Distributionally Robust Management of Hybrid Energy Station Under Exogenous-Endogenous Uncertainties and Bounded Rationality

Hybrid energy station (HES) systems coupling diverse energy sectors can facilitate the low-carbon and sustainable transition by integrating massive wind-solar power and energy conversion technologies. With the proliferation of renewable and flexible demand response (DR), different uncertainties are inevitably introduced into energy systems because of external volatile surroundings and internal consumers' mutable behavior. Moreover, the probability distribution function (PDF) of these uncertainties may be unavailable or ambiguous and even affected by decisions. In this work, we propose a hierarchical optimal framework for HES systems, in which the energy hub plant determines the schedules of generation and trade in different markets (upper-level) and users minimize energy expense by managing facilities operations considering decision-dependent uncertainties (lower-level). A scheme of energy transaction options is also provided under the bounded rationality of user sides. To tackle problems with uncertain PDFs, a tailored chance-constrained distributionally robust method is implemented. In the process, the correlation DR operation pattern and analytic pricing formulation are derived incorporating the property of users. Case studies with comparison tests validate the efficacy of the proposed model in handling uncertainties and pursuing economic profits.