independently and communicate with the electronic device through the interface circuit ( Figure 6 (not shown) is coupled to the processor, which is not specifically limited in this embodiment of the present invention.

[0100] In addition, it should be noted that Figure 6 The structure of the electronic device shown in the figure does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or may combine certain components, or arrange the components differently. In addition, the technical effects achieved by the electronic device when executing the method of the first embodiment can refer to the technical effects described in the first embodiment above, and therefore will not be repeated here.

[0101] Fourth embodiment

[0102] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device. The instructions stored therein can be loaded by a processor in a terminal to execute the method described above.

[0103] Furthermore, it should be noted that the present invention may be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention may take the form of a fully or partially hardware embodiment, a fully or partially software embodiment, or an embodiment combining software and hardware aspects. Furthermore, when implemented using software, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired connection (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium. The semiconductor medium may be a solid state drive.

[0104] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0106] It should also be noted that, in this document, relational terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. The terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. Without further limitation, an element defined by the phrase "comprising a..." does not preclude the presence of other identical elements in the process, method, article, or terminal device comprising the element. In addition, the term "and / or" is merely a description of an associative relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: the presence of A alone, the presence of A and B simultaneously, or the presence of B alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding. "At least one" means one or more, and "more" means two or more. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0107] In addition, it can be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0108] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0109] In the several embodiments provided herein, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of functional modules / units is merely a logical functional division. In actual implementation, other division methods may be used, such as multiple units or components being combined or integrated into another device, or some features being ignored or not implemented. Furthermore, the coupling or direct coupling or communication connection shown or discussed between each other may be through some interface, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs. In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0110] If the method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0111] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention. It should be noted that, although preferred embodiments of the present invention have been described, those skilled in the art, once understanding the basic inventive concepts of the present invention, may make various improvements and modifications without departing from the principles of the present invention. Such improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as covering the preferred embodiments and all variations and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A distributed integrated energy data management and application method, characterized in that: include: Obtaining distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different levels and different sources in different regions in the integrated energy power system; Based on the computing performance parameters and communication performance parameters of the computing nodes, the distributed integrated energy data is allocated to appropriate computing nodes, and the data processed by the computing nodes is stored in a distributed database; Formulate an optimal search plan for the distributed database, and use the optimal search plan to obtain required data from the distributed database; Based on the acquired required data, a cost-minimizing load control model is constructed, and based on the load control model, a comprehensive energy management solution with minimum cost in the target area is obtained.

2. The distributed integrated energy data management and application method according to claim 1, characterized in that: The obtaining of distributed integrated energy data includes: Divide each region into multiple subgrids according to the characteristics of the power grid, and equip each subgrid with a network platform; wherein the power grid characteristics include: power grid topology, administrative regions, and electrical equipment; Monitor and manage energy of corresponding subgrids through network platforms; collect distributed integrated energy data from corresponding regional substations through SCADA acquisition servers of each platform and store them in real-time databases; The computing console requests data from the real-time database server to obtain the collected distributed integrated energy data.

3. The distributed integrated energy data management and application method according to claim 1, characterized in that: Based on the computing performance parameters and communication performance parameters of the computing nodes, the distributed integrated energy data is distributed to appropriate computing nodes, and the data processed by the computing nodes is stored in a distributed database, including: The computing console sends data and computing requests to the computing server; The computing server uses the improved Min-min algorithm combined with the computing performance parameters and communication performance parameters of the computing nodes in the subgrid to distribute the distributed integrated energy data to different computing nodes to achieve optimal scheduling; The computing node performs computing processing on the data assigned to it locally, and the data processed by the computing node is sent back to the computing console via the computing server; The computing console sends the data processed by the computing nodes to the database server, and the database server stores the data into a historical database; wherein the historical database is a distributed database.

