Flexibility of the electrical power system: the ability of the electrical power system to quickly and efficiently adapt to changing demand for electricity (power), taking into account the operating modes of electrical grid equipment and generation equipment.
Regulated object, RO: electrical installation and (or) their set, connected in the prescribed manner to electrical grid facilities that are part of general-purpose power supply systems, which has the technical property of changing its operating mode (consumption and (or) generation, output of electrical energy) according to request and is not a dispatch object integrated into the system of centralized operational dispatch control of the electric power system.
Note – Special cases of RO may include power receiving installations of electrical energy consumers with a controlled load, a generating facility, an electrical energy storage system, and electric charging stations (ECS).
Distributed regulated objects, DRO: a set of RO located in different geographical territories within the general purpose power supply system(s).
Electric power flexibility of the RO: the ability of the RO to change the technological mode of production and (or) consumption of electricity in response to an external control signal. When the RO is connected (integrated) to the FESCOM system for adaptive control of the power consumption mode, the RO's ability to change the technological mode in response to an external control signal is converted into a controlled, automated function.
Adaptive control system for electricity consumption mode FESCOM, SAURP: a hierarchical system consisting of control systems (s) and a set of adaptive control tools, designed to solve problems of transforming the technical properties of aggregated and individual regulated objects, to influence the amount or nature of the use of electrical energy from general purpose power supply systems into a managed function, ensuring the integration of managed objects into operational dispatch control systems, as well as integration with external automated systems and hardware and software systems.
Note 1 – a general purpose power supply system is understood as a set of electrical installations and electrical devices designed to provide electrical energy to various consumers of electrical networks, in accordance with GOST 32144–2013.
Note 2 – an external automated system, in accordance with GOST R - 70450-2022, means an autonomous (not part of SAURP) automated system of a network organization (enterprise resource management, personnel management, customer interaction management, etc.) or other organizations, including public authorities, for which information exchange with SAURP is provided.
Adaptive control of the mode of electricity consumption from general-purpose power supply systems, AURP: a process controlled and managed by SAURP, the purpose of which is to influence the amount or nature of the use of electrical energy consumed by end consumers from a general-purpose power supply system, by automatically changing settings and (or) structure aggregation of managed objects.
Edge computing is a distributed computing concept in which data processing occurs close to the source of its generation or at the “edge” of the network, rather than relying solely on centralized cloud servers. The goal of edge computing is to reduce latency, expand real-time data processing capabilities, and reduce the load on information and communication channels and cloud resources, bringing computing power closer to where it is needed.
In traditional cloud computing models, data is sent to a centralized data center for processing and analysis. Edge computing, on the other hand, involves processing data locally on devices or on nearby edge servers. This proximity to the data source allows for faster response times and improved performance, making it especially useful for applications that require low latency, such as Internet of Things (IoT) devices, autonomous vehicles, and augmented reality.
Edge computing is characterized by its decentralized nature, where computing resources are distributed across different locations. This approach is beneficial in scenarios where real-time data processing is critical, bandwidth is limited, or where privacy and security require local processing. Edge computing can complement cloud computing by offloading certain tasks to local, on-site devices, optimizing overall system efficiency and responsiveness.
Edge computing is a distributed computing concept in which data processing occurs close to the source of its generation or at the “edge” of the network, rather than relying solely on centralized cloud servers. The goal of edge computing is to reduce latency, expand real-time data processing capabilities, and reduce the load on information and communication channels and cloud resources, bringing computing power closer to where it is needed.
In traditional cloud computing models, data is sent to a centralized data center for processing and analysis. Edge computing, on the other hand, involves processing data locally on devices or on nearby edge servers. This proximity to the data source allows for faster response times and improved performance, making it especially useful for applications that require low latency, such as Internet of Things (IoT) devices, autonomous vehicles, and augmented reality.
Edge computing is characterized by its decentralized nature, where computing resources are distributed across different locations. This approach is beneficial in scenarios where real-time data processing is critical, bandwidth is limited, or where privacy and security require local processing. Edge computing can complement cloud computing by offloading certain tasks to local, on-site devices, optimizing overall system efficiency and responsiveness.
Edge computing is not an end-to-end technology in the truest sense, as it focuses on a specific area of data processing rather than the entire lifecycle of a product or service. However, they can be used in combination with other end-to-end technologies to achieve a more efficient data processing system.
Cross-cutting technologies are a set of technologies that are not related to any specific application area, but are used in many different areas. They are important components in various projects and products, but are not the core functionality of those products. Examples of end-to-end technologies:
Regulated object, RO: electrical installation and (or) their set, connected in the prescribed manner to electrical grid facilities that are part of general-purpose power supply systems, which has the technical property of changing its operating mode (consumption and (or) generation, output of electrical energy) according to request and is not a dispatch object integrated into the system of centralized operational dispatch control of the electric power system.
