CN112017074B - Energy collaborative management system based on machine learning - Google Patents
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Abstract
The invention discloses an energy collaborative management system based on machine learning, which comprises: the system comprises a metering control module, an energy structure analysis auxiliary module, an electric topology and service topology compiling module, an energy flow digital twin module based on a machine learning algorithm and an energy cooperative control module based on machine learning; the energy structure analysis auxiliary module, the electrical topology and business topology establishment module, the energy flow digital twin module and the energy cooperative control module are sequentially connected; the metering control module is respectively connected with the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module; the metering control module is also used for being connected with a target customer energy system; the energy cooperative control module is also used for being connected with an electric power market. The energy collaborative management system can guide or control the transaction activities of the customers and the electric power market, help the customers to reduce the energy consumption cost, and can automatically optimize the upgrade to ensure the function effect.
Description
Technical Field
The invention relates to the technical field of energy management systems, in particular to an energy collaborative management system based on machine learning.
Background
At present, the traditional energy consumption analysis system or energy-saving system can only simply count the electricity consumption condition of the electricity consumption equipment, cannot guide or control the transaction activities of customers and the electric market, is unfavorable for the customers to reduce the energy consumption cost, and meanwhile, the model or strategy adopted by the system is generally fixed and cannot be updated along with the change of the external environment, so that the system gradually breaks away from the actual demands of target customers, and the functional effect is gradually reduced.
Therefore, the management system is designed, can guide or control the transaction activities of the customers and the electric power market, helps the customers to reduce the energy consumption cost, and can automatically optimize the upgrade and ensure the function effect, which is a technical problem to be solved urgently at present.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: in order to solve the problems, an energy collaborative management system based on machine learning is provided.
The technical scheme adopted by the invention is as follows:
an energy collaborative management system based on machine learning, comprising: the system comprises a metering control module, an energy structure analysis auxiliary module, an electric topology and service topology compiling module, an energy flow digital twin module based on a machine learning algorithm and an energy cooperative control module based on machine learning; the energy structure analysis auxiliary module, the electrical topology and business topology establishment module, the energy flow digital twin module and the energy cooperative control module are sequentially connected; the metering control module is respectively connected with the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module; the metering control module is also used for being connected with a target customer energy system; the energy cooperative control module is also used for being connected with an electric power market;
the metering control module is used for monitoring and controlling the target customer energy system, acquiring electricity utilization data of internal equipment in the target customer energy system, and uploading the acquired electricity utilization data to the energy structure analysis auxiliary module and the energy flow digital twin module;
the energy structure analysis auxiliary module is used for analyzing the energy consumption figures according to the received electricity consumption data and uploading the energy consumption figures to the electric topology and service topology establishment module;
the electric topology and service topology compiling module is used for carrying out energy consumption analysis on each subarea according to the energy consumption figures and uploading energy consumption analysis results to the energy flow digital twin module;
the energy flow digital twin module is used for correcting the inside of the system according to the electricity consumption data and the energy consumption analysis result;
the energy cooperative control module is used for acquiring and analyzing the data of the energy flow digital twin module and the metering control module and sending control instructions to each module; the control instructions include instructions to transact with the power market according to a specified strategy and coordinated control instructions to the various modules within.
Further, the metering control module comprises a middleware, an industrial communication protocol layer and an Internet of things communication layer which are sequentially connected;
the middleware is respectively connected with the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module and is used for sending data and instructions to the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module;
the Internet of things communication layer is used for enabling the metering control module to be connected with the target customer energy system;
the industrial communication protocol layer is used for enabling the metering control module to access the distributed energy source of the target customer energy system.
Further, the energy structure analysis auxiliary module comprises a portrait module and a data analysis module which are connected with each other;
the data analysis module is connected with the metering control module to acquire electricity consumption data of the internal equipment, and mathematical characteristics of the electricity consumption data are obtained through data analysis;
the portrait module is used for generating a portrait according to the mathematical characteristics of the electricity consumption data and uploading the portrait to the electric topology and service topology compiling module.
Further, the electric topology and service topology compiling module comprises an electric topology module, a service topology module and a combined topology and portrait module; the electric topology module is connected with the energy structure analysis auxiliary module, and the electric topology module and the service topology module are connected with the combined topology and portrait module; the combined topology and portrayal module is connected with the energy flow digital twin module;
the electrical topology module is used for updating portrait data corresponding to each node of the electrical topology according to the energy consumption portrait and uploading the portrait data to the joint topology and portrait module;
the service topology module is used for a target client to complete the division of service topology and upload the service topology to the combined topology and portrait module;
the combined topology and portrait module is used for periodically updating the energy consumption portraits of each service area, analyzing the energy consumption according to the energy consumption portraits and uploading the energy consumption analysis result to the energy flow digital twin module.
