A power system regulation cloud power grid operation analysis platform

Abstract

A power system regulation cloud power grid operation analysis platform, comprising: a software-level service SaaS layer, a support platform service layer, an infrastructure-level service IaaS layer, an external connection layer, a communication layer, and a display layer. And a container management module is provided, which is used for constructing a container-based standardized application management mode and a container management method, facilitating the improvement of the standardized construction and homogeneous management of the sub-provincial dispatching analysis and decision-making applications in the country, forming a centralized, coordinated, standardized and efficient "large operation" system, and achieving higher standards in aspects such as the efficiency, rationality and security of the resource utilization of the power system regulation cloud platform.

Description

Technical Field

[0001] The present invention belongs to the field of power regulation cloud, and particularly relates to a power system regulation cloud power grid operation analysis platform. Background Art

[0002] As one of the most important terminal consumption energies, electric power plays a crucial role in improving the energy regulation level. At present, the electricity consumption efficiency of power customers in China is relatively low, and there is a large amount of electric energy waste. At the same time, the peak-valley difference of electricity consumption is continuously widening, and the utilization efficiency of power generation and power supply equipment is relatively low, with great potential for power saving. In order to optimize the power consumption mode, improve the terminal power regulation, change the mode of solely relying on expanding the construction of power plants and power grids to meet the electricity growth, further promote energy conservation and emission reduction and reduce environmental pollution, it is imperative to build an energy efficiency public service platform and carrier to support energy conservation services.

[0003] As a new type of network computing mode, cloud computing technology can provide resource sharing and services of computing, storage, network, and software to various network applications at low cost and high efficiency. It is a new stage in the development of distributed computing, parallel computing, and grid computing, and has now become one of the important trends in the development of computer science. By using cloud computing technology to build an energy efficiency public service platform, various types of energy, technologies, organizational environments, etc. can be integrated into one, promoting the exchange of energy efficiency information and knowledge sharing; with the help of cloud computing, organizations can build various personalized, proactive, and integrated integrated innovation energy usage models, and carry out energy regulation work more effectively, which will be the future technical development trend of energy efficiency services and guidance.

[0004] The power system regulation cloud platform is a key technology to improve the power energy management ability and ensure the regulation safety, and is also of great significance to the future high-speed development of power production regulation. At present, big data is gradually playing a role in the power field, and many fields have begun to define their own system architectures, and develop specific software required for power regulation on the system architectures. However, the software developed based on these architectures has problems such as weak basic service capabilities, low efficiency, and poor platform on-demand adjustment capabilities, and can no longer meet the multi-level, diversified, and continuous demand changes of different power management and power users. These requirements include: (1) the requirement to support the rapid elastic scaling of the basic resources of the platform, (2) the requirement for the efficient operation and global management of the analysis and decision-making center, and (3) the requirement for rapid construction, agile delivery, and convenient operation and maintenance of applications.

[0005] Therefore, it is necessary to design a power system regulation cloud platform and its container management components to provide technical support for the development, deployment, and operation environment of various applications built on the platform, and meet the requirements of rapid construction, agile delivery, and convenient operation and maintenance of applications. Summary of the Invention

[0006] To improve the management and control level of the smart grid, the present invention provides a power system regulation cloud power grid operation analysis platform, including:

[0007] The SaaS layer, which is a software-level service layer, is used to provide software cloud application products. The energy efficiency service cloud system realizes the collection and monitoring of energy efficiency information, provides diagnostic analysis of energy efficiency parameters, and realizes power energy efficiency cloud regulation and cloud services for enterprise users through cloud products via the energy efficiency service application SaaS layer;

[0008] The platform service layer includes a support platform, a common component management platform, and a cloud container engine platform; among them, the support platform includes a model data platform, a big data platform, an operation data platform, and a data exchange platform; the common component management platform includes a diary management component, an alarm management component, a permission management component, an internal and wide-area message bus management component, and an internal and wide-area service bus management component; the cloud container engine platform includes an image building module, a private image repository, an application orchestration module, and a PaaS platform;

[0009] The IaaS layer, which is an infrastructure-level service layer, includes a server resource pool, a storage resource pool, and a network resource pool, is used to provide a basic data solution for applications and provide cloud functions of the regulation cloud platform;

[0010] The external connection layer is used to dock with the databases of multiple related platforms to obtain data, store power system-related information, and provide information data for power regulation;

[0011] The communication layer is used to connect the external connection layer and the IaaS layer, and provides an information transmission channel between the platform and each user and system. The information transmission channel includes a power information network, the Internet, and a mobile Internet;

[0012] The display layer is used to provide a unified access entrance for users and visually display the functions and services supported by the SaaS layer, the platform service layer, and the IaaS layer.

