The Internet of Things (IoT) is about connecting everyday objects to communication networks with the aim of providing a range of services or applications in energy areas such as smart grids, home automation or intelligent transportation. IoT encompasses both [1]:

• Machine-to-machine (M2M) communication: devices interact and share data without the direct involvement of people; and
• Connecting “things” to networks with the aim of enabling people to remotely control processes or manage their devices.

Electrical power networks have made use of IoT applications [2] by following closely the path of technology availability. Advances in computing, databases and analytical tools now allow for the rapid application of predictive and prescriptive analytics to large volumes of Supervisory Control And Data Acquisition (SCADA), advanced Metering Infrastructure (AMI), and data from other IoT devices.

The current trend in today’s sensor technology is the integration of multiple sensors and other connected devices to form wireless sensing networks (WSNs). The figure below shapes the 3 layers, making WSNs [3] as deployed in electricity network (transmission and distribution) systems.

1. The first layer is the perception or input layer. It consists of the deployment of multiple sensors on various components of the transmission system (referred to as input sensors or source sensor nodes).
2. The second layer eases the transfer of the raw transmission grid parameter data into the application layer for further processing. The network layer is twofold: the core networks and the access networks [4].
3. The third layer is called the application layer. It is built around several application infrastructures, middleware and application systems, such as SCADA servers and SCADA dashboards. This is where transmitted data are analysed by expert operators.

Use of IoT has several benefits:

• The sensor nodes can be deployed remotely to acquire raw grid parameter values, which are critical to ensure the monitoring of individual grid components.
• The raw grid parameter values acquired can be interfaced with hardware for processing or control purposes through remote terminal units and programmable logic controller devices.
• The grid parameter data can be processed in the application layer for the detection of growing fault characteristics for online transmission system monitoring or online individual equipment monitoring.
• Machine learning techniques can be used to process the raw grid parameter data for fault classification and fault forecasting, leading to the predictive maintenance of the system. This forms an outage management system [5] for the transmission (distribution) system components.

The enablers of IoT are listed below:

• Sensor Devices [6]: they are used to collect and transmit data in real-time. The use of sensors enhances effectiveness and functionality, and plays a critical role in successful IoT implementations. The general model of a smart sensor [7] as implemented in smart grids is essentially based upon four modules, as depicted in the diagram below [3].

• Communication Technologies: wireless communication systems have a critical role in activating IoT. They connect sensor devices to IoT gateways and perform end-to-end data communications between these elements of IoT. The table summarises the features of several technologies [6].

  

• Actuators: actuators are devices that take electrical input from the automation systems, transform the input into action, and act on devices and machines within IoT systems. In transmission networks, pneumatic, hydraulic and electrical actuators are frequent technologies very often encountered embedded in network security sub-systems.
• IoT Data and Computing [6]: processing and analysing the data generated by IoT allows deeper insights to be gained and accurate responses of the interconnected networks system. Two terms can be mentioned here: Cloud Computing and Edge Computing.

R&D mainly focuses on:

• Storage and Power Optimisation: 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.
• Credibility of Data Acquisition: addressing the challenges of sensor node identification, heterogeneity, losses and synchronicity is a must.
• Heterogeneity: there are numerous grid parameters required to be sensed for the full grid such as sensing current, voltage, temperature, humidity, pressure, chemical, gas and vibration calls for the heterogenous mixtures of sensors.
• Big Data & Data Profusion: data preprocessing at the edge level is suggested to reduce the size of data near the source, thereby reducing the size and power required for data transportation, data storage and data processing. This also suggests the use of distributed file systems, NoSQL databases and data processing tools in power grid computations such as Hadoop, Storm, Spark and Grid Gain to avoid the issue of data profusion.
• Scalability: the complete integration of advanced sensing technologies with every component of the power grid is difficult to build and implement. The power grid is a huge entity with a multitude of parameters of a diverse nature that requires monitoring and controlling.
• Security: This is required in every layer of a sensor system, especially the network and the application layer. Thus, breach or loss of data may directly impact the power security and stability of the power grid. Two options are available for addressing the above issues: the design of advanced and secured protocols with authentication and authorisation as for access and control [8]. All the parameters can be retained in blockchains and accessed through blockchain protocols [9].

Standardisation [10] is also required by:

• The main ETSI IoT standardisation activities are conducted at radio layer in 3GPP (LTE-M, NB-IoT and EC-GSM-IoT) and at service layer in oneM2M. A wide range of technologies work together to connect things in IoT. ETSI is involved in standardising several of these technologies.
• SAREF is ETSI’s Smart Applications REFerence ontology that 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.

