An Agile Mobilizing Framework for V2G-Enabled Electric Vehicles Under Wildfire Risk

Public safety power shut-offs (PSPSs), implemented by California utilities during high wildfire risk periods, lead to the de-energization of grid sections and leave certain customers out of power for several hours and even days. We propose an agile decision support system (DSS) to mitigate these impacts by harnessing electric vehicles (EVs) as mobile energy sources serving the multiple microgrids (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>Gs) formed within affected communities. Given that not all <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>Gs possess adequate energy storage and distributed energy resources (DERs), we advocate for the mobilization of vehicle-to-grid (V2G)-enabled EVs for equitable and resilient energy access. Our emergency service relocation (ESR) model incentivizes EV owners to transport stored energy between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>Gs. However, traditional DSS cannot promptly solve the associated mixed-integer programming (MIP) problem, necessitating a faster solution algorithm for rapid EV deployment under emergency conditions. We introduce a learning framework employing graph convolutional networks (GCNs) that significantly expedites the MIP problem's solution by predicting binary values. Our results demonstrate the effectiveness of the proposed framework in promoting grid resilience and considerably reducing solve time when the problem has 69k binary decision variables.