Edge Computing for Smart Grids

Detailed overview of innovation with sample startups and prominent university research

What it is

Edge computing in the context of smart grids involves processing and analyzing data closer to the source of data collection, such as at substations, transformers, or smart meters, rather than sending all data to a central cloud or data center. This approach reduces latency, improves response times, and enhances the efficiency and reliability of grid operations.

Impact on climate action

Edge Computing for Smart Grids in the Smart Grids domain revolutionizes climate action by enhancing grid efficiency and reliability. By processing data closer to the source, this innovation enables real-time decision-making, reduces energy waste, and accelerates the integration of renewable energy sources, fostering a more resilient and sustainable energy infrastructure.


  • Edge Devices: Edge devices, such as gateways, routers, and intelligent electronic devices (IEDs), are equipped with processing and storage capabilities, allowing them to perform data analysis and decision-making at the edge of the network.
  • Distributed Computing: Edge computing distributes computing power throughout the grid, reducing reliance on a central data center and improving resilience.
  • Data Analytics and AI: AI and machine learning algorithms can be deployed on edge devices to analyze data in real-time, enabling faster and more efficient decision-making.
  • Communication Networks: Edge computing requires reliable and secure communication networks to transmit data between edge devices and the central control system.

TRL : 6-7

Prominent Innovation themes

  • High-Performance Edge Devices: Advancements in hardware and software are leading to the development of more powerful and efficient edge devices that can handle complex data analytics and AI workloads.
  • Edge AI and Machine Learning: AI and ML algorithms are being optimized for edge computing environments, enabling real-time data analysis and decision-making at the edge of the grid.
  • Edge-Cloud Integration: Hybrid edge-cloud architectures are being developed to combine the benefits of edge computing with the scalability and flexibility of cloud computing.
  • Security for Edge Computing: Cybersecurity solutions are being developed specifically for edge computing environments to protect against cyberattacks and ensure data privacy.

Other Innovation Subthemes

  • Edge Device Optimization
  • Distributed Computing Infrastructure
  • Real-time Data Analytics
  • AI and Machine Learning Integration
  • Secure Communication Networks
  • Hybrid Edge-Cloud Architectures
  • Cybersecurity Solutions for Edge
  • Advancements in Edge Hardware
  • Edge AI Algorithm Development
  • Scalable Edge-Cloud Integration
  • Resilient Edge Infrastructure
  • Edge Data Privacy Solutions
  • Industrial IoT Edge Platforms
  • Real-time Edge Data Processing
  • Edge Computing for Grid Optimization
  • Edge Computing for Fault Detection
  • Demand Response with Edge Computing
  • Distributed Energy Resource Management

Sample Global Startups and Companies

  1. FogHorn:
    • Technology Enhancement: FogHorn specializes in edge computing software for industrial IoT (IIoT) and smart grid applications. Their Edge AI platform enables real-time data processing, analytics, and machine learning at the edge of the network, allowing smart grid devices and sensors to perform advanced analytics and decision-making locally.
    • Uniqueness of the Startup: FogHorn stands out for its focus on enabling edge intelligence in distributed energy systems, including smart grids. Their platform provides a lightweight, scalable, and secure solution for deploying AI and analytics at the edge, optimizing grid operations, and enhancing grid reliability and resilience.
    • End-User Segments Addressing: FogHorn serves utilities, grid operators, and energy companies seeking to leverage edge computing for smart grid applications. Their software platform is deployed in smart substations, distribution automation systems, renewable energy assets, and grid-edge devices, enabling real-time monitoring, control, and optimization of grid operations.
  2. SWIM.AI:
    • Technology Enhancement: SWIM.AI offers edge intelligence software for real-time data processing and analytics in distributed systems, including smart grids. Their platform leverages edge computing and machine learning techniques to analyze streaming data from sensors and devices at the edge of the network, providing insights and predictions for grid optimization and control.
    • Uniqueness of the Startup: SWIM.AI stands out for its approach to distributed edge intelligence, where data processing and analytics are performed directly on edge devices without the need for centralized servers or cloud infrastructure. Their platform enables autonomous decision-making and anomaly detection at the edge, improving grid efficiency and reliability.
    • End-User Segments Addressing: SWIM.AI serves utilities, energy companies, and smart grid solution providers looking to enhance grid intelligence and responsiveness. Their edge computing software is deployed in smart meters, distribution automation systems, grid sensors, and renewable energy assets, enabling real-time monitoring and control of grid operations.
  3. ClearBlade:
    • Technology Enhancement: ClearBlade provides an edge computing platform for building and deploying IoT solutions, including smart grid applications. Their platform enables edge data processing, orchestration, and integration with cloud services, allowing smart grid devices and sensors to communicate, analyze data, and respond to events in real-time.
    • Uniqueness of the Startup: ClearBlade stands out for its comprehensive edge computing platform that supports edge-to-cloud connectivity, data management, and application development for smart grid deployments. Their platform offers flexibility, scalability, and security for deploying edge intelligence solutions in diverse grid environments.
    • End-User Segments Addressing: ClearBlade serves utilities, grid operators, and energy companies seeking to modernize grid infrastructure and enable distributed intelligence. Their edge computing platform is utilized in smart meters, substations, grid-edge devices, and renewable energy systems, facilitating real-time monitoring, control, and optimization of grid operations.