Further, the energy flow digital twin module comprises a service and energy combination topology module, a service area energy portrait model module and a service area energy consumption analysis module; the service and energy combination topology module and the service area energy portrait model module are both connected with the electrical topology and service topology editing module; the service and energy combination topology module, the service area energy consumption portrait model module and the metering control module are all connected with the service area energy consumption analysis module; the service and energy combination topology module, the service area energy portraits model module and the service area energy consumption analysis module are connected with the energy cooperative control module;
the service and energy combined topology module is used for periodically acquiring the electric topology, service topology and portrait data of a target client from the front-end module, processing the electric topology, service topology and portrait data into a digital twin model and an energy flow topology model, and uploading the electric topology, service topology and portrait data to the energy cooperative control module and the service area energy consumption analysis module respectively;
the service area energy consumption analysis module is used for analyzing the service area energy consumption of the service area according to the energy consumption of the service area;
the service area energy consumption analysis module is used for realizing a preset application function based on the digital twin model and the energy flow topology model and the user portrait.
Further, the energy cooperative control module comprises a transaction strategy monitoring module and a financial settlement module which are connected with each other; the transaction strategy monitoring module is connected with the energy flow digital twin module and the metering control module, and the financial settlement module is connected with the electric power market;
the transaction strategy monitoring module is connected with the metering control module to acquire monitoring data of the metering control module and control internal equipment of the target customer energy system; the transaction strategy monitoring module is connected with an electric power market to control transaction activities in the electric power market;
the financial settlement module is used for settling transaction activities in the electric power market.
Further, when in the working mode, the transaction policy monitoring module (151) is used for calculating the optimal energy consumption or energy selling policy for the target client and controlling other modules to execute the energy consumption or energy selling policy; when in the self-learning mode, the transaction strategy monitoring module (151) is used for periodically reading the new model and adjusting the internal transaction strategy according to the historical transaction and control conditions.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. the object analyzed by the energy collaborative management system is energy, but not energy consumption, so that the energy collaborative management system can be applied to not only managing energy consumption equipment, but also managing distributed energy, namely equipment for managing negative energy consumption such as a virtual power plant, and has wider application scene;
2. the energy collaborative management system introduces a model of combining and managing the electrical topology and the service topology, can provide clear energy consumption cost for target clients, and is beneficial to more accurately controlling the energy cost of production units;
3. the model of the energy collaborative management system is introduced with a mode of machine learning analysis and self-adaptive improvement thereof, which is beneficial to automatic upgrading of the energy collaborative management system and continuously improves the control intelligence of the energy collaborative management system;
4. the energy collaborative management system introduces a digital twin concept, is different from the traditional physical digital twin, focuses on modeling of energy and service, has stronger pertinence, realizes embedding instead of being led by twin, and can provide service for a target client according to actual requirements;
5. the energy collaborative management system also introduces an energy collaborative control module based on a machine learning model and a digital twin model, so that a control strategy can be continuously optimized to realize energy conservation, and the energy collaborative management system can also directly participate in trading activities of an electric power market.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a machine learning based energy collaborative management system according to an embodiment of the present invention;
FIG. 2 is a block diagram of the fine metering control system of FIG. 1 based on the low power wide area Internet of things;
FIG. 3 is a block diagram of the energy structure analysis assisting module of FIG. 1;
FIG. 4 is a block diagram of the electrical topology and service topology establishment module of FIG. 1;
FIG. 5 is a block diagram of the energy flow digital twinning module of FIG. 1 based on a machine learning algorithm;
fig. 6 is a block diagram of the energy cooperative control module based on machine learning of fig. 1.
Reference numerals: 100-energy collaborative management system, 110-metering control module, 111-middleware, 1111-power standard model module, 1112-time-of-day database, 1113-device control interface, 112-industrial communication protocol layer, 1121-IEC-60870-5-101 module, 1122-IEC-60870-5-104 module, 1123-MMS module, 1124-Modbus TCP, 113-Internet of things communication layer, 1131-message center, 1132-NB-IoT communication module, 1133-Lora communication module, 1134-Ethernet communication module, 1135-Bluetooth, 120-energy structure analysis assistance module, 121-data analysis module, 1211-standard statistical analysis module, 1212-spectrum analysis module, 1213-big data clustering module, 1214-outlier analysis module 1215-user manual setting module, 122-portrayal module, 130-electric topology and service topology construction module, 131-electric topology module, 132-service topology module, 133-combined topology and portrayal module, 1331-electric topology binding module, 1332-service area energy consumption portrayal analysis module, 140-energy flow digital twin module, 141-service and energy combined topology module, 1411-energy flow topology model module, 1412-service area model module, 1413-combined digital model module, 142-service area energy consumption portrayal model module, 143-service area energy consumption analysis module, 150-energy cooperative control module, 151-transaction strategy monitoring module, 152-financial settlement module, 1521-electric charge settlement module, 1522-an electric power market settlement module, 1523-a cost and output analysis module, 1524-a report module, 200-a target customer energy system, 210-a fine energy metering device, 220-a control switch, 230-a distributed energy source, 240-a partial energy utilization device and 300-an electric power market.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
The features and capabilities of the present invention are described in further detail below in connection with the examples.
