CN116739236A - Smart power grid system based on cloud edge fusion architecture and scheduling method - Google Patents
Smart power grid system based on cloud edge fusion architecture and scheduling method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract
The application discloses a smart grid system and a scheduling method based on a cloud edge fusion architecture, wherein the smart grid system and the scheduling method based on the cloud edge fusion architecture comprise a cloud system module, an edge end system module, an edge gateway module and corresponding function designs, and a smart grid scheduling method based on the cloud edge fusion architecture, wherein a cloud system for the whole grid service of a power grid is built by a cloud based on computing resources provided by a cloud platform technology; the edge end comprises two types of edge end systems, namely an edge cluster and an edge gateway, wherein the edge cluster constructs a power grid dispatching operation monitoring system with elastic expansion capacity based on a dynamic container strategy, and supports the elastic access and real-time monitoring of controlled objects such as traditional plant stations, centralized new energy sources, massive emerging grid-connected main bodies and the like; the edge gateway performs data interaction with controlled objects such as traditional plant stations, centralized new energy sources, various emerging grid-connected main bodies and the like in a standardized mode. The intelligent power grid system and the scheduling method based on the cloud edge fusion architecture can be widely applied to the technical field of intelligent power grids.
Description
Technical Field
The application relates to the technical field of smart power grids, in particular to a smart power grid system based on a cloud edge fusion architecture and a scheduling method.
Background
With the construction of a novel power system, development of distributed new energy, micro-grid, power marketing reform and the like is rapid, massive emerging grid-connected main bodies such as large-scale distributed energy, virtual power plants, comprehensive parks and the like participate in operation regulation and control of a power grid, the structure form and the system characteristics of the power grid are changed from planned, centralized to open sharing and intelligent interaction directions, brand new challenges are provided for power grid operation control and management and power market operation, and higher requirements are provided for platform openness, system capacity, access capacity, intelligent degree and the like of a power grid dispatching operation monitoring system; the current control system adopted by the power grid has obvious service demarcation and weak cooperative capability, lacks global-oriented top layer unified monitoring, prediction, optimization, analysis and other support services, and cannot effectively solve the support requirements of source network load storage interactive operation under the development background of the novel power system; in addition, the controlled objects facing the traditional power grid dispatching operation monitoring system are limited and tightly coupled main bodies (power plants, substations and the like), and under the condition of accessing the mass emerging grid-connected main bodies, the traditional power grid dispatching operation technical system is difficult to effectively support the requirements of mass accessing, service elastic expansion, intelligent response decision-making and the like of the emerging grid-connected main bodies; however, the current common way to solve the above problems is to improve the access capability and service capability of the system by expanding the operation resources based on the conventional power grid dispatching operation monitoring system, but it is more and more difficult to support the access of mass objects and complex and changeable service requirements in this way.
Disclosure of Invention
In order to solve the technical problems, the application aims to provide a smart grid system and a scheduling method based on a cloud edge fusion architecture, and provides a smart grid scheduling operation platform with 'cloud end and edge end' two-stage fusion, which provides scheduling operation support for various services such as novel power system source network load storage comprehensive monitoring, analysis and prediction, cooperative control, market transaction and the like.
The first technical scheme adopted by the application is as follows: a smart grid system based on a cloud-edge fusion architecture, comprising:
the cloud system module is used for connecting network, province and ground edge clusters, collecting whole network operation data, realizing global optimization analysis, whole network scheduling decision and panoramic information display, supporting whole network sharing and service sharing;
the edge end system module is used for elastically accessing, monitoring and cooperatively controlling controlled objects such as traditional stations, centralized new energy stations, mass emerging market main bodies and the like in a dispatching range;
and the edge gateway module is used for collecting local power grid operation data and marketing data, uploading the data to the edge cluster, supporting instruction operations such as power grid operation mode switching, frequency modulation peak shaving and the like, and supporting power grid dispatching operation and power market operation.
The second technical scheme adopted by the application is as follows: a smart grid scheduling method based on cloud edge fusion architecture comprises the following steps:
constructing a smart grid system based on a cloud edge fusion architecture as set forth in claim 1;
collecting demand data of various control units stored in source network charges managed by the intelligent power grid system;
the various control units send calculation task requests to the edge end system module and the cloud system module through the edge end system module according to the local calculation capability;
the edge end system module further decomposes the edge cluster into a rolling plan of various governed source network load storage control units based on the current level power grid model and operation data according to the total adjustment requirement of the edge cluster or the market bid-winning result issued by the cloud system in a plan rolling control mode, and issues the rolling plan to a regulation object for execution;
the cloud system module receives real-time active regulation demands issued by cloud unified frequency modulation in a real-time control mode, decomposes the real-time active regulation demands into real-time control instructions of various control units stored in a source network under jurisdiction, and issues the real-time control instructions to a regulation object for execution.
