Next-Generation Automated Adaptive Protection Enabled by Geospatial Load Forecasting in Distribution Networks

Modern distribution networks increasingly face operational stress from variable demand and high penetration of distributed energy resources, challenging the adequacy of purely reactive protection schemes. This study addresses this challenge by enhancing a developed adaptive protection software platform with a Geographic Information System (GIS) driven predictive load forecasting capability to enable anticipatory protection coordination. The proposed framework integrates spatially resolved demand modeling, regulatory and planning constraints, and machine learning-based short- to medium-term load forecasting with a relay coordination and optimization engine. Forecasted load profiles are used as inputs to an optimization layer that proactively updates relay pickup and time delay settings to maintain selectivity and system security under predicted operating conditions. The approach is validated at laboratory scale using real Intelligent Electronic Devices (IEDs) interfaced with synthetic GIS-based network and load datasets. Experimental results indicate that incorporating forecast-informed settings improves coordination margins and reduces the risk of relay maloperation compared with reactive adaptive protection alone. The findings demonstrate that coupling GIS based constrained load forecasting with adaptive relay control can enhance protection performance in active distribution networks, supporting more resilient and forward-looking protection strategies.