As shown in fig. 1, an energy collaborative management system 100 based on machine learning includes: a fine metering control module 110 (hereinafter referred to as metering control module 110) based on low-power wide area internet of things, an energy structure analysis auxiliary module 120, an electrical topology and service topology compilation module 130, an energy flow digital twin module 140 (hereinafter referred to as energy flow digital twin module 140) based on a machine learning algorithm, and an energy cooperative control module 150 (hereinafter referred to as energy cooperative control module 150) based on machine learning; the energy structure analysis auxiliary module 120, the electrical topology and service topology establishment module 130, the energy flow digital twin module 140 and the energy cooperative control module 150 are sequentially connected; the metering control module 110 is respectively connected with the energy structure analysis auxiliary module 120, the energy flow digital twin module 140 and the energy cooperative control module 150; the metering control module 110 is also configured to interface with a target customer energy system 200; the energy co-control module 150 is also used in connection with the power market 300. Wherein the power market 300 may include a power grid; the various systems or modules in the energy co-management system 100 may be connected by wireless or wired connections; the energy co-management system 100 may be wirelessly connected with the power market 300 and the target customer energy system 200.
The workflow of the energy co-management system 100 is as follows:
taking office building as an example, the target customer energy system 200 is an acquisition device and an internal device; the collection device is used for collecting electricity consumption data of the internal devices, such as a smart meter in an office building; the internal devices include, but are not limited to, air conditioners, lighting devices, charging piles, miniature energy storage stations, and the like;
firstly, the metering control module 110 is configured to establish communication with the acquisition equipment in the target customer energy system 200, and is used for monitoring and controlling the target customer energy system 200, namely, acquiring electricity utilization data of the internal equipment through the acquisition equipment and controlling the internal equipment; the metering control module 110 is configured to automatically operate, continuously monitor the power consumption data and the working state of the target customer energy system 200, and upload the acquired power consumption data to the energy structure analysis auxiliary module 120 and the energy flow digital twin module 140;
the energy structure analysis assistance module 120 then analyzes the energy usage pattern from the received electricity usage data and uploads the energy usage pattern to the electrical topology and business topology creation module 130. In the energy consumption figure, if the electricity consumption of an air conditioner and a lighting device in the office building is higher, the continuous periodic energy duty ratio in the energy consumption figure is higher, and if the electricity consumption of a miniature energy storage station in the office building is higher, the virtual power plant duty ratio in the energy consumption figure is higher.
The electrical topology and service topology creating module 130 then performs energy consumption analysis on each sub-area according to the energy consumption figures, and uploads the energy consumption analysis results to the energy flow digital twin module 140. For example, the target client sets 1-3 layers of the office building as a machine room subarea, sets 4-6 layers of the office building as an office area subarea, and selects corresponding internal devices to bind with the machine room subarea and the office area subarea, and the electric topology and service topology establishment module 130 further analyzes the energy consumption conditions of each subarea and uploads the analysis result to the energy flow digital twin module 140.
Second, the energy flow digital twin module 140 corrects the system according to the electricity consumption data and the energy consumption analysis result. The configuration energy flow digital twin module 140 periodically acquires electricity data of internal equipment from the metering control module 110, acquires the energy consumption analysis results of all subareas from the electric topology and service topology establishment module 130, automatically corrects the inside of the system (mainly corrects models and data in digital twin) according to the electricity data and the energy consumption analysis results, realizes digital twin of the target customer energy system 200, and realizes application functions of energy consumption abnormality analysis, energy consumption prediction analysis and the like in a preset time period in the future of the target customer energy system 200.
Finally, the energy cooperative control module 150 is a decision center of the energy cooperative management system 100, and the energy cooperative control module 150 is configured to acquire and analyze the data of the energy flow digital twin module 140 and the metering control module 110, and send control instructions to other modules; the control instructions include instructions to transact with the power market 300 according to a specified strategy, and coordinated control instructions to the various modules within.