Further, the edge system module is used for elastically accessing controlled objects such as a traditional plant station, a centralized new energy station, a mass emerging market main body and the like in a regulation range, and specifically comprises the following steps:
the edge cluster provides the capacity of allocating running resources according to the need based on the container technology, realizes the elastic expansion of the edge cluster business application, and realizes the 'plug and play' of an application module based on a standard interface provided by a distributed service bus and a message queue;
(1) An edge cluster container operating environment;
the safety zone I and the safety access zone I adopt customized container operation environments meeting the safety protection requirements of the control zone of the power monitoring system; the safe area II and the safe area III construct a container running environment based on a general container service technology, and are compatible with cloud system application downloading deployment;
(2) Data storage of the edge cluster application module;
the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; storing the data into an edge cluster database and a storage service mounted on the container;
(3) Data interaction between the edge cluster application modules;
the message transmission support among the application modules supported by different container groups is carried out through a message bus, the application modules supported by the same container group have strong correlation in function, and the data exchange in a mode of sharing memory, a database and storage is supported;
(4) Service call of the edge cluster application module;
and configuring a distributed service agent in the application module container group, wherein the application modules provide functional services to the outside through the distributed service agent, and each application module calls related services through the distributed service agent as required.
Further, the edge system module further includes:
based on technologies such as containers, micro-services and the like, front-end acquisition with elastic expansion is constructed, and dynamic elastic access of controlled objects such as traditional plant stations, centralized new energy field stations, mass emerging grid-connected main bodies and the like is supported;
front data parallel processing mechanisms based on container, message bus and other technical construction support large-scale access object monitoring and control;
based on cloud-edge fusion cooperative control system, the real-time cooperative control of controlled objects such as traditional plant stations, centralized new energy field stations, massive emerging grid-connected main bodies and the like is supported.
Further, the dynamic elastic access of the edge system module to the controlled object also comprises a data preprocessing method designed with a type of dynamic container, which specifically comprises the following steps:
collecting edge cluster preamble data, wherein the edge cluster preamble data comprises remote control data S 1 Manual operation data S 2 Real-time statistics S 3 Related history data S 4 ；
At the beginning of each time slot, the remote control data S 1 Manual operation data S 2 Real-time statisticsData S 3 Related history data S 4 Preprocessing data, extracting abnormal value, filling up related missing value to obtain data value of correspondent data
The speed of generation of the four types of data is expressed as V 1 ,V 2 ,V 3 ,V 4 ]When the speed of data generation is lower than the processing speed, i.e.At this time, the container capacity R i Without processing, increasing the capacity of the container when the rate of data generation is higher than the processing rate;
computing resources [ C ] allocated for four classes of data to be jointly optimized edge cluster parallelism 1 ,C 2 ,C 3 ,C 4 ]And corresponding container capacity for four classes of data [ R ] 1 ,R 2 ,R 3 ,R 4 ]The task preprocessing delay is minimized, and the optimization problem is expressed as:
in the above formula, δ represents the duration of a task processing time slot, the constraint (1) represents that the capacity of a container allocated to each type of data is greater than the rate generated by the task minus the data processing rate, so as to ensure that the related data cannot be discarded, and the constraint (2) represents the limitation of the capacity and the limitation of the computing resources;
and optimizing and solving the optimization problem through a genetic algorithm, and automatically calculating according to real-time operation data to obtain a calculated result after the calculation of the dynamic container is completed, and sending the calculated result to a message queue for other services.
Further, the method also comprises two modes of edge cluster support to develop operation prediction:
the first mode is that a cloud-edge fusion cloud system intelligent prediction module is remotely called to conduct load prediction and new energy prediction through a cloud-edge two-stage fusion mode, and a prediction result is directly obtained from a cloud system;
the second mode is to develop model training of bus load prediction, new energy power prediction, system load prediction and distributed energy prediction in the jurisdiction of the edge cluster in the cloud system based on cloud artificial intelligence technology, and to load the trained model to the edge cluster in a cloud-edge two-stage fusion mode, and after the edge cluster is deployed, develop prediction based on operation data of the edge cluster so as to obtain a prediction result.
Further, the cloud system adopts a 'cloud primary' scheme, and supports application 'micro-servitization' and system elastic capacity expansion upgrading based on a container running environment; based on an open and standard platform interface provided by a service bus and a message queue, a plug-and-play application module is constructed, and the cloud application is jointly constructed by supporting the whole network, specifically:
(1) Deploying an application module of the cloud system;
the cloud platform-based container running environment is deployed, and the cloud platform is used for realizing the services of management, internal communication, scheduling, start-stop, transverse expansion and the like of the container cluster;
(2) Communication among cloud system application modules;
based on virtual IP provided by the cloud platform VPC network, the application module service support composed of a plurality of container groups is mounted under one responsible balanced IP, so that load balancing of the application module on external service flow is realized;
(3) Data storage of the cloud system application module;
the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; the data is stored in a cloud platform database and a storage service of the container mount;
(4) Data interaction between cloud system application modules;
the transfer of messages between application modules supported by different container groups is supported by a message bus. The application modules supported by the same container group have strong correlation in function and support data exchange in a mode of sharing memory, a database and storage;
(5) Service calling of the cloud system application module;
and supporting to package all or a certain piece of sub-function logic of the application module and mount the sub-function logic on the cloud service bus to provide functional service for the outside, and calling the service mounted on the cloud service bus by each application module through the cloud service bus according to the requirement.