[0013] To realize the application development, deployment, and operation and maintenance methods based on the shared service architecture under the regulation system, on the basis of deeply studying the container resource management technology, the present invention breaks through the key technologies of standardized management of regulation cloud applications based on containers, and constructs a comprehensive technical support system for the system application development, deployment, and operation environment based on containers. Taking applications as the core, it studies the lightweight cluster management technology based on containers to realize the on-demand dynamic allocation of container resources; studies the application management technology based on the container image repository to realize the automatic construction, release, download, and deployment of applications based on container images; studies the application full life cycle management technology based on containers to construct an automatic application operation hosting environment; studies the application fault monitoring and security protection strategies for the container environment to realize the safe and reliable operation of applications.

[0014] The beneficial effects of the present invention include:

[0015] Firstly, based on the regulation cloud platform, combining the characteristics of emerging technologies and traditional dispatching automation technologies, in addition to meeting the requirements for distributed processing and services of massive data, the container management platform of the regulation cloud platform improves the resource utilization efficiency, meets the processing capacity requirements of the system during peak business and load surges, and achieves higher standards in terms of the efficiency, rationality, and security of platform resource utilization.

[0016] Secondly, under the architecture of the scheduling control system based on shared services, based on the key technologies of container-based resource management and application operation management, the whole process support for the deployment, upgrade, expansion, rollback, and offline of power grid analysis and decision-making applications can be realized. By building an application development and operation environment covering all links of development, deployment, trial operation, and operation, the rapid construction, agile delivery, and convenient operation and maintenance of power grid analysis and decision-making applications can be achieved, providing support for continuously improving the real-time sharing ability of regulation information of the scheduling control system, the complex logic processing, distributed computing, and continuous reliable service ability of applications, and the on-demand access ability of application services.

[0017] Thirdly, the standardized application management construction mode and container management method based on containers are conducive to improving the standardized construction and homogeneous management of provincial and sub-provincial dispatching analysis and decision-making applications, forming a "large operation" system that is centralized, collaborative, and efficient, and further improving the regulation ability to control large power grids and the ability to optimize the allocation of resources on a large scale.

[0018] Moreover, a containerized regulation cloud platform that provides all-round support for application development, deployment, and operation environment is built using Docker container technology, realizing the on-demand dynamic allocation of container resources and the rapid construction, agile delivery, and convenient operation and maintenance of power grid analysis and decision-making applications, providing support for continuously improving the real-time sharing ability of regulation information of the new generation scheduling control system, the complex logic processing, distributed computing, and continuous reliable service ability of applications, and further improving the regulation ability to control large power grids and the ability to optimize the allocation of resources on a large scale. Also, through the dynamic resource allocation method of virtual machines, the virtual machines in the physical machines with high load rates can be dynamically migrated to the physical machines with low load rates, so as to achieve the purpose of balancing the load of each physical machine in the virtual machine cluster.

[0019] Finally, a resource-saving construction mode for the scheduling system is established. On the premise of fully considering security, in terms of system management, system operation and maintenance, and equipment construction, the IT system resources are transformed from the mode of "sharing when needed" to the mode of "available when needed", improving the utilization efficiency of system resources such as communication, network, and hardware, and forming a new resource-saving construction mode. Description of the Drawings

[0020] Figure 1Platform framework diagram of the present invention. Detailed implementation mode

[0021] To better understand the present invention, the system of the present invention will be further described below with reference to the description of the embodiments in conjunction with the drawings.

[0022] To comprehensively understand the present invention, numerous specific details are mentioned in the following detailed description. However, those skilled in the art should understand that the present invention can be implemented without these specific details. In the embodiments, well-known methods, processes, and components are not described in detail to avoid unnecessarily complicating the embodiments.