Each module in the energy collaborative management system 100 may be carried by software, hardware or electronic devices of a set of information systems, and the collaborative work may be implemented by configuring each module of the whole energy collaborative management system 100. The implementation scheme of each module is as follows:
(1) Metering control module 110
As shown in fig. 2, the metering control module 110 includes a middleware 111, an industrial communication protocol layer 112 and an internet of things communication layer 113, which are sequentially connected; the middleware 111 is respectively connected with the energy structure analysis auxiliary module 120, the energy flow digital twin module 140 and the energy cooperative control module 150, and is used for transmitting data and control instructions with the energy structure analysis auxiliary module 120, the energy flow digital twin module 140 and the energy cooperative control module 150; the internet of things communication layer 113 is configured to connect the metering control module 110 with the target customer energy system 200; the industrial communication protocol layer 112 is used to enable the metering control module 110 to access the distributed energy sources 230 of the target customer energy system 200. In addition, the target customer energy system 200 includes a fine energy metering device 210, a control switch 220, a distributed energy source 230, a partial energy device 240, and the like.
(1.1) middleware 111
The middleware 111 is designed for servicing upper-layer modules (the energy structure analysis assisting module 120, the energy stream digital twin module 140, and the energy cooperative control module 150). The middleware 111 includes a power standard model module 1111, a time-offer database 1112, a device control interface 1113, and the like.
The power standard model module 1111 is configured to abstract various devices of the target customer energy system 200 into a standard power model, for example, normalize switches, electric meters, energy storage systems, power sources, and the like into the power model, so that information irrelevant to an upper layer system such as a complicated brand and a communication link is transparent, and only data and interfaces in the standard power model are reserved, so that the upper layer module can send simple commands to control various devices. These simple commands include, but are not limited to: acquisition data (including data of a certain period of time of the electricity meter), acquisition model parameters (including the size and state of the energy storage system), and the like. The power standard model module 1111 is a decoupling concept in communication protocols and technologies, and the power standard model module 1111 may enable flexible application of different communication technologies through an adaptation layer (such as an abstract channel service interface ACSI in the IEC61850 standard) between an introduced data model and a communication service. The power criteria model module 1111 may include four semantic domains including revenue metering and demand response.
The timing database 1112 is used to store timing data (including data collected by the smart meter). The time-scale database 1112 may be customized based on an open source scheme, and in this embodiment, the time-scale database 1112 may be an OpenTSDB. Thus, the present embodiment provides a system for collaborative management of energy 100 that employs a database 1112 that supports fast reading and writing of very fine granularity, long-term, large-scale time series data, as compared to conventional systems for energy management.
The device control interface 1113 is configured to cooperate with a standard power model and provide a fast command issuing channel for upper layer modules (the energy structure analysis assistance module 120, the energy stream digital twin module 140, and the energy cooperative control module 150). Such as an instruction to turn on the control switch 220, an instruction to control an operating state of the energy storage system, an instruction to control start and stop of the air conditioner, an instruction to control start and stop of the heating system, etc. Thus, the device control interface 1113 is further transparent and covers complex communication information as the standard power model, so that various devices of the target client can be controlled by simple instructions without considering unique detailed characteristics of the various devices, and the communication method of the system is simplified.
(1.2) Industrial communication protocol layer 112
The industrial communication protocol layer 112 includes an IEC-60870-5-101 module 1121, an IEC-60870-5-104 module 1122, an MMS module 1123, modbus TCP1124, and the like. Thus, through these industrial communication protocol layers 112, the metering control system is able to access all devices of the target customer energy system 200, such as the distributed energy source 230, the distributed energy source 230 comprising all devices of the new energy age that have standard communication protocols or that can be controlled by sensors (including smart meters), such as: charging pile, photovoltaic, fan, new energy automobile, power generation equipment, memory system etc.
(1.3) Internet of things communication layer 113
The internet of things communication layer 113 may include a message center 1131, NB-IoT communication module 1132, lora communication module 1133, ethernet communication module 1134, bluetooth 1135, and the like, and may also include other passive contactless power sensors, such as DBKCT-36.
The work flow of the metering control system:
the communication addresses (including the IP list of smart meters) of the various sensors of the target customer energy system 200 and their jurisdictions, topological relationships, etc. are configured in the metering control system. In this way, the metering control system can accurately determine the communication addresses of various devices of the target client, establish basic communication with various devices through the internet of things communication layer 113, and communicate with various devices by using the same language based on the industrial communication protocol layer 112, so that the metering control system can realize data acquisition and work control of various devices.
Compared with the traditional energy efficiency system or energy saving system, the metering control system of the management system provided by the embodiment can introduce various distributed energy sources 230, namely power generation equipment and discharge equipment such as new energy automobiles, fan systems, photovoltaics and the like, and can perform basic modeling on various objects by adopting the electric power standard model module 1111, so that the compatibility is stronger, and in addition, a time-sharing database 1112 is adopted, so that the operation efficiency is higher.