Note – Special cases of RO may include power receiving installations of electrical energy consumers with a controlled load, a generating facility, an electrical energy storage system, and electric charging stations (ECS).
Distributed regulated objects, DRO: a set of RO located in different geographical territories within the general purpose power supply system(s).
Electric power flexibility of the RO: the ability of the RO to change the technological mode of production and (or) consumption of electricity in response to an external control signal. When the RO is connected (integrated) to the FESCOM system for adaptive control of the power consumption mode, the RO's ability to change the technological mode in response to an external control signal is converted into a controlled, automated function.
Adaptive control system for electricity consumption mode FESCOM, SAURP: a hierarchical system consisting of control systems (s) and a set of adaptive control tools, designed to solve problems of transforming the technical properties of aggregated and individual regulated objects, to influence the amount or nature of the use of electrical energy from general purpose power supply systems into a managed function, ensuring the integration of managed objects into operational dispatch control systems, as well as integration with external automated systems and hardware and software systems.
Note 1 – a general purpose power supply system is understood as a set of electrical installations and electrical devices designed to provide electrical energy to various consumers of electrical networks, in accordance with GOST 32144–2013.
Note 2 – an external automated system, in accordance with GOST R - 70450-2022, means an autonomous (not part of SAURP) automated system of a network organization (enterprise resource management, personnel management, customer interaction management, etc.) or other organizations, including public authorities, for which information exchange with SAURP is provided.
Adaptive control of the mode of electricity consumption from general-purpose power supply systems, AURP: a process controlled and managed by SAURP, the purpose of which is to influence the amount or nature of the use of electrical energy consumed by end consumers from a general-purpose power supply system, by automatically changing settings and (or) structure aggregation of managed objects.
Edge computing is a distributed computing concept in which data processing occurs close to the source of its generation or at the “edge” of the network, rather than relying solely on centralized cloud servers. The goal of edge computing is to reduce latency, expand real-time data processing capabilities, and reduce the load on information and communication channels and cloud resources, bringing computing power closer to where it is needed.
In traditional cloud computing models, data is sent to a centralized data center for processing and analysis. Edge computing, on the other hand, involves processing data locally on devices or on nearby edge servers. This proximity to the data source allows for faster response times and improved performance, making it especially useful for applications that require low latency, such as Internet of Things (IoT) devices, autonomous vehicles, and augmented reality.
Edge computing is characterized by its decentralized nature, where computing resources are distributed across different locations. This approach is beneficial in scenarios where real-time data processing is critical, bandwidth is limited, or where privacy and security require local processing. Edge computing can complement cloud computing by offloading certain tasks to local, on-site devices, optimizing overall system efficiency and responsiveness.
Edge computing is a distributed computing concept in which data processing occurs close to the source of its generation or at the “edge” of the network, rather than relying solely on centralized cloud servers. The goal of edge computing is to reduce latency, expand real-time data processing capabilities, and reduce the load on information and communication channels and cloud resources, bringing computing power closer to where it is needed.
In traditional cloud computing models, data is sent to a centralized data center for processing and analysis. Edge computing, on the other hand, involves processing data locally on devices or on nearby edge servers. This proximity to the data source allows for faster response times and improved performance, making it especially useful for applications that require low latency, such as Internet of Things (IoT) devices, autonomous vehicles, and augmented reality.
Edge computing is characterized by its decentralized nature, where computing resources are distributed across different locations. This approach is beneficial in scenarios where real-time data processing is critical, bandwidth is limited, or where privacy and security require local processing. Edge computing can complement cloud computing by offloading certain tasks to local, on-site devices, optimizing overall system efficiency and responsiveness.
Edge computing is not an end-to-end technology in the truest sense, as it focuses on a specific area of data processing rather than the entire lifecycle of a product or service. However, they can be used in combination with other end-to-end technologies to achieve a more efficient data processing system.
Cross-cutting technologies are a set of technologies that are not related to any specific application area, but are used in many different areas. They are important components in various projects and products, but are not the core functionality of those products. Examples of end-to-end technologies:
- Programming technologies: programming languages, development environments, debugging tools, etc.
- Databases: database management systems, data warehouses, indexing and information retrieval technologies, etc.
- Network technologies: data transfer protocols, routing and switching technologies, network devices, etc.
- Security technologies: authentication and authorization mechanisms, information security systems, encryption technologies, etc.
- Development tools: development environments, version control systems, build and test tools, etc.
- Project management technologies: project management methodologies, planning and resource management tools, task tracking systems, etc.
- Data analytics technologies: data collection and analysis tools, machine learning and artificial intelligence methods, etc.