<p>The optimization of renewable energy systems (RES), in particular, the concentrated solar power plant (CSP) integrated with thermal energy storage (TES), is a challenging task considering the inherent uncertainties in solar radiation and variable electricity demand. The transition toward sustainable energy systems requires advanced methods to optimize solar power integration under uncertainty. Most conventional dynamic economic dispatch (DED) methodologies often deal very poorly with these uncertainties, generally giving suboptimal system design and operations. This limitation motivates the use of possibilistic optimization to better account for the imprecise nature of RES variables. The paper presents a new framework that embeds possibilistic optimization in the DED model for enhancing resilience and adaptiveness while handling the incorporated uncertainty. A mixed-binary linear programming (MBLP) formulation is used, integrating possibility and necessity measures into the DED model. This research covers seven different necessity and possibility levels, representing a complete decision-making tool to select optimal system configurations in the presence of certain degrees of uncertainty in solar radiation, electricity demand, and interest rate. We hypothesize that possibilistic programming enables more robust and flexible decision-making than conventional deterministic approaches under uncertainty. Simulation results using the IEEE 39-bus system reveal that increasing necessity levels lead to reduced profits but improved reliability and investment conservatism. These findings underline the fact that incorporating short-term operational constraints with the long-term investment considerations will equip the decision-makers with the capability for choosing the most suitable degree of necessity and possibility that can result in achieving the desired economic and grid stability outcomes. Hence, possibilistic optimization proves to be a viable approach for robust economic planning in uncertain RES scenarios.</p>