Sample Research At Top-Tier Universities

  1. Massachusetts Institute of Technology (MIT):
    • Research Focus: MIT is a pioneer in research on Edge Computing for Smart Grids, focusing on developing advanced computing architectures, communication protocols, and data analytics techniques to enable real-time decision-making and control in decentralized energy systems.
    • Uniqueness: Their research involves designing edge computing platforms that integrate distributed sensors, actuators, and intelligent devices deployed throughout the grid infrastructure. These platforms leverage machine learning algorithms, predictive analytics, and optimization models to optimize energy distribution, manage grid stability, and enhance system reliability.
    • End-use Applications: The outcomes of their work find applications in distribution grid management, demand response, and renewable energy integration. By deploying edge computing solutions, MIT’s research enables utilities, grid operators, and energy consumers to enhance grid resilience, reduce energy costs, and accelerate the transition to a more sustainable and flexible energy infrastructure.
  2. Stanford University:
    • Research Focus: Stanford University conducts innovative research on Edge Computing for Smart Grids, leveraging its expertise in computer science, electrical engineering, and power systems to develop scalable and resilient edge computing solutions for next-generation grid control and optimization.
    • Uniqueness: Their research encompasses the development of edge computing frameworks that enable real-time processing, analysis, and decision-making at the network edge. They also investigate the integration of edge devices with renewable energy resources, energy storage systems, and electric vehicle charging infrastructure to support dynamic load management and grid balancing.
    • End-use Applications: The outcomes of their work have applications in microgrid control, grid-edge intelligence, and energy IoT (Internet of Things) applications. By harnessing edge computing for smart grid operations, Stanford’s research facilitates autonomous grid management, distributed energy resource coordination, and enhanced grid resiliency in the face of evolving energy challenges.
  3. Carnegie Mellon University (CMU):
    • Research Focus: CMU is engaged in cutting-edge research on Edge Computing for Smart Grids, leveraging its expertise in cybersecurity, networked systems, and energy management to develop secure and resilient edge computing solutions for critical infrastructure protection and grid modernization.
    • Uniqueness: Their research involves developing edge computing architectures that prioritize data privacy, integrity, and availability in smart grid deployments. They also explore the use of blockchain technology, secure multiparty computation, and federated learning techniques to ensure trustworthiness and transparency in edge computing operations.
    • End-use Applications: The outcomes of their work find applications in grid-edge cybersecurity, distributed control systems, and resilient energy infrastructure. By addressing cybersecurity challenges and privacy concerns in edge computing, CMU’s research enables utilities and grid operators to safeguard critical infrastructure, prevent cyber attacks, and ensure the secure operation of smart grid technologies.

commercial_img Commercial Implementation

Edge computing is being increasingly adopted in smart grid deployments, particularly for applications that require real-time data analysis and decision-making, such as fault detection and isolation, demand response, and distributed energy resource management.