The method and the system have the beneficial effects that: the application provides a cloud and edge two-stage fused intelligent power grid dispatching operation platform, which provides dispatching operation support for various services such as novel power system source network load storage comprehensive monitoring, analysis prediction, cooperative control, market transaction and the like, builds a cloud system by adopting advanced computer technologies such as cloud platform large-scale calculation and storage and the like, serves as unified 'brain' of whole power grid sharing, gathers whole power grid operation data, carries out optimized calculation analysis of whole power grid network performance, supports whole power grid system operation and power market operation, builds an edge cluster with elastic expansion by adopting advanced computer technologies such as containers, micro-service, message queues and the like, serves as a real-time operation control system of all-stage power grid dispatching mechanisms of a network, a province and a ground, supports elastic access and real-time monitoring of all-stage power grid dispatching mechanisms on controlled objects such as traditional plants, centralized new energy sources, mass emerging grid-connected main bodies and the like, builds a cloud edge fused data interaction system by adopting computer technologies such as service agents and the like, and builds a cloud edge two-stage flattened, standardized and high-safety cloud edge interaction channel, and supports efficient operation interaction of cloud edge two-stage service fusion interaction.
Drawings
Fig. 1 is a block diagram of a smart grid system based on a cloud-edge fusion architecture of the present application;
fig. 2 is a flowchart of steps of a smart grid scheduling method based on a cloud-edge fusion architecture.
Detailed Description
The application will now be described in further detail with reference to the drawings and to specific examples. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.
Referring to fig. 1 and fig. 2, the application provides a smart grid system and a scheduling method based on cloud-edge fusion architecture, which adopts a system architecture of 'cloud+edge' to perform platform construction, and comprises a cloud system and an edge system, wherein the edge system consists of a plurality of edge computing nodes, comprises two types of edge systems of an edge cluster and an edge gateway, and comprises the following steps:
the cloud system is built uniformly on the basis of a cloud platform technology whole network, adopts a layering design, and consists of three layers from bottom to top, wherein the cloud system comprises a public resource layer, a service platform layer and a service application layer;
specifically, the cloud system is built based on a cloud platform IaaS, paaS, daaS cloud service component, serves as a central intelligent brain of a power grid, connects network, province and ground edge clusters, gathers whole-network operation data, achieves global optimization analysis, whole-network scheduling decision and panoramic information display, supports whole-network sharing, and serves sharing. The cloud system consists of a public resource layer, a service platform layer and a service application layer. The public resource layer provides basic operation resources for the cloud platform, the service platform layer provides standardized and unified public service for upper-layer service applications, and the service application layer comprises advanced applications such as power grid operation monitoring, calculation analysis, operation prediction, operation optimization, evaluation analysis, intelligent interaction and the like. And the cloud system performs data interaction and application collaboration with the edge cluster.
(1) In the technical aspect, the cloud system adopts a 'cloud primary' scheme, and supports application 'micro-servitization' and system elastic capacity expansion upgrading based on a container running environment; based on an open and standard platform interface provided by a service bus and a message queue, a plug-and-play application module is constructed, and the cloud application is jointly constructed by supporting the whole network.
1) Deployment of an application module of the cloud system: the cloud platform-based container running environment is deployed, and the cloud platform is used for realizing the services of management, internal communication, scheduling, start-stop, transverse expansion and the like of the container cluster;
2) Communication among cloud system application modules: based on virtual IP provided by the cloud platform VPC network, the application module service support composed of a plurality of container groups is mounted under one responsible balanced IP, so that load balancing of the application module on external service flow is realized;
3) And (3) data storage of the cloud system application module: the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; and storing the data into a cloud platform database and a storage service of the container.
4) Data interaction between cloud system application modules: the transfer of messages between application modules supported by different container groups is supported by a message bus. The application modules supported by the same container group have strong correlation in function and support data exchange in a mode of sharing memory, database and storage.
5) Service call of cloud system application module: and supporting to package all or a certain piece of sub-function logic of the application module and mount the sub-function logic on the cloud service bus to provide functional service for the outside. And each application module calls the service mounted on the cloud service bus through the cloud service bus according to the requirement.
(2) In business aspect, the cloud system realizes comprehensive sensing, intelligent prediction and global optimization of the charge storage of a large power grid source network based on a full-network 10kV and above (in an enclosure) panoramic large model containing an emerging grid-connected main body, realizes operation and maintenance intensification of a secondary system based on a cloud intelligent operation and maintenance system, and promotes 'considerable, measurable and controllable' level of a novel power system through the cloud system.
