CN119721318A - A multi-energy collaborative optimization management system and method - Google Patents
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Abstract
The invention provides a multi-energy collaborative optimization management system and a multi-energy collaborative optimization management method, which belong to the technical field of collaborative optimization, wherein the module comprises a demand module, a system design module, an algorithm module and a packaging module, wherein the demand module is used for determining an energy object of the multi-energy collaborative optimization management system based on target demands and configuring a sensor to acquire real-time data of all energy types, the system design module is used for designing a multi-energy collaborative optimization management system framework in a test environment, storing the real-time data of all energy types into a real-time database, the algorithm module is used for configuring an initial model and training the initial model according to the real-time data of all energy types and historical data of corresponding energy types to acquire a first model which is a system algorithm part, and the packaging module is used for adding a main function into the multi-energy collaborative optimization management system framework to acquire a system packaging file, improving the resource efficiency, promoting the application of renewable energy and enhancing the optimization promotion degree.
Description
Technical Field
The invention relates to the technical field of collaborative optimization, in particular to a multi-energy collaborative optimization management system and method.
Background
At present, with the gradual development of big data, the method is increasingly applied to various fields, wherein the management field of energy sources is an important basic stone for various factory production, but a multi-energy system relates to various energy types, and the data formats, the acquisition frequencies and the communication protocols of different energy sources have challenges for data integration and real-time analysis.
Therefore, the invention provides a multi-energy collaborative optimization management system and a multi-energy collaborative optimization management method.
Disclosure of Invention
The invention provides a multi-energy collaborative optimization management system and a multi-energy collaborative optimization management method, which are used for maintaining and updating a system configuration to an actual environment by determining a target energy type of the system, acquiring energy parameters according to a sensor, designing an optimization model algorithm, generating a system architecture.
In one aspect, the present invention provides a multi-energy collaborative optimization management system, including:
The demand module is used for determining an energy object of the multi-energy collaborative optimization management system based on target demand and configuring a sensor to acquire real-time data of all energy types;
The system design module designs a multi-energy collaborative optimization management system architecture in a test environment and stores real-time data of all energy types into a real-time database;
The algorithm module is used for configuring an initial model, training the initial model according to real-time data of all energy types and historical data of corresponding energy types to obtain a first model, wherein the first model is a system algorithm part;
The system packaging module adds the main functions into the multi-energy collaborative optimization management system architecture to obtain a first system, and packages the first system to obtain a system packaging file;
And the configuration module is used for configuring the system packaging file to an actual environment, and monitoring the performance parameters of the first system for maintenance and updating.
In another aspect, the demand module includes:
the demand determining unit is used for determining all energy objects analyzed by the system according to a preset target demand;
and the screening unit is used for preferentially selecting an energy object with energy saving potential larger than national standard as a main monitoring object.
In another aspect, the demand module includes:
the acquisition and analysis unit is used for determining the type and the requirement of the monitored parameters according to the main monitored object;
And the sensor unit is used for matching the corresponding sensors according to the parameter types, determining the initial configuration parameters of the sensors according to the parameter requirements, configuring all the sensors to the designated installation positions and obtaining real-time data of all the energy types.
In another aspect, the system design module includes:
The architecture generating unit determines a system architecture mode according to target requirements, and designs components of the multi-energy collaborative optimization management system, wherein the components comprise a function tree formed by main function components and sub-function components;
the database unit initializes a system database according to a preset standard database field table, performs field refinement based on target requirements and generates different functional databases, wherein all the functional databases form a real-time database assembly;
The storage unit is used for initializing the real-time data of all the energy types acquired by the sensor to obtain first real-time data, and carrying out time standardization parameter correspondence processing on the real-time data of any energy type according to a standard time interval to obtain standard real-time data;
standard real-time data for all energy types is stored in a real-time database.