(2) Energy structure analysis assisting module 120
As shown in fig. 3, the energy structure analysis auxiliary module 120 includes a portrait module 122 and a data analysis module 121 which are connected with each other;
(2.1) data analysis Module 121
The data analysis module 121 is connected with the metering control module 110 to obtain electricity consumption data of the internal equipment, and obtains mathematical characteristics of the electricity consumption data through data analysis; specifically, the data analysis module 121 corresponds to a mathematical tool package, and the data analysis module 121 includes algorithm modules such as a standard statistical analysis module 1211, a spectrum analysis module 1212, a big data clustering module 1213, an outlier analysis module 1214, and a manual setting module 1215, which may be implemented by carrier firmware or software, such as Python. The data analysis module 121 may perform an indiscriminate data analysis on the electricity data of the internal device and obtain mathematical characteristics of the electricity data.
(2.2) portrait module 122
The representation module 122 is configured to generate the energy consumption representation based on the electricity consumption data and upload the energy consumption representation to an electrical topology and service topology creation module 130. Specifically, the representation module 122 periodically classifies the power usage data of the internal devices based on the mathematical characteristics of the power usage data, and obtains the duty ratio of the target customer energy system 200 on each image feature after superposition, for example, obtains a result that the duty ratio of the continuous periodic energy is higher in the energy usage representation similar to a office building. The analysis results are readable by the upper module, so that services are provided for the upper module.
Portrayal module 122 may also be implemented by carrying firmware or software, such as Python, etc. Objects encompassed in the energy usage representation include portal energy (involving cliff lifting type devices, such as on-off high power devices), continuous periodic energy (involving refrigerators, air conditioners, etc.), gaussian energy (small energy like noise, such as electric leakage, fast-to-break fluorescent lamps), motor type energy (involving generators, elevators, water pumps, etc.), virtual power plants (involving integration of partially abstract energy sources, such as centrally controlled charging piles, heating devices, controllable air conditioning systems, photovoltaics, wind power systems, energy storage systems, etc.), and the like.
Compared with the conventional energy efficiency system or energy saving system, the energy structure analysis auxiliary module 120 of the energy collaborative management system 100 provided in this embodiment not only analyzes more comprehensively, but also defines a new energy usage figure, and defines various energy characteristics in the energy figure, which is effective for load and power generation, and can exist simultaneously, such as being applied to a new energy automobile, and can be discharged or charged.
(3) Electrical topology and service topology establishment module 130
As shown in fig. 4, the electrical topology and service topology creating module 130 includes an electrical topology module 131, a service topology module 132, and a joint topology and portrait module 133; the electric topology module 131 is connected with the energy structure analysis auxiliary module 120, and the electric topology module 131 and the service topology module 132 are connected with the joint topology and portrayal module 133; the joint topology and portrayal module 133 is connected to an energy stream digital twinning module 140. The electrical topology and service topology establishment module 130 can also be implemented by carrying firmware or software, such as Python, etc.
(3.1) Electrical topology Module 131
The electrical topology module 131 can periodically obtain a representation of the energy from the energy structure analysis assistance module 120 based on the electrical topology information of the target customer energy system 200. The electrical topology module 131 is configured to update the portrait data corresponding to each node of the electrical topology according to the energy consumption portrait, and upload the portrait data to the joint topology and portrait module 133.
(3.2) traffic topology Module 132
The service topology module 132 is used by the target client, and has an operation interface for configuring or importing data for the target client to complete the division of the service topology, and upload to the joint topology and portrayal module 133. The service topology is a free abstraction, for example, 1 layer of office building is divided into reception areas, 2-4 layers are machine rooms, 5-7 layers are office areas, etc.
(3.3) Combined topology and portrayal Module 133
The joint topology and representation module 133 is configured to periodically update the usage representation of each service area, perform a usage analysis based on the usage representation, and upload the result of the usage analysis to the energy stream digital twin module 140.
In particular, the combined topology and portrayal module 133 includes an electrical topology binding module 1331 and a business district energy portrayal analysis module 1332. The joint topology and portrayal module 133 provides an operational interface for the target customer energy system 200 to correlate the electrical topology with the business topology and periodically updates the analysis of the energy usage portrayal of each business segment based on the correlation and provides the electrical topology, business topology and portrayal data to the energy flow digital twin module 140. The service area is an abstract topological structure, for example, a physical area can be divided into an A workshop and a B workshop, or divided into an A office and a B office, and energy flows are bound with each service area. The combined topology and representation module 133 combines the energy usage representation, i.e., the energy usage representation of each service area, to provide support for subsequent control strategies, such as finding that the Gaussian energy of the A plant is excessive, indicating a situation that may be subject to long-term leakage, and if the gate energy of the B plant is excessive, indicating that an energy storage system may need to be added.
