ENTSO-ECloud and Edge Computing
Overview
Massive amounts of data are generated every second by power transmission networks. In 2023, the world generated approximately 123 zettabytes of data, according to International Data Corporation [1]. Advanced data analytic algorithms are used to transform such data into information and knowledge, which can be then used for network operations and/or parent energy services. Such data analytics rely upon information and communication technologies (ICTs): they have a critical role in data collection, transfer and processing [2]. Computing is a critical function of ICTs: it determines how data analytics typical of transmission networks are performed, and thus becomes the foundation for transmission network operations and services.
Cloud Computing, geo-distributed devices and equipment are connected to cloud data centres, supporting centralised decisions and issuing control orders. The cloud is an abstraction which separates the actions of storing, retrieving and computing on data from the physical constraints of doing so.
The schematics below illustrate the conventional cloud computing structure.
- Data producers generate raw data and transfer it to a cloud.
- Data consumers send requests for consuming data to this cloud.
However, the cloud is no longer just a storage shed. Its users demand that it contributes a lot more to computing tasks and more quickly than ever before. This is the case of Internet of Things (IoT) solutions, which will empower Transmission System Operators (TSOs). The quantity of raw data produced by TSOs will be huge: it will make conventional cloud computing not efficient enough to handle the computing demand. This means that most of the data produced by IoT will be consumed at the “edge of the telecommunication networks”.
The schematics below illustrates [3] the two-way computing streams in edge computing: the “things” are not only data consumers but also play as data producers. At the edge, the things can not only request service and content from the cloud but also perform the computing tasks from the cloud. Edge performs computing offloading, data storage, caching, and processing. It also distributes request and delivery service from cloud to users.
In Ref [2], three types of architectures are described for practical Cloud Computing (CC) and Edge Computing (EC) implementations in power transmission networks:
- “Thing” tier: This layer is widely addressed in the context of the IoT. The thing tier covers most of the electrical equipment and the communication access in the so-called Smart Grids (SG). This tier oversees the operations of SG and realising operation/control orders. Communication connections need to be established to transfer data to high layers.
“Edge” tier: The edge tier contains the intermediate storing; communication “edge” is a relative concept. For instance, “smart metering” belongs:
- to the thing tier when it performs sensing and transferring data; and
- to the edge layer when it becomes a platform for home energy analytics (computation).
- “Cloud” tier: This tier consists of control, storing and computing centres. Compared with the edge tier, the clouds are designated with high- performance storage and computing elements. These powerful resources are deployed to perform complex analyses with a long-term time scale and a grid-wide geographical scope.
Challenges in exploiting the full potential of CC and EC [3]:
- Limited bandwidth resources;
- Heterogeneous working environments;
- Naming - The naming scheme for EC needs to handle the mobility of things, a highly dynamic network topology, and privacy and security protection, as well as the scalability targeting the extremely large amount of unreliability;
- Data Abstraction;
- Service Management;
- Privacy and Security concerns;
- Optimisation Metrics – In EC there are multiple layers with different computation capabilities. Workload allocation becomes an issue i.e. which layer to handle the workload or how many tasks to assign at each part; and
- Dynamic workload allocation.
Benefits
EC pushes the frontier of computation applications away from centralised nodes to the communication network’s extremes. EC leverages computing resources closer to sensors and users to carry out data analytics. It then brings benefits from several features [3, 4] such as system delay reduction, improved system scalability and availability, increased data security and privacy. Moreover, EC lightens the burden of cloud computing centres. Cloud computing provides scalable computing and storage resources. The right combination of cloud- and edge-based applications is the key to maximum performance.
Numerous TSOs functions can benefit from cloud and edge computing:
- Data collection for asset monitoring, especially from IoT captors, supporting asset management;
- Automatic flow control for distributed energy resources (DER) integration (in association with batteries or dynamic line rating), distributed voltage control, faster automatic service restoration;
- HD video surveillance for intrusion detection; and
- Monitoring of network traffic, suspicious activity detection by edge devices.