4. The distributed integrated energy data management and application method according to claim 3, characterized in that: The improved Min-min algorithm is combined with the computing performance parameters and communication performance parameters of the computing nodes in the subgrid to allocate the distributed comprehensive energy data to different computing nodes to achieve optimal scheduling, including: In grid computing, quantify the quality of service and earliest completion time; Based on the quantitative results of the service quality and the earliest completion time, the overall benefit is calculated; based on the overall benefit, the task is scheduled, and the distributed integrated energy data is allocated to different computing nodes to achieve optimal scheduling.

5. The distributed integrated energy data management and application method according to claim 4, characterized in that: The service quality is quantified using task priority and execution cost, expressed as: U1=a1×p+a2×c Among them, U1 represents the quantified result of service quality; p represents the quantified and normalized task priority; c represents the normalized execution cost; a1 and a2 represent the weights corresponding to p and c respectively; The earliest completion time is quantified using a performance function, expressed as: Among them, U2 represents the earliest completion time quantification result; t represents the completion time of a task on a certain node; t max and t min They represent the maximum and minimum completion time of the task respectively.

6. The distributed integrated energy data management and application method according to claim 5, characterized in that: The overall benefit is expressed as: U=b*U1+(1-b)*U2 Among them, U represents the overall benefit; b is the preset weight factor.

7. The distributed integrated energy data management and application method according to claim 1, characterized in that: The step of formulating an optimal search plan for the distributed database and obtaining required data from the distributed database using the optimal search plan includes: After receiving the query request, the syntax analyzer generates a syntax tree based on the query request, and schedules the nearest node to obtain the overall data from the distributed database; The optimizer selects the best query node, optimizes the query request as a whole according to the syntax tree, and generates a query command. The query engine is used to issue query commands, and the query tree converts the query commands into an executable search space. The improved grey wolf algorithm is used to calculate the query method with the lowest cost in the query tree to obtain the optimal query solution. The query node executes the query command according to the optimal query solution and returns the query result.

8. The distributed integrated energy data management and application method according to claim 7, characterized in that: The improved grey wolf algorithm adds a proportional weight to each solution generated during execution; wherein the proportional weight of the first solution is greater than 1; the proportional weight of the second solution is between 1 and 2; the proportional weight of the third solution is 1; the proportional weight of the first solution and the proportional weight of the second solution are dynamically updated.

9. The distributed integrated energy data management and application method according to claim 1, characterized in that: The method of constructing a cost-minimizing load control model based on the acquired required data, and obtaining a cost-minimizing integrated energy management solution in a target area based on the load control model, includes: Determine the parameter adjustment range of the adjustable load based on the required data obtained, and build a load control model that minimizes the cost; Based on the load control model, the operation cost is used as the objective function, and the genetic algorithm is used to solve the problem to obtain the state and power consumption of the adjustable load; wherein the objective function is expressed as: P t =P RE +P grid +P CCHP +P ESS +P AL ; Among them, P t represents the total operating cost; P RE represents the operating cost of renewable energy; P grid P represents the power purchase cost of the power grid; CCHP represents the energy cost of the trigeneration system; P ESS represents the cost of the energy storage system; P AL represents the compensation cost of adjustable load; According to the status of adjustable loads and electricity consumption, a comprehensive energy management solution with the lowest cost is obtained.

10. A distributed integrated energy data management and application system, characterized in that: include: A data acquisition module, used to acquire distributed integrated energy data; wherein the distributed integrated energy data refers to integrated energy data from different levels and different sources in different regions in the integrated energy power system; A data distribution and storage module is used to distribute the distributed integrated energy data acquired by the data acquisition module to appropriate computing nodes based on the computing performance parameters and communication performance parameters of the computing nodes, and store the data processed by the computing nodes into a distributed database; A data reading module, used to formulate an optimal search plan for the distributed database, and use the optimal search plan to obtain required data from the distributed database; The energy management plan formulation module is used to construct a cost-minimizing load control model based on the required data obtained by the data reading module, and to obtain a comprehensive energy management plan with the lowest cost in the target area based on the load control model.

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