[0023] See Figure 1 As shown, the present invention provides a power system regulation cloud power grid operation analysis platform, including:

[0024] The SaaS layer, which is a software-level service layer, is used to provide software cloud application products. The energy efficiency service cloud system realizes the collection and monitoring of energy efficiency information, provides diagnostic analysis of energy efficiency parameters, and realizes power energy efficiency cloud regulation and cloud services for enterprise users through cloud products through the energy efficiency service application SaaS layer;

[0025] The platform service layer includes a support platform, a common component management platform, and a cloud container engine platform; among them, the support platform includes a model data platform, a big data platform, an operation data platform, and a data exchange platform; the common component management platform includes a diary management component, an alarm management component, a permission management component, an internal and wide-area message bus management component, and an internal and wide-area service bus management component; the cloud container engine platform includes an image building module, a private image repository, an application orchestration module, and a PaaS platform;

[0026] The IaaS layer, which is an infrastructure-level service layer, includes a server resource pool, a storage resource pool, and a network resource pool, and is used to provide a basic data solution for applications and provide cloud functions for the regulation cloud platform;

[0027] The external connection layer is used to dock with the databases of multiple related platforms to obtain data, store power system-related information, and provide information data for power regulation;

[0028] The communication layer is used to connect the external connection layer and the IaaS layer, and provides an information transmission channel between the platform and each user and system. The information transmission channel includes a power information network, the Internet, and a mobile Internet;

[0029] The display layer is used to provide a unified access entrance for users and visually display the functions and services supported by the SaaS layer, the platform service layer, and the IaaS layer.

[0030] Preferably, the PaaS platform includes a container management module, and the container management module at least includes a monitoring unit and a balancing unit:

[0031] The monitoring unit is used to perform lightweight container cluster monitoring on the container resources of the control cloud platform to obtain cluster monitoring information. Specifically, the monitoring unit includes:

[0032] The monitoring index unit is used to set aggregation indexes from multiple-dimensional perspectives as monitoring indexes according to the dynamics of container cluster nodes.

[0033] The real-time monitoring unit is used to perform real-time monitoring on the performance indexes of container cluster nodes and the containers on the nodes to obtain cluster monitoring information.

[0034] The balancing unit is used to perform dynamic balancing allocation on the Docker container cluster resources of the control cloud platform. Specifically, the balancing unit includes:

[0035] The data pulling unit is used to pull data according to the cluster monitoring information in the same network.

[0036] The evaluation unit is used to evaluate the resource usage status of the containers on the cluster nodes and the load level of the container cluster.

[0037] The allocation unit is used to perform balanced allocation of the Docker container cluster resources of the control cloud platform according to the dynamic expansion / contraction strategy of the container cluster nodes of the control cloud platform.

[0038] Preferably, the allocation unit is used to perform balanced allocation of the Docker container cluster resources of the control cloud platform according to the dynamic expansion / contraction strategy of the container cluster nodes of the control cloud platform. Specifically, it includes:

[0039] The expansion / contraction sub-unit is used to comprehensively calculate various monitoring indexes. When the calculation result is greater than the expansion threshold, the application is expanded and new containers are added, the same image is started, and the container is added to the Docker container cluster node; when the calculation result is less than the contraction threshold, the application is contracted and existing containers are reduced, and the container is removed from the Docker container cluster node.

[0040] Preferably, the monitoring unit, which is used to perform lightweight container cluster monitoring on the container resources of the control cloud platform to obtain cluster monitoring information, further includes: a visualization analysis unit, which is used to perform periodic analysis on the cluster monitoring information and visually display the analyzed data.

[0041] Preferably, the IaaS layer also provides virtual machine cluster resources. The virtual machine cluster has multiple physical machines, and each physical machine corresponds to at least one virtual machine.

[0042] Preferably, the container management module further includes: a virtual machine balancing unit for dynamically allocating the load of a virtual machine cluster, specifically including:

[0043] A calculation unit for calculating the resource weight used by each virtual machine, the resource weight used by each physical machine, and the average resource weight used by the physical machines;

[0044] A judgment unit for judging the difference between the resource weight used by the physical machine and the average resource weight used by the physical machines;

[0045] An execution unit for when the difference between the resource weight used by any physical machine and the average resource weight used by the physical machines is higher than the balance threshold, specifically including:

[0046] A first determination subunit for determining the physical machine corresponding to the maximum resource weight used by the physical machine as the balance source machine;

[0047] A second determination subunit for finding the physical machine corresponding to the minimum resource weight used by the physical machine as the balance destination machine;

[0048] A balance difference subunit for calculating the balance difference between the resource weight used by the physical machine of the balance source machine and the average resource weight used by the physical machines;

[0049] A third determination subunit for finding, among all the balance source machines, the virtual machine corresponding to the virtual machine resource weight closest to the balance difference as the balance virtual machine;

[0050] A migration subunit for migrating the balance virtual machine to the balance destination machine.