1) At present, a power grid model is built according to the regulation range of each level of power grids in the province and the ground and is used as the basis of business applications such as operation and analysis of the power grid of the current level, a cloud system breaks through a hierarchical power grid model, the whole-network source network charge storage model is spliced uniformly, and a power grid panoramic model covering 10kV and above voltage levels is built and used for supporting businesses such as comprehensive sensing, intelligent prediction, global optimization and the like of the whole-network source network charge storage of the power grid. The model splicing comprises two parts, including the traditional power grid model splicing and the splicing of the emerging grid-connected main body model. For traditional power grid model splicing, in order to improve model splicing efficiency, a model unified coding mode is adopted for standardization of the models, and a cloud edge two-stage model splicing method is adopted for realizing traditional power grid model splicing, namely, all levels of edge clusters of a network, a province and a ground are used for completing generation of a current level of traditional power grid model according to model unified coding and are sent to a cloud system, and the cloud system is used for completing splicing of a full-network traditional power grid model based on all levels of traditional power grid models and model boundaries. For the emerging grid-connected main body model, generating an emerging grid-connected main body aggregation control unit by using a network, province and ground edge cluster according to the emerging grid-connected main body grid-connected node-user association relationship, and uploading the emerging grid-connected main body aggregation control unit to a cloud system, wherein the cloud system is used for equating the aggregation control unit into a virtual load/unit based on the emerging grid-connected main body grid-connected node-user association relationship, the aggregation control unit and measurement data and hanging the virtual load/unit into a full-network traditional power grid model, and finally forming a full-network source network charge panoramic model of 10kV and above for business application of the cloud system.
2) The cloud system acquires the 10kV and above voltage class source network load storage operation data from all levels of edge clusters of the network, the province and the ground through a cloud edge two-stage fusion mode based on the power grid panoramic model, so that the comprehensive sensing capability of the whole network of 10kV and above voltage class is realized in the cloud system, and the problem that the current power grid monitoring cannot cover the 10kV and above voltage class simultaneously and is difficult to support the large power grid source network load storage collaborative operation is solved. In order to achieve comprehensive sensing capability of the whole network, the cloud system supports the comprehensive sensing capability of the cloud by constructing a cloud data center, cloud smart grid operation monitoring and the like.
a) The cloud data center takes cloud services such as RDS, OSS, OTS provided by a cloud platform as a support, supports the centralization of data or files with different sources, formats and qualities such as network, province, land edge clusters and other external systems into a unified data resource pool by constructing a data access agent application, realizes multi-source data access to the cloud system, and provides data service support for comprehensive perception of the cloud system; in addition, compared with a conventional data query method, the data center adopts a data resource index technology, and based on the data resource index, the quick lookup and quick call of all kinds of data in the whole network are realized in the cloud system, so that the data access efficiency is improved.
b) The cloud intelligent power grid operation monitoring realizes operation monitoring of full-network 10kV and above voltage class source network load storage based on full-network operation data and a panoramic model, provides full-network key information centralized display of full-network power grid operation key information, full-network power grid operation key indexes, key alarms, key information charts and other different dimensionalities, and supports classification monitoring according to monitoring object types, including full-network power generation and transmission operation monitoring, full-network market main body operation monitoring, full-network energy storage operation monitoring, full-network new energy operation monitoring, novel power system key index monitoring and the like.
3) The cloud system intelligent prediction is based on artificial intelligent technology to develop prediction model training and provide short-term and ultra-short-term prediction services to the whole network. The cloud system provides computing resources, computing frames and data resource services for intelligent prediction model training of the cloud system by means of computing resources of a CPU (Central processing Unit) of the cloud platform, a DaaS (data access system) big data platform component of the GPU and full-network mass operation data of a cloud data center, and supports a series of intelligent prediction works such as data acquisition, feature engineering, prediction model construction, prediction model training, prediction model release, prediction model fusion, prediction model encapsulation, prediction result generation, visual display and the like. On the one hand, the cloud system intelligent prediction supports full network prediction model training and prediction, and comprises full network 110kV and above bus load prediction, full network new energy power prediction and full network regional system load prediction, and the prediction result is used for supporting full network global optimization; on the other hand, model training of bus load prediction, new energy power prediction, system load prediction and distributed energy prediction in the jurisdiction of the edge cluster is supported on the basis of cloud artificial intelligence technology in a cloud system, and the trained model is downloaded to the edge cluster for deployment in a cloud-edge two-stage fusion mode, so that the prediction of the localization of the edge cluster is supported.
4) The cloud system relies on rich computing power resources of the cloud platform to construct a full-network ultra-large-scale global optimization function of 10kV and above. The global optimization is based on a power grid panoramic model, full-network source network load storage operation data, full-network prediction results and the like, active rolling optimization of the full network in 5 minutes or 15 minutes is carried out, adjustment requirements in market or non-market scenes are generated, and the adjustment requirements are issued to edge clusters for execution in a cloud-edge two-stage fusion mode. The cloud system global optimization comprises two functions of plan rolling control and real-time control.
5) The cloud intelligent operation and maintenance system is used for constructing a high-efficiency operation and maintenance system of a whole-network master station system such as a cloud system, a network, a province and ground edge cluster and the like, and supporting comprehensive monitoring, remote operation and comprehensive analysis of the operation state of the whole-network master station system. The cloud system establishes operation data interfaces with the cloud platform base, the virtual machine service, the container management service and each application module of the cloud to acquire operation logs, alarm information and the like, so that different object information, operation states and the like of cloud platform resources, virtual machines, container groups, containers and the like used by the cloud system are monitored; the cloud system collects different monitoring object information such as software and hardware account, container group, container and service assembly and the like of the whole network edge cluster in a cloud edge two-stage fusion mode, and the like, so that the centralized operation and maintenance monitoring of the whole network edge cluster is realized
The edge cluster is independently built by grid dispatching mechanisms of all levels of the network, the province and the ground based on the technologies of containers and the like, adopts layering design, and consists of three layers from bottom to top, including a public resource layer, a service platform layer and a service application layer;
specifically, the edge cluster is a power grid real-time monitoring system which is deployed in each level of dispatching mechanisms of the network, province and ground and is suitable for a novel power system, and the elastic access, real-time monitoring and cooperative control of controlled objects such as traditional stations, centralized new energy stations, mass emerging market main bodies and the like in the dispatching range are realized. The edge cluster consists of a public resource layer, a service platform layer and a service application layer. The public resource layer provides independent basic software and hardware resources for the edge cluster, the business platform layer provides standardized and unified public service for business applications, and the business application layer comprises high-level applications such as operation monitoring, operation prediction, operation control, calculation analysis and the like. And the edge cluster performs data interaction and application collaboration on the upper and cloud systems and the lower and edge gateways.