In another aspect, the algorithm module includes:
the model unit is used for constructing a collaborative optimization objective function, taking the minimum running cost of any energy source type as a target, and constructing a single scheduling model as follows:
Wherein, C i represents the total operation cost of the ith energy source, A i0 represents the operation cost before the energy source is optimized, A i1 represents the single operation parameter characteristic value of the energy source, ε i represents the parameter cost conversion coefficient of the ith energy source, and ρ i represents the operation error coefficient of the ith energy source;
then an initial scheduling optimization model of all the energy sources is constructed as follows:
Wherein n represents n energy sources in total to participate in optimization, C represents a preset cost threshold value, and sigma i represents the cost ratio of the ith energy source;
Taking real-time data of all energy types as a test set, taking historical data of corresponding energy types as a training set, and training an initial scheduling optimization model according to the training set to obtain a training model;
Inputting the test set into a first model to obtain an actual result, comparing the actual result with a predicted result to evaluate the deviation of model indexes, and adjusting model parameters for retraining to obtain the first model, wherein the first model is a system algorithm part.
In another aspect, the system packaging module includes:
The main functional unit adopts an energy internet technology to connect various main functions together through an http protocol, realizes multiple data interaction through an API interface, and adds the multiple data interaction into a multi-energy collaborative optimization management system architecture to obtain a first system;
The packaging unit is used for deploying a first system in a test environment, integrally testing the first system and verifying the correctness of the data stream, if the data stream is abnormal, processing the abnormal occurrence position, and updating the first system to obtain a second system;
and packing the second system based on the packing tool and the dependent items to obtain a system packing file.
In another aspect, the configuration module includes:
the configuration unit is used for transmitting the system packaging file from the test environment to the actual environment, configuring the system parameter items based on the actual environment, and then completing the installation in the actual environment and starting the multi-energy collaborative optimization management system;
The monitoring unit is used for monitoring the operation log of the key performance parameters of the multi-energy collaborative optimization management system in real time according to the system monitoring tool;
And the maintenance unit is used for centrally managing the operation log, evaluating the system performance, modifying and packaging in the test environment according to the occurrence position of the performance abnormality, updating the system version and deploying and configuring the system version into the actual environment.
On the other hand, the invention provides a multi-energy collaborative optimization management method, which comprises the following steps:
Step 1, determining an energy object of a system based on target requirements, and configuring a sensor to acquire real-time data of all energy types;
Step 2, designing a multi-energy collaborative optimization management system architecture in a test environment, and storing real-time data of all energy types into a real-time database;
Step 3, configuring an initial model, training the initial model according to real-time data of all energy types and historical data of corresponding energy types to obtain a first model, wherein the first model is a system algorithm part;
step 4, adding the main functions into a multi-energy collaborative optimization management system architecture to obtain a first system, and packaging to obtain a system packaging file;
and 5, configuring the system packaging file to an actual environment, and monitoring performance parameters of the system for maintenance and updating.
Compared with the prior art, the invention has the beneficial effects that:
The invention provides a multi-energy collaborative optimization management system and a multi-energy collaborative optimization management method, which are used for maintaining and updating a system configuration to an actual environment by determining a target energy type of the system, acquiring energy parameters according to a sensor, designing an optimization model algorithm, generating a system architecture.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic structural diagram of a multi-energy collaborative optimization management system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a multi-energy collaborative optimization management method according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
as shown in fig. 1, a multi-energy collaborative optimization management system provided by an embodiment of the present invention includes:
The demand module is used for determining an energy object of the multi-energy collaborative optimization management system based on target demand and configuring a sensor to acquire real-time data of all energy types;
The system design module designs a multi-energy collaborative optimization management system architecture in a test environment and stores real-time data of all energy types into a real-time database;
The algorithm module is used for configuring an initial model, training the initial model according to real-time data of all energy types and historical data of corresponding energy types to obtain a first model, wherein the first model is a system algorithm part;
The system packaging module adds the main functions into the multi-energy collaborative optimization management system architecture to obtain a first system, and packages the first system to obtain a system packaging file;
And the configuration module is used for configuring the system packaging file to an actual environment, and monitoring the performance parameters of the first system for maintenance and updating.
In this embodiment, the target requirements define the functions and capabilities that the system needs to implement, and also provide a clear direction and framework for the design, development and implementation of the system.
In the embodiment, the multi-energy collaborative optimization management system is an integrated technical platform and aims to monitor, manage and optimize various energy resources in real time.
