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. All of this is based on 3 fundamental pillars:
1) Greater scalability: by distributing storage and processing along many locations, the investment needed for infrastructure and capacities for a higher traffic volume or better algorithms is more controlled
2) Greater data security and sovereignty: by not leaving its original location, the risks for illegal access or theft are reduced dramatically
3) More data processed and lower latency in responses: The analysis of frequencies allow working with thousands of data almost instantaneously, and for the analysis and responses , the time needed is about milliseconds. This means near real-time use cases, somtehing that is unthinkable in Cloud Environments more oriented to offline analysis of batch information
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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advantages of IoT Edge Computing in the Energy 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 forecastingDavid Purón
IoT Edge Computing is particularly relevant in:
- Oil and gas distribution infrastructures: where one day of downtime due to a failure can cost more than $20 million, and the average is five times in a year for large operators. The IoT Edge makes it possible to analyse the data in real time to avoid problems in advance, or else to identify their causes, much faster. All this with a high level of security to avoid problems such as the one that occurred at Colonial Pipeline a few months ago.
- Electrical Substations: especially in medium to low voltage, which are counted by tens of thousands in large operators. The central element of this revolution is the Smart Transformer, which in addition to being «connected», enables real-time dynamic regulation of power supply to the different lines on which now hang new elements such as electrical chargers or batteries. The IoT Edge provides these adjustments in real time thus, preventing failures and avoiding unnecessary displacements as well as generating new services that can increase the ROI of the entire value chain.
- Consumption points: 2020 was an unprecedented year for energy self-consumption. In Spain alone, 596 megawatts were installed, 30% more than in 2019, of which more than half were installed in industrial companies. However, few were the users who made the most of these installations. Through IoT Edge Computing, and accompanied by sensors that can measure production or storage conditions, or smart actuators (relays) that can control consumption, energy savings can be raised by double-digit figures.
What are the main challenges of IoT Edge Computing for the Energy Sector?
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.
However, the adoption of these technologies is not without its challenges for industrial energy companies:
1) First, training Employees
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.»
2) Second is the adaptation of the financial and legal structure of industrial energy companies.
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.
3) Third, but not less important, with the increase of information generated and processed, the big challenge is data ownership. 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.
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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