(1) In the technical aspect, the edge cluster provides the capacity of allocating running resources according to needs based on the container technology, the elastic expansion of the edge cluster business application is realized, and the plug and play of the application module is realized based on the standard interface provided by the distributed service bus and the message queue.
1) Edge cluster container runtime environment: the safety zone I and the safety access zone I adopt customized container operation environments meeting the safety protection requirements of the control zone of the power monitoring system; and the safe areas II and III construct a container running environment based on a general container service technology, and are compatible with cloud system application downloading deployment.
2) Data storage of the edge cluster application module: the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; the data is stored in the container-mounted edge cluster database and the storage service.
3) Data interaction between edge cluster application modules: the transfer of messages between application modules supported by different container groups is supported by a message bus. The application modules supported by the same container group have strong correlation in function and support data exchange in a mode of sharing memory, database and storage.
4) Service call of the edge cluster application module: and configuring a distributed service agent in the application module container group, and providing functional services to the outside through the distributed service agent by the application module. Each application module invokes relevant services through the distributed service agents as needed.
(2) In the aspect of business, the edge cluster is based on technologies such as containers, micro-services and the like to construct front-end acquisition with elastic expansion, and supports dynamic elastic access of controlled objects such as traditional plant stations, centralized new energy stations, mass emerging grid-connected main bodies and the like; front data parallel processing mechanisms based on container, message bus and other technical construction support large-scale access object monitoring and control; based on cloud-edge fusion cooperative control system, the real-time cooperative control of controlled objects such as traditional plant stations, centralized new energy field stations, massive emerging grid-connected main bodies and the like is supported.
1) The edge cluster uses the technologies of container, micro-service, container arrangement and the like to split and containerize the front-end acquisition. The front-end collection of the edge cluster is split into a data collection micro-service, a data convergence micro-service and a channel message storage micro-service according to a business logic link and operates in a container mode, and the front-end collection supports dynamic access of a mass collection channel through online elastic expansion of a micro-service container instance. The front collection of various micro-service container examples supports the self-defining quantity of the station channels, and one container example supports the processing of messages of a plurality of station channels.
2) A data preprocessing method of a dynamic container is designed, and the edge cluster processes data received in real time by using the method, so that operation monitoring and operation control of the plant station are realized. According to business logic, the data of the edge cluster pre-processing comprises: remote control data S 1 Manual operation data S 2 Real-time statistics S 3 Related history data S 4 The edge clusters need to process the data in parallel, then the data sizes of the different types of data are completely different, and the occupied resources are also completely different. The differentiated container capacity is dynamically allocated for different data, so that the matching between the data and the computing resources is realized, and the data preprocessing time delay is shortest under the condition of ensuring the minimum data processing resource cost, and the specific operation is as follows:
a) At the beginning of each time slot, the remote control data S 1 Manual operation data S 2 Real-time statistics S 3 Related history data S 4 Performing data preprocessing, extracting abnormal values, filling related missing values, finally obtaining corresponding data magnitude values of the data,
b) The speeds at which the four types of data were generated are expressed as: [ V 1 ,V 2 ,V 3 ,V 4 ]The speed of data generation is lowAt the time of the processing speed,at this time, the container capacity R i Only the minimum limit is required to be met. In turn, it is desirable to increase the capacity of the container in order to avoid loss of the generated data.
c) Computing resources [ C ] allocated for four classes of data to be jointly optimized edge cluster parallelism 1 ,C 2 ,C 3 ,C 4 ]And corresponding container capacity for four classes of data [ R ] 1 ,R 2 ,R 3 ,R 4 ]The task preprocessing delay is minimized, and the optimization problem is expressed as:
in the above formula, δ represents the duration of the task processing time slot, the constraint (1) represents that the container capacity allocated to each type of data is greater than the rate generated by the task minus the data processing rate, so as to ensure that the relevant data cannot be discarded, and the constraint (2) represents the limitation of the capacity size and the limitation of the computing resources.
d) The optimization problem is a typical mixed integer programming problem, and is to be solved in an optimized way by adopting a genetic algorithm.
e) After the calculation of the dynamic container is completed, the calculation result is obtained by automatic calculation according to the real-time operation data and is sent to a message queue for other services. Particularly, for the historical data, a mode of combining micro-service and dynamic container is adopted, so that the collection and storage service of the historical data and the historical alarm information is improved.