In this embodiment, the energy object refers to various energy resources in the system that need to be monitored and managed, including electric power, thermal energy, renewable energy sources (such as solar energy, wind energy), fuel gas, and the like.
In this embodiment, the real-time data refers to various energy related information that is collected, transmitted and processed in a timely and continuous manner during the operation of the system.
In this embodiment, the energy source type refers to different kinds of energy sources in the system, including electric power, heat energy, renewable energy sources (such as solar energy, wind energy), fuel gas and the like.
In this embodiment, the test environment refers to a stand-alone environment for developing, testing, and verifying software or system functions.
In this embodiment, the architecture refers to the overall structure and design scheme of the system, and relates to the relationships among different components, data flow, division of functional modules, technical selection, and the like.
In this embodiment, the real-time database is a database system specifically designed for processing and storing real-time data.
In this embodiment, the initial model refers to a preliminary decision model constructed based on existing data (real-time data and historical data).
In this embodiment, the first model refers to a trained and constructed model of the preliminary algorithm.
In this embodiment, the first system refers to a preliminary complete system formed after each module design and implementation.
In the embodiment, the system packaging file integrates the main functional modules of the whole system and configuration, dependency items, documents and the like thereof into a file which can be deployed and installed in the development process of the multi-energy collaborative optimization management system.
In this embodiment, the actual environment refers to a specific physical environment in which the system operates in the real world.
In this embodiment, the performance parameters are key indicators used to evaluate system performance, including response time, data throughput, system availability, resource utilization, etc.
In this embodiment, maintenance and updating
The technical scheme has the advantages that through real-time data acquisition and model training, efficient energy management and optimization decision is realized, sustainable development is supported, operation cost is reduced, system performance is improved, and resource utilization maximization is promoted.
Example 2:
on the basis of the above embodiment 1, the demand module includes:
the demand determining unit is used for determining all energy objects analyzed by the system according to a preset target demand;
and the screening unit is used for preferentially selecting an energy object with energy saving potential larger than national standard as a main monitoring object.
In this embodiment, energy saving potential refers to the amount of energy savings that can be achieved by implementing energy saving measures in a particular energy use scenario.
In this embodiment, the national standard refers to a standard formulated and issued by the national Standards Administration (SAC).
In this embodiment, the primary monitoring object refers to an energy object selected for important monitoring and management in the system analysis.
The technical scheme has the advantages that through clear demand units and energy-saving potential screening, high-efficiency energy objects are monitored preferentially, accurate analysis and resource optimization are achieved, energy saving and emission reduction are promoted, system compliance is enhanced, and sustainable development and environmental protection are promoted.
Example 3:
on the basis of the above embodiment 2, the demand module includes:
the acquisition and analysis unit is used for determining the type and the requirement of the monitored parameters according to the main monitored object;
And the sensor unit is used for matching the corresponding sensors according to the parameter types, determining the initial configuration parameters of the sensors according to the parameter requirements, configuring all the sensors to the designated installation positions and obtaining real-time data of all the energy types.
In this embodiment, the parameter types refer to various indexes and data for quantifying and evaluating the main monitoring object, such as power, electric quantity, heat, flow, temperature, and the like.
In this embodiment, the parameter requirements refer to specific conditions and criteria, such as accuracy, sampling frequency, range, etc., that need to be met for each monitored parameter during the monitoring and acquisition process.
In this embodiment, initial parameters
The technical scheme has the advantages that the real-time data acquisition of energy sources is realized through clear monitoring parameters and accurate sensor configuration, the data accuracy and the management efficiency are improved, support is provided for intelligent decision making and energy saving optimization, and sustainable development is promoted.
Example 4:
on the basis of the above embodiment 1, the system design module includes:
The architecture generating unit determines a system architecture mode according to target requirements, and designs components of the multi-energy collaborative optimization management system, wherein the components comprise a function tree formed by main function components and sub-function components;
the database unit initializes a system database according to a preset standard database field table, performs field refinement based on target requirements and generates different functional databases, wherein all the functional databases form a real-time database assembly;
The storage unit is used for initializing the real-time data of all the energy types acquired by the sensor to obtain first real-time data, and carrying out time standardization parameter correspondence processing on the real-time data of any energy type according to a standard time interval to obtain standard real-time data;
standard real-time data for all energy types is stored in a real-time database.
