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The deepening of the penetration of renewable energy is challenging how powersystem operators cope with their associated variability and uncertainty. Theinherent flexibility of dispathchable assets present in power systems, which isoften ill-characterized, is essential in addressing this challenge. Severalproposals for explicit flexibility characterization focus on defining afeasible region that secures operations either in generation or uncertaintyspaces. The main drawback of these approaches is the difficulty in visualizingthis feasibility region when there are multiple uncertain parameters. Moreover,these approaches focus on system operational constraints and often neglect theimpact of inherent couplings (e.g., spatial correlation) of renewablegeneration and demand variability. To address these challenges, we propose anovel data-driven inverse optimization framework for flexibilitycharacterization of power systems in the demand space along with its geometricintuition. The approach captures the spatial correlation of multi-siterenewable generation and load using polyhedral uncertainty sets. Moreover, theframework projects the uncertainty on the feasibility region of power systemsin the demand space, which are also called loadability sets. The proposedinverse optimization scheme, recast as a linear optimization problem, is usedto infer system flexibility adequacy from loadability sets.

Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach