The challenges and opportunities of Edge Computing for the Energy Sector

The growth of IoT devices has multiplied the amount of data that can - and must - be processed by business during their digitisation process by millions. To make this processing more efficient a powerful new computing model has emerged: Edge Computing, which complements the processing of centralised Cloud infrastructures with Machine Learning and Artificial Intelligence algorithms that are processed at the Edge, i.e. at the node where data originates and closer to users or devices.

Data processing at the Edge can be done using powerful servers on mobile network equipment («Thick» Edge), or on smaller, more widely-distributed nodes across plants («Thin» or «Far» Edge). In both formats, it opens up great opportunities for new revenue streams as well as for cost optimisation.

3 fundamental pillars of Edge Computing

1. Greater scalability: by distributing storage and processing over many locations, less investment is needed for infrastructure and capacity for higher volume of traffic or better algorithms.

2. Greater data security and sovereignty: as the data does not leave its original location, the risks for illegal access or theft are reduced dramatically.

3. More data processed and less latency: Frequency analysis make it possible to work with thousands of data almost instantaneously, with just milliseconds needed for analysis and response. This translated into near real-time use cases - something unthinkable in Cloud Environments that are 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 of 802 industry leaders who did deploy Edge Computing, 30% stated that their primary motivation was bandwidth costs, 27% data protection, and 19% latency constraints. 12% of the companies surveyed were from the energy sector.

Advantages of IoT Edge Computing in the Energy Sector

Power generation itself is being decentralised: from a traditional linear structure - where energy travels from large generation plants to the world - to modern distribution networks that can support a more decentralised and widely-distributed model with renewable energy sources, prosumers generating their own energy, and new large scale storage elements.

All this is leading to exponential growth in the complexity of network operation and maintenance, as well as in supply and demand forecasting. In order to keep all of these complex structures in view, different devices are now being installed, from simple IoT sensors or Smart Meters, to communication interfaces in generation or transmission equipment that allows data to be extracted through standardised protocols.

IoT Edge Computing enables real-time, secure, and scalable analysis of these complex data structures at the most far-flung points of the network, optimising maintenance tasks and improving supply and demand forecasting

Key elements in IoT Edge Computing

• Oil and gas distribution infrastructures: one day of downtime can cost over $20 million, and large operators have an average of five such failures per year. 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. This all comes 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, of which large operators have tens of thousands. The centrepiece of the new technology is the Smart Transformer, which in addition to being “connected”, enables real-time dynamic regulation of power supply to the different lines that now also feed new elements such as electrical chargers or batteries. The IoT Edge makes these adjustments in real time, thus preventing failures and avoiding unnecessary travel, 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% up on 2019, of which more than half were installed in industry. However, few users really made the most of these installations. Through IoT Edge Computing and with the addition of sensors that can measure production or storage conditions, or smart actuators (relays) that can control consumption, energy savings can be increased significantly.

What are the main challenges of IoT Edge Computing for the Energy Sector?

IoT Edge is being driven by 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 give these nodes the ability to execute algorithms in a cybersecure way, typically packaged in virtual software “containers” such as Docker.

Challenges for energy industry

1. Staff training

The introduction of these new technologies in a workforce traditionally consisting mainly of automation engineers (OT), and far fewer IT and telecommunications engineers (IT), means there is a gap in the available skillset. This is evident in the number of IoT projects that remain in so-called «PoC (proof of concept)». It is relatively simple to run IoT Edge Computing in a test setting, but when it comes to taking the project into a real environment with hundreds or thousands of distributed nodes, the need for a market-driven SLA can be very frustrating 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 into repeatable enterprise applications, vendor products that simplify deployments are gaining attention. Solutions that allow you to solve the issue [of IoT Edge complexity] – in a unique, remarkable way.”‍

2. Adaptation of the financial and legal structure

The eventual goal is to move from the traditional large investment models (CAPEX) to more flexible models with a smaller initial investment, but where traditional IT OPEX can be higher (including SaaS licences, maintenance costs, and upgrade services). This requires a cultural sea-change and also may need regulatory changes to allow the energy sector to move forward at the necessary rate.

3. Last but not least: data ownership.

Production data traditionally belongs to the operator, but in a more widely-distributed environment with an increasingly complex value chain, the boundaries between who owns the data and who can use it become blurry.

For 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 falls within the remit of the manufacturers and cannot be shared without violating data protection laws.

This means that public funding projects are needed to further analyse such issues. A good example of this is the Platoon project, which focuses on proposing solutions for smart grids through the use of data based on the integration of IDS reference architecture, for exchanging information exchange between European Agents.

Despite these challenges, it is clear that IoT Edge Computing has the potential to transform the energy industry, with the ability to process large amounts of information in real time and ultimately improve the safety and efficiency of operations. Any business that can adequately address these challenges can benefit from it and position themselves at forefront of the energy sector transformation.

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