Distributed Intelligence: Why Computing at Grid's Edge Adds Up

The power grid is changing fast. More renewable energy, electric vehicles, and the need for better resilience are driving a shift to the smart grid. This uses advanced tech like sensors, data analysis and control to make the grid more responsive and efficient. But the huge amount of data from smart grid devices is hard to manage. This is where edge computing comes in – it allows localized computing, storage and analytics at the source.

What is Edge Computing?

Edge computing means processing power, data storage, and apps are located near the data source – like in sensors, devices and equipment. This is different to old-school cloud computing where everything is centralized. The big benefits of edge computing are less network bandwidth, lower latency, and better reliability. For the smart grid, edge resources can go in substations, solar panels, EV charge points and more.

Why Edge Computing Now?

A few factors are pushing edge computing into smart grids. Firstly, the amount of data being produced is massive. Advanced meters, grid sensors and smart devices generate terabytes daily. Sending this all centrally is impossible. Edge computing helps by enabling local number crunching and storage.

Secondly, many grid applications are time-critical and can't tolerate delays. For example, automated fault finding and power restoration need rapid data analysis. Edge computing gives low latency through localized processing. Likewise, voltage monitoring is better with faster edge-enabled responses.

Also, centralized cloud infrastructure introduces weaknesses, which matters for critical systems like the grid. Edge computing promotes resilience through distributed computing power. Networks of edge nodes can keep operating if central connections go down.

How can it be implemented?

There are a few ways to build edge computing into smart grids. Many new assets already support embedded edge servers. For older equipment, external gateways can be added to provide edge capabilities. The gateways collect data from legacy devices and handle edge computing functions.

Microgrids are perfect for leveraging edge computing. Resources like solar inverters, batteries and EV chargers can network into a local microgrid edge system. This lets microgrids operate independently from the wider grid if needed. Critical loads stay powered even if upstream lines fail.

For utility-scale edge computing, compact data centres can be installed at substations – putting compute at the heart of distribution grids. Utilities get substation data without long-distance communication. Latency-sensitive monitoring and protection applications can run reliably on these localized data centres.

At the transmission level, edge computing aligns with deploying phasor measurement units (PMUs). PMUs provide high-fidelity monitoring by measuring grid conditions in sync across the network. Doing PMU analytics at the edge reduces communication bandwidth needs versus transmitting huge streams of phasor data to the cloud. This edge approach is crucial for real-time situational awareness and stability assessments.

Key Uses

Edge computing enables capabilities that are either impossible or less effective with conventional cloud architectures. Here are some key smart grid applications:

• Renewable forecasting
  Short-term solar and wind forecasting improves by leveraging on-site edge computing at plants. This allows faster updates using local measurements.
• EV charging optimization
  Managing large EV fleets needs low-latency control. Local edge computing at charge points enables real-time charge management based on grid conditions.
• Microgrid management
  Islanding decisions, balancing loads, and controlling distributed energy in microgrids benefit from prompt edge computing.
• Substation automation
  Protection relays and smart devices get accelerated fault detection and diagnosis when edge servers are on-site.
• PMU-based monitoring
  Phasor data analytics improves through edge computing compared to sending huge PMU data streams to the cloud.
• Volt-VAR optimization
  Edge computing enables continuous analysis of conservation voltage reduction and volt-var optimization using real-time load knowledge.
• Distributed energy aggregation
  Collectively optimizing behind-the-meter distributed energy relies on edge computing for local data sharing, coordination and control.

Traditional AMI Solutions and Edge Computing

Old-school smart meter systems usually don't use edge computing. They're built for centralized data gathering and number crunching. Smart meters talk to a central AMI headend for things like usage reports, outage alerts and remote readings. While smart meters could do advanced stuff with edge computing, that's not common yet.

On the flip side, our energy storage solutions for microgrids already use edge computing. These systems independently control charging and discharging, using local data to make real-time decisions. This boosts microgrid resilience and efficiency, showing the practical perks of edge intelligence.

Takeaway

Edge computing is vital for realizing the full smart grid vision. It tackles challenges around cost, speed, resilience and scalability. Grasping and getting ready for the shift to edge computing will be essential for utilities, regulators, vendors, and other stakeholders who want to modernize grid operations.

If you have any inquiries or need further information about our microgrid and energy storage solutions, please do not hesitate to reach outContact Us to us. We are here to assist you and welcome your valuable thoughts and comments.

Until then, keep shining bright like a solar panel on a sunny day!