For each function, real time data are treated at the edge and complex data such as topology and forecast are sent by the cloud.
The European Commission aims to provide European businesses and public authorities with secure, sustainable and interoperable cloud infrastructures and services. To achieve this goal, the European Commission has set in motion tools resulting from the European data strategy [5], the digital strategy [6], the digital decade [7], the industrial strategy [8] and the Digital Europe Programme [9].
Current Enablers
A cloud enabler refers to a technology, service, or tool that simplifies or facilitates its deployment and management, together with the integration of applications and resources within a cloud computing environment. These enablers typically improve the accessibility, scalability and flexibility of cloud-based services for organisations and users. Cloud enablers include:
- Application Programming Interfaces (APIs);
- Cloud management platforms;
- Cloud brokering services;
- Automated computing;
- Service-Oriented Architectures (SOA); and
- Virtualisation
The three major enabling technologies of EC are:
- Edge Intelligence [10];
- 5G; and
- Containerisation, orchestration.
R&D Needs
R&D as indicated in [2, 3, 4, 11] focuses on:
- Storage and Power Optimisation: larger memory devices for the storage of the acquired raw grid parameter data are needed. Different storage systems are required at multiple levels such as sensing levels in the input layer, communication level and application level for data processing. Furthermore, storage systems are required to be secure from cyber-attacks and to avoid the issue of data profusion;
- Dedicated software to handle data complexity;
- The EU-funded project VEDLIOT has developed a Deep Learning (DL) IoT platform. Instead of traditional algorithms, Artificial Intelligence (AI) and DL enables the large complexity of data handling to be addressed. The distributed approach allows applications to be divided into smaller and more efficient components: they work together in larger collaborative systems within the IoT, enabling AI-based algorithms distributed over IoT devices from edge to cloud [12].
- Scalability: the complete integration of advanced sensing technologies with every component of the power grid.
- Continuous monitoring: not only of grid assets, but also of communication, storage and processing elements of the cloud-edge continuum as part of the cyber-physical system.
- Dynamic resources allocation: optimal work and resources allocation according to the current status and needs of the system which can change dynamically (e.g. due to communication network failure).
Standardisation [13] is also required:
- The standardisation of communication processes;
- EC–CC systems can be deployed with a unified communication adapter using any category of drivers. However, after the EC node receives all the information, the data fusion methods for sensors in EC among power equipment need to be improved in future EC–CC systems for the transmission grid;
- ETSI: IoT and G5 [14];
- The main ETSI IoT standardisation activities are conducted at radio layer in 3GPP (LTE-M, NB-IoT and EC-GSM-IoT) and at the service layer in oneM2M. A wide range of technologies work together to connect things on the IoT. ETSI is involved in standardising several of these technologies; and
- ETSI’s Smart Applications REFerence (SAREF) ontology allows connected devices to exchange semantic information in many applications’ domains. ETSI ISG CIM specifies protocols (NGSI-LD API) running “on top” of IoT platforms and allows for the exchange of data together with its context.
The technology is in line with milestone “Improvement of the speed of dynamic simulations by application of new computing technologies (e. g. quantum computing)” under Mission 3 and milestone “Highly innovative real time power flow simulation tools (e. g. based on HPC or quantum computing)” under Mission 4 of the ENTSO-E RDI Roadmap 2024-2034.
