Capacity of Virtual Energy Storage System for Frequency Regulation Services via a Data-Driven Distributionally Robust Optimization Method

Due to large thermal inertia of buildings and flexibility of interruptible loads, smart buildings pose a remarkable potential for developing virtual energy storage systems (VESSs). However, current literature lacks advanced models to quantify and thus properly optimize available capacity of VESS for power system ancillary services, especially frequency regulation services (FRS). This paper, firstly, presents a novel probabilistic model for explicitly quantifying the VESS capacity in charging and discharging modes, which can further be optimized by scheduling building loads. While the optimized capacity is fully available for FRS, building customer economic benefit and comfort could considerably be impaired by various uncertainties including weather and user related parameters, as well as FRS requests. Due to limited information of the uncertainties, common model-based stochastic and robust optimization approaches are inefficient, leading to either poor out-of-sample performance or over-conservative solutions. Thus, secondly, this paper develops a data-driven distributionally robust optimization (DRO) method to robustly optimize the capacity of VESS against the worst distribution of the uncertainties with a finite training dataset. The proposed data-driven DRO-based model can well match modern data-driven operation environment. Numerical simulations validate the high efficiency and out-of-sample performance of the proposed VESS capacity optimization method under the uncertainties.