3) The method comprises the steps of supporting two modes of operation prediction by an edge cluster, remotely calling an intelligent prediction module of the cloud fusion cloud system to perform load prediction and new energy prediction and directly obtaining a prediction result from the cloud system, performing model training of bus load prediction, new energy power prediction, system load prediction and distributed energy prediction in a cloud system in a jurisdiction range of the edge cluster based on a cloud artificial intelligent technology, downloading a trained model to the edge cluster in a cloud two-stage fusion mode, and performing prediction based on operation data of the edge cluster after the edge cluster is deployed so as to obtain the prediction result.
4) Based on cloud edge cooperative control, under a plan rolling control mode, the edge cluster is further decomposed into rolling plans of various control units stored in the source network load under jurisdiction based on the current-level power grid model and operation data according to the total adjustment requirements of the edge cluster or market bid-up results issued by the cloud system, and issued to a regulation object for execution; in a real-time control mode, the edge cluster receives real-time active regulation demands issued by cloud unified frequency modulation, decomposes the real-time active regulation demands into real-time control instructions of various control units stored in the managed source network load, and issues the real-time control instructions to a regulation object for execution. In the edge cluster control, besides the conventional automatic control of AGC, AVC and the like, the fact that a mass of emerging grid-connected main body aggregation control units participate in the power grid operation control is considered, a real-time adjustment instruction is issued from a scheduling end to the adjustable load/mass emerging grid-connected main body aggregation control units by constructing an adjustable load control function, and the active automatic adjustment of the adjustable load resource/mass emerging grid-connected main body aggregation control units in the adjustment control range is realized, so that the closed-loop control process for realizing the real-time safety balance and frequency deviation adjustment of the power grid is realized.
In summary, it is proposed to construct an edge cluster with elastic expansion by adopting a dynamic container policy, which is used as a real-time operation control system of each stage of power grid dispatching mechanism of a network, a province and a local, and supports the elastic access and real-time monitoring of each stage of power grid dispatching mechanism to controlled objects such as traditional plant stations, centralized new energy sources, mass emerging grid-connected main bodies and the like.
The edge gateway is data interaction gateway equipment deployed at the sides of a plant station and a market main body, and adopts a unified technical architecture for upper access edge clusters and lower access controlled objects to provide functions of in-situ decision making, local autonomy and external service;
cloud side two-stage service fusion interaction is carried out by the cloud side system and the edge cluster through cloud side interaction cloud side service and cloud side interaction edge cluster Agent;
specifically, the edge gateway is deployed at the positions of grid connection points and the like of emerging grid connection main bodies such as a transformer substation, a distribution substation/station area/tower, distributed energy sources and the like, is used as data interaction gateway equipment for data export and command reception, is responsible for collecting local power grid operation data and marketing data, is uploaded to the affiliated edge cluster, and supports command operations such as power grid operation mode switching, frequency modulation/peak regulation and the like and supports power grid dispatching operation and power market operation;
a standardized cloud side interaction channel is constructed for realizing high concurrency uploading of cloud data and edge cluster data, wherein the cloud data and the service are rapidly pushed downwards; a cloud-edge fusion service mode is built for the services of novel power system operation monitoring, prediction, cooperative control, intensive operation and maintenance and the like;
in order to support efficient and safe interaction between a cloud system and each level of edge clusters of a network, a province and a ground, cloud side interaction cloud service comprising cloud base service, cloud product warehouse service, cloud application interaction service and cloud monitoring center service is constructed in the cloud system, and four types of interaction agents including base interaction Agent, product interaction Agent, application interaction Agent and monitoring interaction Agent are constructed in the edge clusters through Agent technology;
the cloud side interaction cloud service and the edge cluster interaction Agent jointly complete the configuration and establishment of four special interaction channels such as base downloading, product downloading and uploading, cloud side application interaction and cloud Bian Yunwei monitoring interaction, respectively serve as unified channel outlets of cloud side interaction services of all types, realize safe transmission of cloud side interaction services and provide interaction services which can be called as required for cloud side interaction service application;
the cloud edge business fusion interaction comprises cloud edge fusion operation monitoring, cloud edge fusion prediction, cloud edge fusion cooperative control, cloud edge block chain service and cloud edge fusion intelligent operation and maintenance.
(1) Cloud edge fusion operation monitoring: the cloud system gathers the operation data of the whole network from the network, province and ground edge clusters through cloud edge application interaction channels, and realizes real-time monitoring of the whole network 10kV and above traditional plant stations, centralized new energy and emerging grid-connected main bodies based on a panoramic model; the network, province and land edge clusters realize real-time monitoring of traditional plant stations, centralized new energy and emerging grid-connected main bodies in the regulation and management range.
(2) Cloud edge fusion prediction: the cloud system provides an intensive artificial intelligence training environment, and completes prediction model training based on the panoramic model and massive operation data, and provides a prediction result or a prediction model for the edge cluster; the edge cluster obtains a required prediction result from the cloud system through a cloud edge application interaction channel, or obtains a prediction model from the cloud system through a cloud edge product interaction channel, deploys the prediction model locally in a containerization mode and generates a localized prediction result.
