Why 80% of Power Generation Runs at the Edge

• ABB reports 80% of power generation deployments run on-premise at edge
• Battery systems require 150-millisecond response times for grid compliance
• Latency, security, and autonomy drive edge computing adoption in energy
• Cloud computing cannot meet real-time control demands for critical infrastructure

Cody Falcon, Global Digital Products and Technology Leader for ABB’s Energy Industries division, revealed at IIoT World Energy Day 2026 that close to 80% of power generation deployments in ABB’s global electrification and power install base run on-premise at the edge. This statistic reflects the current operational reality of how utilities and power generators manage critical infrastructure, driven by latency requirements that cloud computing architectures cannot satisfy.

For plant managers and control engineers, this means understanding why edge computing has become the default architecture for power generation control systems—and what technical constraints make centralized cloud alternatives impractical for real-time grid operations.

Why Do Power Systems Require Edge Computing?

Battery systems must respond within 150 milliseconds to meet strict grid requirements, a constraint that makes cloud-based control architectures technically unfeasible. Industrial edge computing refers to local processing, analysis, and pre-processing of data in close physical proximity to machines, production equipment, or industrial processes—typically achieving response times below 10 ms for time-critical applications. Power generation systems face similar demands across multiple asset classes.

Edge controllers directly connect sensors and actuators, completing data processing and instruction issuance locally with latency controlled within 10ms, meeting stringent requirements for scenarios such as grid secondary frequency regulation and microgrid black starts. Edge computing can improve the power grid’s resiliency by enabling autonomous decision-making at the edge in case of network disruptions—if a substation loses connectivity with the central control system, edge servers can switch to a backup mode and continue to operate autonomously using data stored locally.

By processing data locally, response times are nearly instantaneous—often measured in milliseconds—which is critical for an Energy Storage System (ESS) as it allows the system to react immediately to grid fluctuations, disconnect during a fault to protect sensitive equipment, or optimize battery charging based on real-time conditions. Traditional cloud architectures introduce round-trip latency that exceeds acceptable thresholds for grid-critical control functions.

What Security and Reliability Advantages Drive Edge Adoption?

Industrial Edge Computing offers much tighter control over the security of sensitive data by keeping it stored and processed locally—a feature especially valuable in sectors such as advanced manufacturing, energy or critical infrastructure, where maintaining data privacy and security is absolutely essential. Recent incidents underscore these risks. A cyberattack on Poland’s energy grid nearly crippled power in part of the country during a very cold period, with hackers initially breaching the system through vulnerable internet-facing edge devices before deploying wiper malware that damaged operational technology.

Increasingly distributed networked grid assets present a broader attack surface for adversaries to exploit. However, thanks to the ability to operate locally without the need for constant connection to cloud services, Edge solutions provide greater autonomy and operational robustness—especially critical for industries operating in remote locations or environments with limited connectivity. This architectural decision reduces exposure to network-based attacks while maintaining operational continuity during communication failures.

Placing enterprise class compute and control systems near the sources of data affords many benefits such as faster response time, higher reliability, and reduced bandwidth requirements. For utilities managing geographically distributed assets, this translates to continued operation even when wide-area network connectivity fails—a critical capability for maintaining grid stability.

What Implementation Challenges Do Power Operators Face?

A single site can have thousands of battery containers, and for each individual cell inside, operators need state of charge and state of health readings every one to 10 seconds, or even faster during charge and discharge cycles—the resulting data volume is enormous, making battery storage the most data-intensive asset class on the grid. Battery energy storage systems are designed to run for 20 to 25 years, and over that span, devices will break—in 10 or 15 years, operators may not be able to procure the exact same device, and even if they can, it will not have the same firmware, creating a compounding data challenge.

Many utilities have substantial investments in existing infrastructure that cannot be replaced all at once—successful implementation requires careful planning to enable new edge-enabled devices to work alongside traditional equipment during the transition period. This integration challenge means operators must maintain compatibility with legacy systems while deploying modern edge computing architectures.

One ABB customer spent five years building a hundred-million-dollar enterprise data lake, but when ABB identified their primary problem—heat exchanger fouling at offshore facilities—the solution required only five of 28 available tags rather than a 400,000-tag real-time system. This example illustrates the importance of problem-definition before infrastructure investment.

Article Source: Why 80% of Power Generation Runs at the Edge