Compared with the conventional energy efficiency system or energy saving system, the electrical topology and service topology creating module 130 of the energy collaborative management system 100 provided in this embodiment can manage service flows and energy flows simultaneously, and can be compatible with most office buildings and factory buildings, so that flexible configuration can be realized.
(4) Energy stream digital twinning module 140
As shown in fig. 5, the energy flow digital twin module 140 includes a service and energy combination topology module 141, a service area energy consumption model module 142 and a service area energy consumption analysis module 143; the service and energy combination topology module 141 and the service area energy portrayal model module 142 are connected with the electrical topology module 131 and the energy structure analysis auxiliary module 120; the service and energy combination topology module 141, the service area energy consumption portrait model module 142 and the metering control module 110 are all connected with the service area energy consumption analysis module 143; the service and energy combination topology module 141, the service area energy representation model module 142 and the service area energy consumption analysis module 143 are all connected with the energy cooperative control module 150.
(4.1) traffic and energy combining topology module 141
The service and energy combined topology module 141 is configured to periodically obtain the electrical topology, service topology and portrait data of the target customer energy system from the front-end module, process the electrical topology, service topology and portrait data into a digital twin model and an energy flow topology model, and upload the electrical topology, service topology and portrait data to the energy cooperative control module 150 and the service area energy consumption analysis module 143 respectively.
Specifically, the business and energy combining topology module 141 includes an energy flow topology model module 1411, a business region model module 1412, and a combining digital model module 1413. The configuration service and energy combination topology module 141 periodically acquires the electrical topology, service topology and portrait data of the target client from the front-end module, further abstracts and processes the electrical topology, service topology and portrait data into a digital twin model and an energy flow topology model, and uploads the digital twin model and the energy flow topology model to the energy cooperative control module 150 and the service area energy consumption analysis module 143 respectively. The energy flow topology model is a model combining an electrical model and a business model, such as a tree model: total flow of the inflow port, 100% by weight, 80% by weight, and periodic main image by branch 1, 20% by weight, and gate main image by branch 2. After the data are dynamically updated, if the model of the virtual power plant exists, the dressing model is changed into a partial annular model. The service area model module 1412 and the combined digital model module 1413 are auxiliary modules, and only serve as data carrying and bonding functions for more tightly combining with the electrical topology, the service topology and the portraits.
(4.2) energy representation model Module 142 for service area
The service area energy consumption analysis module 143 is used for uploading the service area energy consumption model module 142 to the collaborative control module. The business district energy portrayal model module 142 differs from the business and energy combination topology module 141 in that only the model is updated without twinning its actual operational data. That is, the business district energy representation model module 142 does not analyze the actual energy flow, only analyzes past data characteristics, and the business and energy combination topology module 141 is responsible for analyzing the actual energy flow.
(4.3) service area energy consumption analysis Module 143
The service area energy consumption analysis module 143 is used for realizing a preset application function based on the digital twin model and the energy flow topology model and the user portrait. The application functions include machine learning data analysis, energy consumption prediction, abnormal data analysis, distributed energy 230 productivity prediction, model self-learning improvement, and the like. The algorithms for machine learning data analysis include regression, clustering, decision trees, and the like. The configuration service area energy consumption analysis module 143 uploads the energy law to the energy cooperative control module 150, more importantly, periodically reads the data of the metering control system, updates and retrains the internal model, and provides support for the control strategy of the energy cooperative control module 150. Of course, the service area energy consumption analysis module 143 may be configured as a functional module, and the workflow thereof is to receive a command (such as the electricity consumption 1 hour after prediction) of the upper module, and then return the analysis result to the upper module. The specific training and self-learning improvement of the service area energy consumption analysis module 143 can be controlled by the Python module, and the training and adjustment can be realized after the training and adjustment are performed by external data.
Compared with the conventional energy efficiency system or energy saving system, the energy flow digital twin module 140 of the energy collaborative management system 100 provided in this embodiment introduces the concept of digital twin, and compared with the conventional digital twin, the energy flow digital twin module 140 in this embodiment is an optimization and customization, which includes the service and energy combination topology module 141, the service area energy consumption analysis module 143 and the service area energy consumption analysis module 142, so that the system is not limited by the conventional concept. Meanwhile, a machine learning method is introduced, so that modeling functions are more abundant, characteristic description is more accurate, and the self-learning self-adaptive lifting and improving functions are realized.
