Electric Boats and Electric Vehicles Data-Driven Approach for Enhanced Resilience in Power Distribution Networks

This paper proposes a novel hierarchical optimization framework to enhance power distribution network resilience against extreme events by incorporating electric boats (E-Boats) as complementary assets to electric vehicles (EVs). Unlike EVs reliant on potentially compromised roadways, E-Boats navigate unaffected waterways, offering superior resilience during disaster response in regions with rivers and canals. The approach employs a two-phase methodology evaluated on a modified IEEE 123-node test feeder with actual infrastructure data, enabling large-scale integration of non-pre-determined EVs/E-Boats. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Phase-I</i> emphasizes proactive planning and anticipatory optimization, leveraging extensive data integration and a developed exchange architecture enabling efficient EV/E-Boat distribution through novel selection and pairing algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Phase-II</i> focuses on adaptive restoration and coordination optimization by employing sophisticated model layers. These include advanced EV and E-Boat dispatch models that use changing geospatial data to approximate motion conditions and charging/discharging requirements, enabling energy-efficient and optimized dispatch. Detailed simulations with comprehensive performance analysis demonstrate the proposed algorithm’s superiority in achieving optimal solutions within shorter computational times. This work presents an effective solution for enhancing power network resilience using a combined EV/E-Boat network with geospatial data for EV and E-Boat locations, state-of-charge updates, and adaptive path optimization algorithms. Results highlight faster restoration, minimized costs, and reduced energy consumption.