[0051] Preferably, the resource weight used by the virtual machine, the resource weight used by the physical machine, and the average resource weight used by the physical machines are calculated according to the following formula:

[0052]

[0053]

[0054] α = 1 / P

[0055] Where j is the physical machine number, i is the virtual machine number, P is the total number of physical machines in the virtual machine cluster, n is the total number of virtual machines, v is the number of virtual machines on each physical machine, VM jiRate is the virtual machine resource ratio of the virtual machine resource weight of the i virtual machine in the j physical machine, is the processor load rate of the i virtual machine in the j physical machine, VM jiRAMallocate is the storage allocation of the i virtual machine in the j physical machine, HOSTjiRate The entity machine resource ratio for the entity machine of the j physical machine, and α is the average entity machine resource ratio for the average used resource weight of the entity machine.

[0056] The beneficial effects of the present invention include:

[0057] Firstly, based on the regulation cloud platform, combined with the characteristics of emerging technologies and traditional dispatching automation technologies, in addition to meeting the requirements for distributed processing and services of massive data, the container management platform of the regulation cloud platform improves the resource utilization efficiency, meets the processing capacity requirements of the system during peak business and load surges, and achieves higher standards in terms of the high efficiency, rationality, and security of platform resource utilization.

[0058] Secondly, under the scheduling control system architecture based on shared services, based on the key technologies of container-based resource management and application operation management, the whole process support for the deployment, upgrade, expansion, rollback, and offline of power grid analysis and decision-making applications can be realized. Building an application development and operation environment covering all links from development, deployment, trial operation to operation can realize the rapid construction, agile delivery, and convenient operation and maintenance of power grid analysis and decision-making applications, and provide support for continuously improving the real-time sharing ability of regulation information in the new generation of scheduling control systems, the complex logic processing, distributed computing, and continuous reliable service capabilities of applications, and the on-demand access ability of application services.

[0059] Thirdly, the standardized application management construction mode and container management method based on containers are conducive to improving the standardized construction and homogeneous management of provincial and sub-provincial dispatching analysis and decision-making applications, forming a "large operation" system that is centralized, collaborative, and efficient, and further improving the regulation ability to control large power grids and the ability to optimize the allocation of resources on a large scale.

[0060] Moreover, a containerized regulation cloud platform that provides all-round support for application development, deployment, and operation environment is constructed using Docker container technology, realizing the on-demand dynamic allocation of container resources and the rapid construction, agile delivery, and convenient operation and maintenance of power grid analysis and decision-making applications, providing support for continuously improving the real-time sharing ability of regulation information in the new generation of scheduling control systems, the complex logic processing, distributed computing, and continuous reliable service capabilities of applications, and further improving the regulation ability to control large power grids and the ability to optimize the allocation of resources on a large scale. Also, through the dynamic resource allocation method of virtual machines, the virtual machines in the physical machine with a high load rate can be dynamically migrated to the physical machine with a low load rate, thus achieving the purpose of balancing the load of each physical machine in the virtual machine cluster.

[0061] Finally, establish a resource-saving scheduling system construction model. On the premise of fully considering safety, in aspects such as system management, system operation and maintenance, and equipment construction, transform the IT system resources from the mode of "shared when needed" to the mode of "available when needed", improve the utilization efficiency of system resources such as communication, network, and hardware, and form a new resource-saving construction model.

[0062] Only the preferred embodiments of the present invention are described here, but their intention is not to limit the scope, applicability, and configuration of the present invention. On the contrary, the detailed description of the embodiments enables those skilled in the art to implement the present invention. It should be understood that appropriate changes and modifications can be made to some details without departing from the spirit and scope of the present invention determined by the appended claims.