(5) Energy cooperative control module 150
As shown in fig. 6, the energy cooperative control module 150 includes a transaction policy monitoring module 151 and a financial settlement module 152 connected to each other; the transaction strategy monitoring module 151 is respectively connected with the energy flow digital twin module 140 and the metering control module 110, and the transaction strategy monitoring module 151 and the financial settlement module 152 are both connected with the electric power market 300;
(5.1) transaction policy monitoring Module 151
The transaction policy monitoring module 151 is connected with the metering control module 110 to obtain monitoring data of the metering control module 110 and control internal devices of the target customer energy system 200; the trading strategy monitoring module 151 controls trading activity in the power market 300 by interfacing with the power market 300. Specifically, the transaction policy monitoring module 151 is configured with a power-saving policy, an aggressive policy, an equalization policy, a manual policy, and the like, and meanwhile, the transaction policy monitoring module 151 further includes a market price machine learning analysis and prediction module, a market price machine learning model self-learning continuous improvement module, an energy-saving analysis module, and the like.
The configuration transaction policy monitoring module 151 has two modes of operation: an operating mode and a self-learning mode. When in the working mode, the transaction policy monitoring module 151 is configured to periodically predict the energy consumption of future systems, predict the electricity price of the electricity market 300, analyze the current charge and discharge capabilities of the energy storage system, the refrigeration and heating equipment and other equipment which can be abstracted into a virtual power plant, thereby calculating the optimal energy consumption or energy selling policy for the target customer, and control other modules to execute the energy consumption or energy selling policy. While in the self-learning mode, the transaction policy monitor module 151 is configured to periodically read new models, such as a predictive model for internal energy flow, a predictive model for electricity market price, and a transaction policy, and adjust internal transaction policies based on historical transaction and control conditions.
(2) Financial settlement module 152
The financial settlement module 152 is used to settle transaction activities in the power market 300. Specifically, the finance settlement module 152 is configured as a regular finance module which is driven by events, embeds a standardized process and provides report services. The agreement to configure the financial settlement module 152 to transact with the power market 300 will be maintained by the provider based on the local, current version of the time period. Specifically, the financial settlement module 152 may include an electricity fee settlement module 1521, an electricity market settlement module 1522, a cost and output analysis module 1523, a report module 1524, and the like, so as to implement various settlement functions, analysis functions, and report functions.
The energy cooperative control module 150 may be implemented by a carrier firmware or software, such as Python, etc. Compared with the conventional energy efficiency system, energy saving system or transaction control system, the energy cooperative control module 150 of the energy cooperative management system 100 provided in this embodiment introduces an energy flow digital twin module 140 before executing the control strategy, so that the characteristics of the system can be more accurately described, the future energy flow of the system can be more accurately predicted, and a more reliable basis is provided for the subsequent transaction instead of purely relying on experience and manual operation. The energy cooperative control module 150 also considers the power supply security, energy conservation and transaction benefits at the same time, and is a comprehensive optimization for target customer energy management.
The energy collaborative management system 100 based on machine learning provided in this embodiment has the following beneficial effects:
1. the object analyzed by the energy collaborative management system 100 is energy rather than energy consumption, so that the energy collaborative management system 100 can be applied to not only managing energy consumption equipment, but also managing distributed energy sources 230, namely equipment for managing negative energy consumption such as a virtual power plant, and has wider application scenes;
2. the energy collaborative management system 100 introduces a model of combining and managing the electrical topology and the service topology, can provide clear energy consumption cost for target customers, and is beneficial to more accurately controlling the energy cost for production units;
3. the model of the energy collaborative management system 100 introduces a mode of machine learning analysis and self-adaptive improvement thereof, which is beneficial to automatic upgrading of the energy collaborative management system 100 and continuously improves the control intelligence of the energy collaborative management system 100;
4. the energy collaborative management system 100 introduces a digital twin concept, is different from the traditional physical digital twin, focuses on modeling of energy and service, has stronger pertinence, realizes embedding instead of being led by twin, and can provide service for a target client according to actual requirements;
5. the energy collaborative management system 100 also introduces an energy collaborative control module 150 based on a machine learning model and a digital twin model, which not only can continuously optimize a control strategy to achieve energy conservation, but also can directly participate in the trading activity of the power market 300.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (5)
1. An energy collaborative management system based on machine learning, comprising: the system comprises a metering control module, an energy structure analysis auxiliary module, an electric topology and service topology compiling module, an energy flow digital twin module based on a machine learning algorithm and an energy cooperative control module based on machine learning; the energy structure analysis auxiliary module, the electrical topology and business topology establishment module, the energy flow digital twin module and the energy cooperative control module are sequentially connected; the metering control module is respectively connected with the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module; the metering control module is also used for being connected with a target customer energy system; the energy cooperative control module is also used for being connected with an electric power market;
the metering control module is used for monitoring and controlling the target customer energy system, acquiring electricity utilization data of internal equipment in the target customer energy system, and uploading the acquired electricity utilization data to the energy structure analysis auxiliary module and the energy flow digital twin module;
