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How edge-to-cloud computing powers smart grids and smart cities

A smart city, built on a smart grid, demonstrates how different functions are best run at the edge, in the cloud, or on premises.

When Category 4 Hurricane Ike knocked out Houston's entire power grid in 2008, CenterPoint Energy, a provider of electrical power to 2.4 million customers in the Houston area, saw the event as a wake-up call. Namely, it was spurred into action to transform its service into an internet of things network steered by intelligent meters and IoT sensors around the power grid. The new smart grid system that resulted allows for automatic insight into power delivery patterns, service, disruption, infrastructure damage, security breaches, and other information.

Smart grids use cloud-based computing to enable multidirectional movement of both electricity and data, detecting and reacting to changes in supply, demand, and functioning in real time. Such networks have game-changing capabilities, such as the ability to automatically prepare for and recover from disasters, address high-volume service calls digitally, and constantly monitor the power network for issues.

The ability to target where energy is needed, including making changes in real time when disruptions occur, offers a sharp contrast to traditional methods of energy provisioning. "Historically, the energy market has been reactive rather than proactive," says Amy Cameron, principal analyst at STL Partners, a telecommunications consultancy. "They always plan for oversupply, which is not cost or energy efficient."

How smart cities use edge computing

As cities and municipalities move toward instituting smart systems to optimize infrastructure functions and services across the city landscape, they must confront the question of how to do so efficiently and securely.

Please read: Where are all those smart cities we were promised?

In many cases, their efforts are falling short, as these systems have been instituted in a patchwork way, with data siloed and use cases unable to inform one another. To create a truly smart city, municipal leaders must look at how to integrate all data into a single, integrated system that uses a combination of cloud, edge, and on-premises computing to function optimally and safely.

Understanding the ever-shifting availability of energy and location of demand requires a lot of processing capability. In advanced systems, algorithms assess how and when to distribute energy, including which systems and customers are the priorities to receive supply and where power can be held back if necessary. All of that analysis requires compute power, both to store and analyze data and to run the applications that keep the grid functioning.

Running such systems via the cloud has limitations, particularly when it comes to security and efficiency. Certain sectors, such as utilities and government, may not want systems to be located in the public cloud. Another challenge is that to track and predict real-time supply and usage, the smart grid ingests vast amounts of data using sensors. Moving this data to the cloud to process is expensive and inefficient, increasing the potential for latency.

"Sending all that data in a centralized server and running the analysis there and sending the results back is costly and takes time," says Cameron.

Edge-to-cloud computing is a powerful alternative, as it brings the data and the processing location closer together to ensure rapid processing at reasonable cost while eliminating key security risks to maintain a safe energy supply.

The same principle applies to other smart systems cities may have implemented, such as traffic monitoring, waste handling, and public transit management.

"Whenever use cases become bandwidth-sensitive and latency-sensitive, that is when you make heavy use of the edge," says Nitin Agarwal, worldwide lead for smart cities at Hewlett Packard Enterprise.

Please read: The future of analytics is real time

Agarwal provides the example of video-based applications, such as those that analyze traffic and make real-time changes in response. The system must have sufficient bandwidth to process the video, and the real-time decisions involved in the system mean it's also latency-sensitive. As such, sending the video to the cloud and back for analysis is simply too inefficient.

Another example is connected vehicles, an area where cities are innovating aggressively. The real-time nature of such a system means it's imperative to reduce the risk of latency, again making edge computing an important solution.

"Edge computing could play a big role in supporting connected vehicle infrastructure," says Cameron. "Connected vehicles processing information in real time is the third-largest use case that we've forecasted."

Security is another factor that motivates city leaders to consider looking for alternatives to the public cloud. Some data monitored or handled by smart systems is best kept somewhere more secure, making on-prem or edge computing an appealing option.

"In any city, confidential data is on prem, while the data that is not that critical goes to the cloud," says Lin Nease, chief technologist for IoT at HPE.

The edge, smart cities, and integrated data

Looking at the functioning of a smart grid inevitably leads to discussion of how that energy infrastructure fits into the various other high-tech systems that cities are developing. Agarwal and Nease point out that the future of smart city innovation lies in finding ways to integrate data across a variety of smart systems, including smart grids, to create a unified, intelligent network.

"The real value in smart cities comes from integrating the data," says Nease. "It's how data comes together that's the value proposition in the end."

However, in many cases, cities have data in silos and systems that don't connect with one another, vastly limiting the potential of their smart systems. They may implement smart electricity meters using one system and then orchestrate fleet management with a separate system so that the two are not able to share data and inform each other.

Please read: Smart cities: What it takes to build one

"Everybody understands that IoT has potential, but customers do not know how or where to start and how to do it," says Agarwal. "To quickly realize value from AI and IoT, customers are taking a siloed approach. Then, they can't aggregate data from multiple use cases that departments are deploying over time so the real potential of AI is missed."

To capture the true value of AI, sensors that exist in various systems—for example, smart sensors for traffic and smart meters for energy—should be treated as connected objects that are part of a holistic smart city approach, employing a hybrid data processing model that combines cloud, edge, and on-prem computing.

"The idea should be that all these use cases should not be treated as systems in siloed environment," says Agarwal. "They should all be sending data on a single, central layer that comprises all of the city's IoT devices and edge services, and on which the data can be normalized, aggregated, and analyzed."

The smart grid is not a stand-alone entity but part of a larger networked whole, the pieces of which connect and inform one another—and the computing for which must be handled in a hybrid arrangement of resources that makes heavy use of the edge to ensure efficiency, speed, and security.

This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.