Why Edge Computing is the Missing Link for the Energy Sector

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Moving forward, edge computing will allow the energy sector to better meet the challenges of growing energy demand, improve resource usage, and enable a more sustainable energy ecosystem.

The energy industry is going through a major transformation as it confronts numerous challenges, such as integrating renewable energy sources, the surging demand for electricity, and the increase in electric vehicles. One way the energy sector can address these challenges is by using edge computing.

In today’s energy industry, a vast amount of data is created across the generation, transmission, and distribution networks, says Arnie de Castro, principal product manager of industry products at SAS, an analytics software provider.

He says bringing all that data into a centralized computer infrastructure, such as the cloud, would require considerable bandwidth. Edge computing is needed to help process this data to be compressed, reduced, and transmitted efficiently or support local decision-making.

Satisfies the Need For Speed

When energy companies need to make control decisions in milliseconds, any delay caused by moving data between a centralized computer infrastructure and the edge is unacceptable, de Castro says.

“Edge computing can aggregate all this information and schedule transmission of the data in batches for processing at the control center.

“With this aggregation, edge computing makes it easier for the utility company to identify areas of outage for faster restoration, leading to a more reliable grid and happier customers.”

While most grid-edge devices currently offer grid-edge computing, the computing is closed-loop, which means it does not allow anyone except the original equipment manufacturers (OEMs) to use it, says Haider Khan, senior director of energy analytics at ICF, an advisory and technology services provider.

“Currently, the technology for real-time computing is focused primarily on leveraging the cloud to run algorithms,” he explains. “The algorithms compute the required signal sent to the grid-edge devices using the cloud to react to an objective, need, or constraint on the distribution grid system.”


This adds a time lag that limits the ability of grid-edge technologies to respond to grid needs, which change every few microseconds, he notes. Grid-edge computing would enable technologies like thermostats to react to the grid requirements within the necessary timeframe to avoid voltage, thermal, or frequency violations.

In the future, grid-edge computing will likely mature from basic monitoring and OEM controls to enable third parties, such as distribution grid operators, to manage the devices autonomously, according to Khan.

“The application areas of this could span a variety of services, including load/demand management, generation/storage control, self-healing networks, transactive energy, and microgrid control,” he says.

Edge Offers Grid Stability, Reliability

By decentralizing computational processes and data storage, edge computing enables real-time analysis and decision-making at the network’s periphery and alleviates strain on centralized data centers, says Andy Foster, product director of IOTech Systems, an open-source edge data platform provider.

“This approach not only reduces latency and bandwidth requirements but also enhances grid stability, allowing utilities to optimize power distribution and consumption with greater efficiency at the moment they need it.”

Electricity grids that once depended on centralized data processing increasingly benefit from edge computing’s localized data analysis, says Carl Moberg, CTO of Avassa.io, a provider of edge management and operations solutions.

“At the application layer, devices and sensors within the grid can instantly process and react to fluctuations, demands, and potential faults,” he says. “This real-time responsiveness means reduced outages, efficient energy distribution, and a significant boost in grid reliability and innovation.”

According to Moberg, edge computing facilitates instantaneous decision-making, improving when to draw power from renewables and when to rely on traditional sources.

By deploying edge computing devices and sensors at renewable energy sites, grid operators can gather crucial data about energy generation, storage, weather conditions, and demand fluctuations, Foster says.

He adds that this data is then used to predict energy surpluses and deficits and respond to fluctuations in renewable energy production. This results in a more stable and reliable grid that supports the variable nature of renewable, sustainable energy sources.

Edge computing also plays a crucial role in helping to manage the additional complexity of a fully decentralized grid, according to Foster.

“New edge computing capabilities support both horizontal integration across distributed energy resources,” he says. “This enables highly responsive local control decisions and also vertical integration with the data centers providing the overall grid control.”

Edge Computing and Data Sharing

Edge computing also has the potential to significantly impact building energy management and thus help mitigate the impacts of operational carbon emissions from the built environment, says Colm Nee, CTO at Enlighted, a property technology IoT solutions provider.

“Edge computing enables real-time monitoring and control of energy systems to maximize savings, reduce inefficiency, reduce downtime, enhance data security, and easily manage data communications,” he says.

The rapid advancement of Internet of Things (IoT) technology and the wealth of data it generates necessitate compatibility between various sectors, structures, software, and users, according to Nee.

He says organizations can establish a secure framework for sharing data with IoT applications through edge processing. This facilitates enhanced operational efficiency, streamlined integration of applications and enables intelligent decision-making at a local level within a building.

An edge architecture enables real-time monitoring, analysis, and control of a building’s energy components, including lighting and HVAC, functions that typically occur at a local building level, Nee says.

“Real-time occupancy data that drives more reactive building improvements are better served at the edge to influence lighting energy savings by turning off the lights in areas where people have just left and temperature control savings through integrations with local HVAC systems based on occupancy,” Nee notes.

Edge Computing Facilitates Electric Vehicle-to-Grid Communication

According to Foster, edge computing also provides localized data processing and analytics capabilities at charging stations and within electric vehicles.

“This enables faster and more precise monitoring of battery health, grid management, and dynamic load balancing, which are crucial for optimizing the charging process and minimizing strain on the electrical grid,” he says.

Edge computing can also facilitate vehicle-to-grid communication, he adds. This typically happens when electric vehicles are connected to a charging station and have excess energy stored in their batteries that can be transferred back to the grid to help meet energy demand.

“This two-way flow of energy from the grid to the vehicle and from the vehicle to the grid contributes to our energy systems’ overall sustainability and resilience,” Foster says.

The Bottom Line

Edge computing will allow the energy sector to meet the challenges of growing energy demand, improve resource usage, and enable a more sustainable energy ecosystem.

Edge computing facilitates instantaneous decision-making, optimizing when to draw power from renewables and when to rely on traditional sources, according to Moberg.

“As the energy sector charts its course into a sustainable future, edge computing at the application layer stands as a beacon, illuminating the way,” he says.


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Linda Rosencrance
Technology journalist
Linda Rosencrance
Technology journalist

Linda Rosencrance is a freelance writer and editor based in the Boston area, with expertise ranging from AI and machine learning to cybersecurity and DevOps. She has been covering IT topics since 1999 as an investigative reporter working for several newspapers in the Boston metro area. Before joining Techopedia in 2022, her articles have appeared in TechTarget, MSDynamicsworld.com, TechBeacon, IoT World Today, Computerworld, CIO magazine, and many other publications. She also writes white papers, case studies, ebooks, and blog posts for many corporate clients, interviewing key players, including CIOs, CISOs, and other C-suite execs.