the energy structure analysis auxiliary module is used for analyzing the energy consumption figures according to the received electricity consumption data and uploading the energy consumption figures to the electric topology and service topology establishment module;
the electric topology and service topology compiling module is used for carrying out energy consumption analysis on each subarea according to the energy consumption figures and uploading energy consumption analysis results to the energy flow digital twin module;
the energy flow digital twin module is used for correcting the inside of the system according to the electricity consumption data and the energy consumption analysis result;
the energy cooperative control module is used for acquiring and analyzing the data of the energy flow digital twin module and the metering control module and sending control instructions to each module; the control instructions comprise instructions for trading with the power market according to a specified strategy and cooperative control instructions for each internal module;
the energy flow digital twin module comprises a service and energy combination topology module, a service area energy portrait model module and a service area energy consumption analysis module; the service and energy combination topology module and the service area energy portrait model module are both connected with the electrical topology and service topology editing module; the service and energy combination topology module, the service area energy consumption portrait model module and the metering control module are all connected with the service area energy consumption analysis module; the service and energy combination topology module, the service area energy portraits model module and the service area energy consumption analysis module are connected with the energy cooperative control module;
the service and energy combined topology module is used for periodically acquiring the electric topology, service topology and portrait data of a target client from the front-end module, processing the electric topology, service topology and portrait data into a digital twin model and an energy flow topology model, and uploading the electric topology, service topology and portrait data to the energy cooperative control module and the service area energy consumption analysis module respectively;
the service area energy consumption analysis module is used for analyzing the service area energy consumption of the service area according to the energy consumption of the service area;
the service area energy consumption analysis module is used for realizing a preset application function based on a digital twin model and an energy flow topology model and a user portrait;
the energy cooperative control module comprises a transaction strategy monitoring module and a financial settlement module which are connected with each other; the transaction strategy monitoring module is connected with the energy flow digital twin module and the metering control module, and the financial settlement module is connected with the electric power market;
the transaction strategy monitoring module is connected with the metering control module to acquire monitoring data of the metering control module and control internal equipment of the target customer energy system; the transaction strategy monitoring module is connected with an electric power market to control transaction activities in the electric power market;
the financial settlement module is used for settling transaction activities in the electric power market.
2. The machine learning based energy collaborative management system according to claim 1, wherein the metering control module includes a middleware, an industrial communication protocol layer, and an internet of things communication layer connected in sequence;
the middleware is respectively connected with the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module and is used for sending data and instructions to the energy structure analysis auxiliary module, the energy flow digital twin module and the energy cooperative control module;
the Internet of things communication layer is used for enabling the metering control module to be connected with the target customer energy system;
the industrial communication protocol layer is used for enabling the metering control module to access the distributed energy source of the target customer energy system.
3. The machine learning based energy collaborative management system of claim 1, wherein the energy structure analysis assistance module includes a representation module and a data analysis module coupled to each other;
the data analysis module is connected with the metering control module to acquire electricity consumption data of the internal equipment, and mathematical characteristics of the electricity consumption data are obtained through data analysis;
the portrait module is used for generating a portrait according to the mathematical characteristics of the electricity consumption data and uploading the portrait to the electric topology and service topology compiling module.
4. The machine learning based energy collaborative management system of claim 1, wherein the electrical topology and business topology orchestration module includes an electrical topology module, a business topology module, and a joint topology and portrayal module; the electric topology module is connected with the energy structure analysis auxiliary module, and the electric topology module and the service topology module are connected with the combined topology and portrait module; the combined topology and portrayal module is connected with the energy flow digital twin module;
the electrical topology module is used for updating portrait data corresponding to each node of the electrical topology according to the energy consumption portrait and uploading the portrait data to the joint topology and portrait module;
the service topology module is used for a target client to complete the division of service topology and upload the service topology to the combined topology and portrait module;
the combined topology and portrait module is used for periodically updating the energy consumption portraits of each service area, analyzing the energy consumption according to the energy consumption portraits and uploading the energy consumption analysis result to the energy flow digital twin module.
5. The machine learning based energy collaborative management system of claim 1, wherein the transaction policy monitoring module is configured to calculate an energy usage or sales policy optimal for a target customer when in an operational mode and to control other modules to execute the energy usage or sales policy; when in the self-learning mode, the transaction strategy monitoring module is used for periodically reading the new model and adjusting the internal transaction strategy according to the historical transaction and control conditions.
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