TSO Applications
Examples
| Location: Greece [15] | Year: 2023–2026 |
|---|---|
| Description: Demonstration of intelligent edge paradigm in the Energy sector. | |
| Design: The aim of ACES is to develop a distributed, opportunistic, collaborative, heterogeneous, self-managed, self-organising edge services environment, primarily edge-to-edge and secondly on the edge-to-cloud continuum under a cognitive edge-services architecture with multiple agents (AI, ML) creating autonomous actions. The role of IPTO is to create artificial scenarios of its inner procedures (optimal power flow, Market Clearing) and extend them (Digital Twin, Machine-Learning Anomaly Detection) into a new programming model to be integrated into the ACES platform. In the framework of the energy sector’s digital transformation, ACES envisions three use-cases in IPTO:
| |
| Results: Work in progress. | |
| Location: Thessaloniki, Greece – Blagoevrad, Bulgaria [16] | Year: 2021–2024 |
|---|---|
| Description: Real-time wide-area monitoring in a cross-border scenario. | |
| Design: The work focuses on the integration and validation of a 5G-enhanced Wide Area Monitoring (WAM) framework within the Bulgarian and Greek power transmission systems, managed by the Bulgarian and Greek TSOs accordingly. This extensive interconnected energy grid, with over 30,000 km of high-voltage lines and 600 substations, relies on a standalone Fiber Optic (FO) network for communication. Despite the FO network’s advantages, it encounters limitations and hinders long deployment times and high costs, which Smart5Grid UC4 aims to overcome by demonstrating the benefits of 5G technology, particularly Ultra-Reliable Low Latency Communications (URLLC) network slicing, to enhance efficiency, flexibility and resilience. | |
| Results: The deployment to overcome the aforementioned limitation by demonstrating the benefits of 5G technology, particularly URLLC network slicing, to enhance efficiency, flexibility and resilience. | |
| Location: Shaoxing City, China [1, 17] | Year: 2018 |
|---|---|
| Description: A demonstration of a real-time monitoring system of transmission lines covers the 500 kV and 220 kV lines. | |
| Design: The structure of the EC–CC system consists of four layers: the perceptual layer, the EC layer, the network layer and the application layer. For power transmission lines, the perceptual layer includes several detecting devices and sensors. The application layer lies in the Could node: it aims to monitor the state of power lines and update the model in EC nodes. | |
| Results: The stability and performance of the whole system were tested and showed good performance in reducing the transmission latency and saving the bandwidth. | |
Technology Readiness Level The TRL has been assigned to reflect the European state of the art for TSOs, following the guidelines available here.
- TRL 9 for IoT implementation in transmission networks (cables, transformers, substations).
- TRL 7/8 for operation applications requiring very low latency.
References and further reading
Siemens, “MindSphere: enabling the world’s industries to drive their digital transformations,”
ITS GROUP. “The New Digital Substation Architecture” Feng Cheng et al., “Smart grid encounters edge computing: opportunities and applications,” Adv. App. En., vo. 1, no. 4, p. 100006, Dec. 2021.
S. Douch et al., “Edge computing technology enablers: A systematic lecture study,” IEEE Access vol. 10, p. 69265, Jun. 2022.
W. Shi et al., “Edge computing: vision and challenges,” IEEE Int. Things J, vol. 3, no. 5, pp. 637–646, Oct. 2016.
European Commission. “European data strategy.”
EU4Digital. “EU Digital Strategy.”
European Commission. “Europe’s Digital Decade”
European Commission. “European industrial strategy”
European Commission. “The Digital Europe Programme”
Siemens. “Phasor measurement unit (PMU).” C. J. Wu et al., “Machine learning at Facebook: Understanding inference at the edge,” Proc. 25th IEEE Int. Symp. High Perform. Comput. Archit. (HPCA), pp. 331344, Mar. 2019.
L. Junlong et al., “Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications” J. Mod. Pow. Sys. & Clean Ener, V. 10, p. 805, 2022.
European Commission. “Very Efficient Deep Learning in IOT”
ETSI Report (2020), “Multi-Access Edge Computing (MEC) MEC 5G Integration,” 2020
ENTSO-E, “ENTSO-E Research, Development, & Innovation Roadmap 2024-2034,” 10.07.2024
Smart5grid. “use case 4.”
L. Junlong et al., “Edge-cloud Computing Systems for Smart Grid: State-of-the-art, Architecture, and Applications,” J. Mod. Pow. Sys. & Clean Ener, V., vol. 10, p. 805, 2022.