(3) Cloud edge fusion cooperative control: the cloud system realizes full-network source network load storage rolling optimization and generates an adjustment plan based on a power grid panoramic model, full-network source network load storage operation data, full-network prediction results and the like, and the generated adjustment plan is issued to a network, province and ground edge cluster through a cloud side application interaction channel; and the network, province and ground edge clusters decompose the adjustment plan issued by the cloud system and issue control instructions to various control units of the source network charge storage, so that the cloud edge fusion and the operation control of the source network charge storage under the coordination are realized.
(4) Cloud edge block chain service: the cloud system uniformly provides standardized blockchain applications to realize the monitoring of the running condition of the whole-network blockchain applications; the edge cluster realizes blockchain management and sends the running state of the blockchain to the cloud; and the edge gateway and the aggregation type emerging grid-connected main body deploy standardized blockchain application to realize automatic uplink of key data of the aggregation type emerging grid-connected main body terminal. And the comprehensive monitoring and the trusted recording of the running condition and the regulation response of the whole network emerging grid-connected main body are realized through the cloud edge block chain service.
(5) Cloud edge fusion intelligent operation and maintenance: the cloud system monitors and interactively gathers information such as the state of the whole network edge cluster, alarms and the like through cloud Bian Yunwei, so that comprehensive monitoring and analysis of the whole network edge cluster operation are realized; the edge cluster realizes secondary operation monitoring of the cluster and the edge gateway, and the configuration is issued through a standardized remote operation and maintenance interface, so that the intensive remote maintenance of the edge gateway is realized.
In summary, a cloud-edge fusion interaction system constructed by adopting computer technologies such as a service agent is provided, a cloud-edge two-stage flattening, standardization and high-safety cloud-edge interaction channel is established, and efficient collaborative operation of cloud-edge two-stage service fusion interaction is supported.
The content in the method embodiment is applicable to the system embodiment, the functions specifically realized by the system embodiment are the same as those of the method embodiment, and the achieved beneficial effects are the same as those of the method embodiment.
While the preferred embodiment of the present application has been described in detail, the application is not limited to the embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (7)
1. Smart power grids system based on cloud limit fuses framework, characterized by comprising:
the cloud system module is used for connecting network, province and ground edge clusters, collecting whole network operation data, realizing global optimization analysis, whole network scheduling decision and panoramic information display, supporting whole network sharing and service sharing;
the edge end system module is used for elastically accessing, monitoring and cooperatively controlling controlled objects such as traditional stations, centralized new energy stations, mass emerging market main bodies and the like in a dispatching range;
and the edge gateway module is used for collecting local power grid operation data and marketing data, uploading the data to the edge cluster, supporting instruction operations such as power grid operation mode switching, frequency modulation peak shaving and the like, and supporting power grid dispatching operation and power market operation.
2. A smart grid scheduling method based on cloud edge fusion architecture is characterized by comprising the following steps:
constructing a smart grid system based on a cloud edge fusion architecture as set forth in claim 1;
collecting demand data of various control units stored in source network charges managed by the intelligent power grid system;
the various control units send calculation task requests to the edge end system module and the cloud system module through the edge end system module according to the local calculation capability;
the edge end system module further decomposes the edge cluster into a rolling plan of various governed source network load storage control units based on the current level power grid model and operation data according to the total adjustment requirement of the edge cluster or the market bid-winning result issued by the cloud system in a plan rolling control mode, and issues the rolling plan to a regulation object for execution;
the cloud system module receives real-time active regulation demands issued by cloud unified frequency modulation in a real-time control mode, decomposes the real-time active regulation demands into real-time control instructions of various control units stored in a source network under jurisdiction, and issues the real-time control instructions to a regulation object for execution.
3. The smart grid scheduling method based on cloud edge fusion architecture as set forth in claim 2, wherein the edge system module elastically accesses controlled objects such as a traditional plant station, a centralized new energy plant station, a mass emerging market body and the like in a scheduling range, and the smart grid scheduling method specifically includes:
the edge cluster provides the capacity of allocating running resources according to the need based on the container technology, realizes the elastic expansion of the edge cluster business application, and realizes the 'plug and play' of an application module based on a standard interface provided by a distributed service bus and a message queue;
(1) An edge cluster container operating environment;
the safety zone I and the safety access zone I adopt customized container operation environments meeting the safety protection requirements of the control zone of the power monitoring system; the safe area II and the safe area III construct a container running environment based on a general container service technology, and are compatible with cloud system application downloading deployment;
(2) Data storage of the edge cluster application module;
the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; storing the data into an edge cluster database and a storage service mounted on the container;
(3) Data interaction between the edge cluster application modules;
the message transmission support among the application modules supported by different container groups is carried out through a message bus, the application modules supported by the same container group have strong correlation in function, and the data exchange in a mode of sharing memory, a database and storage is supported;
(4) Service call of the edge cluster application module;
and configuring a distributed service agent in the application module container group, wherein the application modules provide functional services to the outside through the distributed service agent, and each application module calls related services through the distributed service agent as required.