Claims

1. A power system regulation cloud power grid operation analysis platform, characterized in that Including: The SaaS layer, which is the software-level service layer, is used to provide software cloud application products. The energy efficiency service cloud system realizes the collection and monitoring of energy efficiency information, provides diagnostic analysis of energy efficiency parameters, and realizes power energy efficiency cloud regulation and cloud services for enterprise users through cloud products via the energy efficiency service application SaaS layer; The platform service layer includes a support platform, a public component management platform, and a cloud container engine platform; among them, the support platform includes a model data platform, a big data platform, an operation data platform, and a data exchange platform; the public component management platform includes a diary management component, an alarm management component, a permission management component, an internal and wide-area message bus management component, and an internal and wide-area service bus management component; the cloud container engine platform includes an image building module, a private image repository, an application orchestration module, and a PaaS platform; The IaaS layer, which is the infrastructure-level service layer, includes a server resource pool, a storage resource pool, and a network resource pool, and is used to provide a basic data solution for applications and provide cloud functions for regulating the cloud platform; The external connection layer is used to dock with the databases of multiple related platforms to obtain data, store power system-related information, and provide information data for power regulation; The communication layer is used to connect the external connection layer and the IaaS layer, and provides an information transmission channel between the platform and each user and system. The information transmission channel includes a power information network, the Internet, and a mobile Internet; The display layer is used to provide a unified access entrance for users and visually display the functions and services supported by the SaaS layer, the platform service layer, and the IaaS layer; The PaaS platform includes a container management module; the IaaS layer also provides virtual machine cluster resources. The virtual machine cluster has multiple physical machines, and each physical machine corresponds to at least one virtual machine; The container management module at least includes: a balancing unit; The balancing unit is used to perform dynamic balanced allocation on the Docker container cluster resources of the regulation cloud platform. Specifically, the balancing unit includes: A data pulling unit, which is used to pull data according to cluster monitoring information in the same network; An evaluation unit, which is used to evaluate the resource usage status of containers on cluster nodes and the load level of the container cluster; An allocation unit, which is used to perform balanced allocation of the Docker container cluster resources of the regulation cloud platform according to the dynamic expansion / contraction strategy of the regulation cloud platform container cluster nodes; The container management module also includes: a virtual machine balancing unit, which is used to perform dynamic allocation of the load of the virtual machine cluster. Specifically, it includes: A calculation unit, which is used to calculate the resource usage weight of each virtual machine, the resource usage weight of each physical machine, and the average resource usage weight of physical machines; A judgment unit, which is used to judge the difference between the resource usage weight of the physical machine and the average resource usage weight of the physical machine; An execution unit, which is used when the difference between the resource usage weight of any physical machine and the average resource usage weight of the physical machine is higher than the balance threshold. Specifically, it includes: A first determination subunit, which is used to determine the physical machine corresponding to the maximum resource usage weight of the physical machine as the balance source machine; The second determination subunit is configured to find out the physical machine corresponding to the minimum physical machine usage resource weight as the balanced target machine; The balance difference subunit is configured to calculate the balance difference between the physical machine usage resource weight of the balanced source machine and the average physical machine usage resource weight; The third determination subunit is configured to find out the virtual machine corresponding to the virtual machine usage resource weight that is closest to the balance difference among all the balanced source machines as the balanced virtual machine; The migration subunit is configured to migrate the balanced virtual machine to the balanced target machine; The virtual machine usage resource weight, the physical machine usage resource weight, and the average physical machine usage resource weight are calculated according to the following formula: Among them, j is the physical machine number, i is the virtual machine number, P is the total number of physical machines in the virtual machine cluster, n is the total number of virtual machines, and v is the number of all virtual machines on each physical machine. is the virtual machine usage resource ratio of the virtual machine usage resource weight of the i-th virtual machine in the j-th physical machine. is the processor load rate of the i-th virtual machine in the j-th physical machine. is the storage allocation amount of the i-th virtual machine in the j-th physical machine. is the physical machine usage resource ratio of the physical machine usage resource weight of the j-th physical machine, and α is the physical machine average usage resource ratio of the physical machine average usage resource weight.

2. The platform according to claim 1, wherein The container management module at least includes a monitoring unit: The monitoring unit is configured to perform lightweight container cluster monitoring on the container resources of the regulated cloud platform to obtain cluster monitoring information. Specifically, the monitoring unit includes: The monitoring index unit is configured to set aggregated indexes from multiple-dimensional perspectives as monitoring indexes according to the dynamics of the container cluster nodes; The real-time monitoring unit is configured to perform real-time monitoring on the performance indexes of the container cluster nodes and the containers on the nodes to obtain cluster monitoring information.

3. The platform according to claim 1, wherein The allocation unit is configured to perform balanced allocation of the Docker container cluster resources of the regulated cloud platform according to the dynamic expansion / contraction strategy of the container cluster nodes of the regulated cloud platform. Specifically, it includes: The expansion / contraction subunit is configured to comprehensively calculate various monitoring indexes. When the calculation result is greater than the expansion threshold, the application is expanded and new containers are added, and the same image is started and the container is added to the Docker container cluster node. When the calculation result is less than the contraction threshold, the application is contracted and existing containers are reduced, and the container is removed from the Docker container cluster node.

4. The platform according to claim 2, wherein The monitoring unit is configured to perform lightweight container cluster monitoring on the container resources of the regulated cloud platform to obtain cluster monitoring information. It further includes: a visualization analysis unit configured to perform periodic analysis on the cluster monitoring information and visually display the analyzed data.

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