Predictive and real-time congestion management to enhance grid hosting capacity

The article explores the confluence of congestion issues in distribution grids, flexibility as a solution, and the enabling role of cloud and edge computing. Implementing such a solution effectively requires the integration of expertise from both electrical engineering and information technology, emphasizing the need for a structured approach to development and knowledge sharing among developers. The paper proposes following the Smart Grid Architecture Model to develop business use cases and system use cases, both formatted using the IEC 62559-2 standard template. Business use cases address the ‘what’ and ‘why,’ while system use cases focus on answering the ‘how.’ Recognizing that the placement of algorithms impacts response time, bandwidth costs, data security, and privacy, the article suggests placing predictive congestion management algorithms in the cloud and real-time congestion management algorithms at the edge. Predictive congestion management, hosted in the cloud, can participate in the flexibility market (also cloud-based) and leverage extended time and computational resources for optimization-based processes. Conversely, lightweight, rule-based algorithms deployed at the edge address immediate, localized needs quickly and efficiently.