In this embodiment, the system architecture mode refers to a system structure and organization mode adopted in designing and constructing a system.
In this embodiment, components refer to individual modules or portions that make up a system, each component having its particular functions and responsibilities.
In this embodiment, the preset standard database field table is a key component in the multi-energy collaborative optimization management system, and defines the database structure and fields to be used in the system.
In this embodiment, the system database is a core component in the multi-energy collaborative optimization management system, and takes responsibility for data storage, management and access.
In this embodiment, initializing refers to creating a corresponding database and a table structure thereof according to a preset standard database field table, so as to lay a foundation for data storage.
In this embodiment, the first real-time data refers to a set of various energy data collected by the sensor for the first time after the system is started.
In this embodiment, the standard time interval refers to a preset time period for performing time normalization processing on real-time data.
In this embodiment, the time normalization parameter mapping process converts irregular data into a regular data format, and ensures consistency of the data in the time dimension.
In this embodiment, the standard real-time data refers to energy usage data generated according to a preset standard time interval after time normalization processing.
The technical scheme has the advantages that the multi-energy collaborative optimization management is realized through the modularized design and the standardized data processing, the system flexibility and the data consistency are improved, the high-efficiency decision is supported, and the energy management capability is enhanced.
Example 5:
On the basis of the above embodiment 1, the algorithm module includes:
the model unit is used for constructing a collaborative optimization objective function, taking the minimum running cost of any energy source type as a target, and constructing a single scheduling model as follows:
Wherein, C i represents the total operation cost of the ith energy source, A i0 represents the operation cost before the energy source is optimized, A i1 represents the single operation parameter characteristic value of the energy source, ε i represents the parameter cost conversion coefficient of the ith energy source, and ρ l represents the operation error coefficient of the ith energy source;
then an initial scheduling optimization model of all the energy sources is constructed as follows:
Wherein n represents n energy sources in total to participate in optimization, C represents a preset cost threshold value, and sigma i represents the cost ratio of the ith energy source;
Taking real-time data of all energy types as a test set, taking historical data of corresponding energy types as a training set, and training an initial scheduling optimization model according to the training set to obtain a training model;
Inputting the test set into a first model to obtain an actual result, comparing the actual result with a predicted result to evaluate the deviation of model indexes, and adjusting model parameters for retraining to obtain the first model, wherein the first model is a system algorithm part.
In this embodiment, the test set refers to a data set used to evaluate the performance of the trained model.
In this embodiment, the training set is a data set for training a machine learning model.
In this embodiment, the operating cost refers to the energy consumption resulting from operating a certain energy type under certain conditions.
In this embodiment, the actual results refer to the actual running costs and output data of each energy type obtained after the model is run under specific conditions.
In this embodiment, the predicted outcome refers to the predicted running cost and output of each energy type under specific conditions calculated by the model.
In this embodiment, model metrics are used to compare the differences between the model predictions and the actual results, and to measure the overall performance of the model, such as mean square error, mean absolute error, cost ratio, etc.
In this embodiment, the deviation refers to the difference between the model predicted result and the actual result.
The technical scheme has the advantages that the scheduling optimization framework aiming at minimizing the running cost is constructed, the real-time and historical data are utilized for training and adjusting, the energy utilization efficiency and scheduling accuracy are improved, and the systematic energy management and cost control are realized.
Example 6:
On the basis of embodiment 5 above, the system packaging module includes:
The main functional unit adopts an energy internet technology to connect various main functions together through an http protocol, realizes multiple data interaction through an API interface, and adds the multiple data interaction into a multi-energy collaborative optimization management system architecture to obtain a first system;
The packaging unit is used for deploying a first system in a test environment, integrally testing the first system and verifying the correctness of the data stream, if the data stream is abnormal, processing the abnormal occurrence position, and updating the first system to obtain a second system;
and packing the second system based on the packing tool and the dependent items to obtain a system packing file.
In the embodiment, the energy internet technology is an emerging technical architecture, and aims to realize intelligent management and optimal configuration of energy resources through deep fusion of an information communication technology and an energy technology.