4. The smart grid scheduling method based on the cloud edge fusion architecture as set forth in claim 3, wherein the edge system module further includes:
based on technologies such as containers, micro-services and the like, front-end acquisition with elastic expansion is constructed, and dynamic elastic access of controlled objects such as traditional plant stations, centralized new energy field stations, mass emerging grid-connected main bodies and the like is supported;
front data parallel processing mechanisms based on container, message bus and other technical construction support large-scale access object monitoring and control;
based on cloud-edge fusion cooperative control system, the real-time cooperative control of controlled objects such as traditional plant stations, centralized new energy field stations, massive emerging grid-connected main bodies and the like is supported.
5. The smart grid scheduling method based on the cloud edge fusion architecture as set forth in claim 4, wherein the dynamic elastic access of the edge system module to the controlled object further includes a data preprocessing method designed with a type of dynamic container, specifically:
collecting edge cluster preamble data, wherein the edge cluster preamble data comprises remote control data S 1 Manual operation data S 2 Real-time statistics S 3 Related history data 3 4 ；
At the beginning of each time slot, the remote control data S 1 Manual operation data S 2 Real-time statistics S 3 Related history data S 4 Preprocessing data, extracting abnormal value, filling up related missing value to obtain data value of correspondent data
The speed of generation of the four types of data is expressed as V 1 ,V 2 ,V 3 ,V 4 ]When the speed of data generation is lower than the processing speed, i.e.At this time, the container capacity R i Without processing, increasing the capacity of the container when the rate of data generation is higher than the processing rate;
computing resources [ C ] allocated for four classes of data to be jointly optimized edge cluster parallelism 1 ,C 2 ,C 3 ,C 4 ]And corresponding container capacity for four classes of data [ R ] 1 ,R 2 ,R 3 ,R 4 ]The task preprocessing delay is minimized, and the optimization problem is expressed as:
in the above formula, δ represents the duration of a task processing time slot, the constraint (1) represents that the capacity of a container allocated to each type of data is greater than the rate generated by the task minus the data processing rate, so as to ensure that the related data cannot be discarded, and the constraint (2) represents the limitation of the capacity and the limitation of the computing resources;
and optimizing and solving the optimization problem through a genetic algorithm, and automatically calculating according to real-time operation data to obtain a calculated result after the calculation of the dynamic container is completed, and sending the calculated result to a message queue for other services.
6. The smart grid scheduling method based on the cloud edge fusion architecture as set forth in claim 5, further comprising performing operation prediction in two modes of edge cluster support:
the first mode is that a cloud-edge fusion cloud system intelligent prediction module is remotely called to conduct load prediction and new energy prediction through a cloud-edge two-stage fusion mode, and a prediction result is directly obtained from a cloud system;
the second mode is to develop model training of bus load prediction, new energy power prediction, system load prediction and distributed energy prediction in the jurisdiction of the edge cluster in the cloud system based on cloud artificial intelligence technology, and to load the trained model to the edge cluster in a cloud-edge two-stage fusion mode, and after the edge cluster is deployed, develop prediction based on operation data of the edge cluster so as to obtain a prediction result.
7. The smart grid scheduling method based on the cloud edge fusion architecture of claim 2, wherein the cloud system adopts a 'cloud primary' scheme, and supports application 'micro-servitization' and system elastic capacity expansion upgrading based on a container running environment; based on an open and standard platform interface provided by a service bus and a message queue, a plug-and-play application module is constructed, and the cloud application is jointly constructed by supporting the whole network, specifically:
(1) Deploying an application module of the cloud system;
the cloud platform-based container running environment is deployed, and the cloud platform is used for realizing the services of management, internal communication, scheduling, start-stop, transverse expansion and the like of the container cluster;
(2) Communication among cloud system application modules;
based on virtual IP provided by the cloud platform VPC network, the application module service support composed of a plurality of container groups is mounted under one responsible balanced IP, so that load balancing of the application module on external service flow is realized;
(3) Data storage of the cloud system application module;
the data is stored in a storage volume mounted on the container and is managed by an application module, and the format supports customization; the data is stored in a cloud platform database and a storage service of the container mount;
(4) Data interaction between cloud system application modules;
the transfer of messages between application modules supported by different container groups is supported by a message bus. The application modules supported by the same container group have strong correlation in function and support data exchange in a mode of sharing memory, a database and storage;
(5) Service calling of the cloud system application module;
and supporting to package all or a certain piece of sub-function logic of the application module and mount the sub-function logic on the cloud service bus to provide functional service for the outside, and calling the service mounted on the cloud service bus by each application module through the cloud service bus according to the requirement.
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| CN117827382A (en) * | 2024-03-06 | 2024-04-05 | 国网四川省电力公司信息通信公司 | Container cloud resource management method based on resource deployment audit |
| CN117826694A (en) * | 2024-03-06 | 2024-04-05 | 北京和利时系统集成有限公司 | Intelligent electromechanical system |
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| CN117827382A (en) * | 2024-03-06 | 2024-04-05 | 国网四川省电力公司信息通信公司 | Container cloud resource management method based on resource deployment audit |
| CN117826694A (en) * | 2024-03-06 | 2024-04-05 | 北京和利时系统集成有限公司 | Intelligent electromechanical system |
| CN117827382B (en) * | 2024-03-06 | 2024-04-30 | 国网四川省电力公司信息通信公司 | Container cloud resource management method based on resource deployment audit |
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