Edge computing and smart grids — the perfect combination?

At a high level, the two pillars of decarbonization are optimized energy usage and widespread clean energy adoption. However, the inconsistent nature of clean energy sources and the current limitations of energy storage technologies make energy optimization difficult to achieve at scale.

Fortunately, smart grids have emerged to alleviate some of these decarbonization challenges. Smart grids are systems that rely on data from smart meters and analytics to improve decision-making and efficiently deliver electricity. This technology provides power and grid companies a whole host of benefits, including reduced energy waste and significant cost savings. In fact, 60% of end users in the U.S. have smart meters, a number that is projected to increase to 81% by 2024.

However, the industry has yet to see smart grids adopted universally. Because smart grids rely so heavily on data and analytics, they require significant computing power — something that is both energy-intensive and impacts latency.

To improve the efficacy of smart grids and reduce their impact on the environment, there is a better solution for satisfying their extensive computing needs — edge computing. Edge computing solves many of the challenges faced when using the cloud by bringing computing closer to the source of data and its users. The list of benefits associated with edge computing is long, but when it comes to smart grids, operators can expect lower latency, improved data privacy, and enhanced forecasting. With these benefits, organizations can quickly work toward making progress on sustainability goals.

Guaranteed low latency

Although powerful, smart grids functioning through the cloud are often limited by issues with latency. Rather than providing operators with real-time information, data is delayed as it is processed through the cloud. Alternatively, an edge-enabled smart grid has the power to rapidly react to fluctuations in usage. For example, an electricity company can quickly respond to a sudden drop in temperature by pulling data from thousands of sensors to understand how it affected electricity usage.

Through real-time grid frequency monitoring, operators have the foresight to make decisions ahead of time to alleviate power factor penalties and reduce energy waste. This type of agility is necessary in a time when conserving resources is top-of-mind in every industry.

Increased data privacy

In recent years, data privacy has become a growing concern as more consumers and organizations interact with IoT devices, which can collect sensitive information. While convenient, these devices can expose user data to malicious entities when processed through the cloud, which is often outside of the region where the data originates.

Fortunately, this can be avoided with the use of the edge, allowing providers to process data locally and keep private information from going through the cloud. The privacy benefits associated with edge computing also serve another purpose — the peace of mind will ultimately increase the uptake of edge computing, which is a greener alternative to cloud computing.

Better forecasting

Consumers and enterprises alike can optimize their energy usage and improve savings when they understand their own consumption patterns. However, understanding these patterns is easier said than done. Energy providers and power companies are working with an enormous amount of data that must be filtered and processed to develop an idea of how users are consuming electricity. With the cloud, providers struggle to capture and analyze the data needed to create models that can predict usage patterns.

In these circumstances, energy providers can replace cloud computing with edge computing, which paves the way for the generation of accurate usage models and forecasting abilities. By processing data locally at the edge, it does not need to travel across large distances to the cloud and back, which is resource-intensive.

Most importantly, this allows for more data to be collected, resulting in precise models. These models can help providers adjust the grid for periods of both high and low usage, which conserves electricity and lets operators switch to renewable energy sources during times of low usage, creating a more sustainable energy industry. For example, a smart grid that has precise models on how consumer electricity usage changes in the winter can help operators fine-tune the complicated balancing act between traditional energy sources and renewables according to the time of year.

Edge computing and smart grids: the perfect combination?

It’s imperative that companies work to integrate technologies that are more efficient, sustainable, and cost-effective as global emissions continue to rise. The energy industry is due for a transformation, with new, green solutions emerging every single day. Combined, smart grids and edge computing are two solutions that stand to resolve many issues that have persisted within the utilities industry. Through reduced latency, better data privacy, and improved forecasting, edge computing will enhance smart grids and give rise to an industry that focuses on sustainability.

Eli Daccach

Eli Daccach,global business development leader for the secure power industrial segment at Schneider Electric.