<p>Robust decision-making analysis (RDM) can be applied to many planning problems that are characterized by deep uncertainties, which are risks that cannot be quantified in a consensual way. In this paper we extend the classic RDM methodology by integrating it with power system optimization models and modeling dynamic adaptation to changing conditions. We then develop an adaptive strategy for power system expansion in Bangladesh over the next decade and consider how uncertain climatic, technical, and policy factors might affect power generation capacity decisions. 200 future states are simulated using Latin Hypercube method to reflect various uncertainties and are then integrated into an optimization model of the Bangladesh power system. Instead of defining alternative strategies ahead of time as in the classic RDM approach, we use the optimization results to identify strategies as mixes of capacity of different technologies and fuel types in 2030. We show how RDM can be used to inform planners and policy makers about which strategies appear most robust and their vulnerability to future conditions. This understanding is then used to develop an ‘adaptive strategy’ that combines the most robust strategy and features tailored to the future possible vulnerabilities, achieving an acceptable performance in over 85% of the future states.</p>