In this embodiment, the http protocol is a protocol for transmitting data over the world wide web.
In this embodiment, the API interface is a set of rules and protocols for interaction and communication between the different components.
In this embodiment, an integration test is used to verify that the interaction and collaboration of the various modules or components in the system after integration is normal.
In this embodiment, a data stream refers to a process in which data flows from one location to another location in a computer system, network, or application.
In this embodiment, the packaging tool and the dependent items, the packaging tool is used to package the application and its dependent items into a unit that can be deployed, and is an external library or framework, e.g., database framework, web framework, etc., that is required for the application to run.
The technical scheme has the advantages that the energy management function is integrated through the HTTP protocol and the API interface, the correctness of the data stream is verified, the data stream is abnormal in treatment, and finally, the data stream is packed to generate the system file, so that high-efficiency integration, accuracy and flexible deployment are ensured, and future function expansion and system upgrading are supported.
Example 7:
on the basis of the above embodiment 1, the configuration module includes:
the configuration unit is used for transmitting the system packaging file from the test environment to the actual environment, configuring the system parameter items based on the actual environment, and then completing the installation in the actual environment and starting the multi-energy collaborative optimization management system;
The monitoring unit is used for monitoring the operation log of the key performance parameters of the multi-energy collaborative optimization management system in real time according to the system monitoring tool;
And the maintenance unit is used for centrally managing the operation log, evaluating the system performance, modifying and packaging in the test environment according to the occurrence position of the performance abnormality, updating the system version and deploying and configuring the system version into the actual environment.
In this embodiment, the system parameter items refer to variables and settings that need to be configured during the system operation, such as information of host address, port, database name, user credentials, etc.
In this embodiment, the system monitoring tool is an application program, such as Nagios, zabbix, grafana, for tracking, analyzing, and reporting system performance and operating status in real-time.
In this embodiment, the running log refers to various events, operations and status information recorded by the system during the running process.
In this embodiment, system performance refers to the ability and efficiency of a system to perform tasks under specific conditions, including response time, throughput, resource utilization, etc.
The technical scheme has the advantages that the deployment and parameter configuration of the system package file are realized through the configuration unit, the monitoring unit tracks key performance parameters in real time, the maintenance unit centrally manages the operation log and carries out system performance evaluation, and the efficient operation, continuous improvement and stability of the system are ensured.
Example 8:
As shown in fig. 2, the multi-energy collaborative optimization management method provided by the embodiment of the invention includes:
Step 1, determining an energy object of a system based on target requirements, and configuring a sensor to acquire real-time data of all energy types;
Step 2, designing a multi-energy collaborative optimization management system architecture in a test environment, and storing real-time data of all energy types into a real-time database;
Step 3, configuring an initial model, training the initial model according to real-time data of all energy types and historical data of corresponding energy types to obtain a first model, wherein the first model is a system algorithm part;
step 4, adding the main functions into a multi-energy collaborative optimization management system architecture to obtain a first system, and packaging to obtain a system packaging file;
and 5, configuring the system packaging file to an actual environment, and monitoring performance parameters of the system for maintenance and updating.
The technical scheme has the advantages that through real-time data acquisition and model training, efficient energy management and optimization decision is realized, sustainable development is supported, operation cost is reduced, system performance is improved, and resource utilization maximization is promoted.
It should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention, and not for limiting the same, and although the present invention has been described in detail with reference to the above-mentioned embodiments, it should be understood by those skilled in the art that the technical solution described in the above-mentioned embodiments may be modified or some technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the spirit and scope of the technical solution of the embodiments of the present invention.
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| CN121389548A (en) * | 2025-12-25 | 2026-01-23 | 江苏新蓝天钢结构有限公司 | Steel structure packing multi-parameter collaborative optimization system and method based on three-dimensional model |
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| CN121389548A (en) * | 2025-12-25 | 2026-01-23 | 江苏新蓝天钢结构有限公司 | Steel structure packing multi-parameter collaborative optimization system and method based on three-dimensional model |
| CN121389548B (en) * | 2025-12-25 | 2026-04-24 | 江苏新蓝天钢结构有限公司 | Steel structure packing multi-parameter collaborative optimization system and method based on three-dimensional model |
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