IoT Edge Computing has the potential to transform the energy industry, through its ability to process large amounts of information in real time, and ultimately improve the safety and efficiency of operations.
The growth of IoT devices has multiplied by millions the amount of data that can and must be processed by enterprises in their digitization process. In order to make this processing more efficient a new computing model has emerged very strongly: Edge Computing, which complements the processing of centralized Cloud infrastructures with Machine Learning and Artificial Intelligence algorithms being processed at the edge, i.e at the node from where data has originated and is closer to users or devices.
This data computing at the Edge can be executed on powerful servers on mobile network equipment («Thick» Edge), or on smaller, more distributed nodes across plants («Thin» or «Far» Edge). In any case, it opens big opportunities for new revenue generations as well as for cost optimization.
IDC´s report on «Edge Computing Solutions Powering the Fourth Industrial Revolution» validates the importance of these three pillars. In a survey amongst 802 industry leaders did deploy edge computing 30% answered their primary motivation to be bandwidth costs, 27% data protection, and 19% latency constraints. 12% of companies surveyed came from the energy sector.
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Edge Computing use case in the Electricity Sector
Power generation itself is being decentralized: from a traditional linear structure, where energy traveled from large generation plants to the world, to modern distribution networks that consider a more decentralized and more distributed model with the incorporation renewable energy sources, prosumers that generate their own consumption, and new elements that allow storage on a larger scale.
All this implies an exponential growth in the complexity of network operation and maintenance, as well as in supply and demand forecasting. In order to have visibility of these complex structures, different devices are being installed, from simple IoT sensors or Smart Meters, to communication interfaces in generation or transmission equipment that allow data to be extracted through standardized protocols.
IoT Edge Computing enables real-time, secure and scalable analysis of these complex data structures at the most distributed points of the network, optimizing maintenance tasks and improving supply and demand forecasting
IoT Edge is being driven by a strong investment by technology manufacturers in cutting-edge solutions that feature smaller, lower-powered and lower-priced microcomputers that can function as IoT Edge computing nodes at scale.
Likewise, operating systems and software are being created to enable these nodes with the ability to execute algorithms in a cybersecure manner, typically packaged in virtualized software containers such as Docker.
The introduction of these new technologies in a workforce that traditionally consists mostly of automation engineers (OT), and far fewer IT and telecommunications engineers (IT), creates a gap in the skills needed to do so. This gap is evident in the number of IoT projects that remain in so-called «PoC (proof of concept)». It is relatively simple to conduct a lab experiment to conduct IoT Edge computing, but when it comes to taking the project to a real environment with hundreds or thousands of distributed nodes, the need to have a market-driven SLA can generate great frustrations due to the lack of internal capabilities to do so.
According to the Gartner Cool Vendors in Edge Computing, 2021 report, «As edge computing moves from proof-of-concept and monolithic projects to repeatable enterprise applications, vendor products that simplify deployments are gaining attention. Solutions that allow you to solve – the problem of IoT Edge complexity – in a unique way, stand out.»
The goal will be to move from the traditional models of large investments (CAPEX), to more flexible models where the initial investment is lower, but the OPEX of traditional IT can be higher ( which include SaaS licences, maintenance costs and upgrade services). This means a cultural change and also may require regulatory changes to allow the energy sector to move forward at the required speed.
Traditionally, production data belongs to the operator, but in a more distributed environment and with an increasingly complex value chain, the boundaries between who owns the data and who can exploit is blur.
To give an example, the Artificial Intelligence and Machine Learning algorithms to be used in an IoT Edge environment for energy distribution need to be trained with the data generated by user devices (smart meters, self-consumption, chargers, batteries, sensors, etc). However, this data resides within the scope of manufacturers and cannot be shared as it would present a violation of the data protection law.
In this sense, public funding projects are needed in order to create consortia to analyse further these issues. The best example of the latter is Platoon project, which focuses on proposing solutions for smart grids through the exploitation of data based on the integration of IDS reference architecture, for information exchange amongst European Agents.
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Barbara IoT will extend the PLATOON Platform to the Edge, to digitize the European energy sector
Despite these challenges, it is clear that IoT Edge Computing has the potential to transform the energy industry, through its ability to process large amounts of information in real time, and ultimately improve the safety and efficiency of operations. Any company able to adequately address these challenges will be able to benefit and be at the forefront of the transformation of the energy sector.
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