The field of sustainable energy transition is moving towards a more integrated and holistic approach, considering technical, economic, environmental, and social dimensions. Researchers are developing comprehensive frameworks and models to evaluate energy transition pathways, optimize power grid topologies, and enhance the integration of renewable energy sources. The use of reinforcement learning, control co-design, and tri-level optimization approaches are emerging as promising methods to improve decision-making in dynamic and uncertain environments. Noteworthy papers include: Optimizing Power Grid Topologies with Reinforcement Learning, which provides a comprehensive survey of RL applications for power grid topology optimization. Control Co-Design Under Uncertainty for Offshore Wind Farms, which proposes a control co-design approach to optimize design and control decisions for integrating offshore wind farms into the power grid. Optimizing Utility-Scale Solar Siting for Local Economic Benefits and Regional Decarbonization, which develops a power system planning model to evaluate how economic impacts influence solar siting decisions.