Edge Offloading in Smart Grid

Abstract

The management of decentralized energy resources and smart grids needs novel data-driven low-latency applications and services to improve resilience and responsiveness and ensure closer to real-time control. However, the large-scale integration of Internet of Things (IoT) devices has led to the generation of significant amounts of data at the edge of the grid, posing challenges for the traditional cloud-based smart-grid architectures to meet the stringent latency and response time requirements of emerging applications. In this paper, we delve into the energy grid and computational distribution architectures, including edge–fog–cloud models, computational orchestration, and smart-grid frameworks to support the design and offloading of grid applications across the computational continuum. Key factors influencing the offloading process, such as network performance, data and Artificial Intelligence (AI) processes, computational requirements, application-specific factors, and energy efficiency, are analyzed considering the smart-grid operational requirements. We conduct a comprehensive overview of the current research landscape to support decision-making regarding offloading strategies from cloud to fog or edge. The focus is on metaheuristics for identifying near-optimal solutions and reinforcement learning for adaptively optimizing the process. A macro perspective on determining when and what to offload in the smart grid is provided for the next-generation AI applications, offering an overview of the features and trade-offs for selecting between federated learning and edge AI solutions. Finally, the work contributes to a comprehensive understanding of edge offloading in smart grids, providing a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to support cost–benefit analysis in decision-making regarding offloading strategies.

Whale Optimization for Cloud–Edge-Offloading Decision-Making for Smart Grid Services

“…It is facilitated by recent advances in edge computing and IoT technologies that have further expanded and decentralized the cloud paradigm with edge and fog resources to ensure the integration of computational capabilities closer to data sources [5]. Edge nodes with enough processing capacity are deployed to analyze energy IoT data and enable quicker decision-making processes to enhance the optimization of various collaborative and decentralized subsystems, allowing for efficient local energy management that contributes to the central grid objectives [6].…”

“…Moreover, it enhances scalability, enabling distributed computing across interconnected devices and fostering dynamic and responsive systems, a key requirement for accommodating the evolving needs of smart grid operations. Thus, edge computing can play a significant role by reducing operational costs associated with cloud usage and optimizing resource allocation while minimizing data transfer overheads, which can make the smart grid decentralization and energy transition more cost-efficient and sustainable in the long run [6,9].…”