Technical advancements such as building-integrated renewable systems and autonomous vehicles (AVs) require existing electricity grids to be transformed into adaptable microgrids considering future energy supply and demand. Interactions between energy demanders and suppliers should be predicted to quickly respond and adapt to future demands or uncertain events. However, communities consisting of buildings, transports, and people are complex and their interactions are difficult to be recognized. This research investigates the interactions among elements in communities featuring AVs and solar photovoltaics (PV)-integrated buildings. A framework for establishing smart energy networks that can be adjustable spatially and temporally is developed by communicating buildings and vehicles. The methodology is tested in buildings and road networks in Kyojima 1, North Sumida, Tokyo, Japan. Existing energy demands and transportation systems are reviewed to establish future scenarios for applying AVs and solar PV buildings. The scenarios are analyzed using community clustering and Voronoi diagram algorithms, in which sustainable community boundaries are examined using prediction data from parametric energy modeling and spatial network analysis. The framework can support decision-making for future community boundaries, thereby distributing energy securely and efficiently. This research will also contribute to preparing communities to share energy information and to achieve resilient and sustainable infrastructure systems.