Currently ongoing R&D projects are among others:

• FLEXITRANSTORE [11]: a project supported by the EU, an Integrated Platform for Increased FLEXIbility in smart TRANSmission grids with STORage Entities and large penetration of Renewable Energy Sources (RES).
• NEPHELE [12]: a project to efficiently manage hyper-distributed applications across heterogeneous infrastructure in the Cloud-to-Edge-to-IoT continuum that ensures that the convergence of IoT technologies and development of synergetic orchestration mechanisms takes place.
• IoT-NGIN [13]: the project aims to uncover pattern-based meta-architecture and optimising IoT/machine-to-machine and 5G/machine-cloud-machine communications by extending the edge cloud paradigm. It enables user and self-aware autonomous IoT systems through privacy-preserving federated machine learning and ambient intelligence, with augmented reality support. Finally, IoT-NGIN aims at distributed IoT cybersecurity and privacy.
• INCODE [14]: this project envisions the design and development of an open platform for the deployment and dynamic management of end user applications, over distributed, heterogeneous and trusted IoT-Edge node infrastructures, with enhanced programmability features and tools at both the network infrastructure level and the service design and operational level. It develops advanced monitoring solutions for high-voltage substations.
• ENORASI [15]: the purpose of the ENORASIS system is to provide automated inspection on the key electrical elements of a substation, using an optical and thermal camera system on a moving robot and performing reliable periodic measurements.
• Hedge-IoT [16]: the project facilitates the orchestrated operation of energy systems with increased flexibility, through the HEDGE-IoT Reference Architecture, which aims to establish functional interconnectivity among IoT/edge devices and fog/cloud platforms.
• 5G-VICTORI [17]: the project conducts large scale trials for advanced vertical use case verification focusing on Transportation, Energy, Media, Smart Factories, by leveraging 5G network technologies developed in 5G-PPP Phase-1 and Phase-2. The energy use case demonstrates the real-time supervision of IoT-enabled industrial equipment and facilities through a private 5G deployment.

The technology is in line with milestones “Advanced reconfiguration and control of network and assets” and “Adoption of real-time data sensors and IoT devices for a more efficient, cost-effective and safer remote monitoring” under Mission 1 of the ENTSO-E RDI Roadmap 2024-2034.

Examples

TRL 9 for IoT implementation in transmission networks (cables, transformers, full network).

1. IEA, “More Data, Less Energy: Making Network Standby More Efficient in Billions of Connected Devices”.

2. Deloitt, “How IoT technology is driving energy innovation”.

3. A. Swain et al., “Sensor technologies for transmission and distribution systems: A review of the latest developments,” Energies, vol. 15, no. 19, p. 7339, Oct. 2022.

4. Z.Y. Hu et al., “Design for communication network of internet in on-line monitoring system for power transmission and transformation equipment,” High Volt. Eng., vol. 41, pp. 2252–2258, 2015.

5. U.S. DOE, “Quadrennial Technology Review 2015”.

6. N. H. Motlagh et al., “Internet of Things (IoT) and the energy sector,” Energies, vol. 13, no. 2, p. 494, Jan. 2020.

7. Y. Huo, Y. et al., “Advanced Smart Grid Monitoring: Intelligent Cable Diagnostics using Neural Networks”, Proc. 2020 IEEE ISPLC 2020; pp. 1–6.

8. A. Mohammad et al., “User authentication and authorization framework in IoT protocols,” Computers, vol. 11, no. 10, p. 147, Nov. 2022.

9. C. Banks et al., “Blockchain for Power Grids”, Proc. 2019 SoutheastConf, Huntsville, 2019; pp. 1–5.

10. ETSI, “Internet of Things (IoT)”.

11. G. Fotis et al., “Scalability and replicability for smart grid innovation projects and the improvement of renewable energy sources exploitation: The FLEXITRANSTORE case,” Energies, vol. 15, no. 13, p. 4519, Jun. 2022.

12. European Commission, “A lightweight software stack and synergetic meta-orchestration framework for the next generation compute continuum”.

13. European Commission, “Next Generation IoT as part of Next Generation Internet”.

14. INCODE, “Homepage - INCODE project (incode-project.eu)”.

15. ENORASI, “About ENORASI Project (enorasi-project.com)”.

16. Hedgeiot, “Hedgeiot Homepage”.

17. 5G-VICTORI, “Home - 5G-VICTORI (5g-victori-project.eu)”.

18. ENTSO-E, “ENTSO-E Research, Development, & Innovation Roadmap 2024-2034”.

19. L. Shi, “Tower tilt angle on-line prognosis by using solar-powered loRa sensor node and sliding XGboost predictor,” IEEE Access, vol. 7, pp. 86168–86176, Jun. 2019.