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Neglecting consumers' comfort always leads to failure or slow-response todemand response request. In this paper, we propose several comprehensivecomfort level models for various appliances in campus-based commercialbuildings (CBs). The objective of the proposed system is to minimize O\&M costsof campus-based CBs and maximize various comfort levels simultaneously underthe worst-case scenarios. Adaptive robust optimization (ARO) is leveraged tohandle various uncertainties within the proposed system: (i) demand responsesignals sending from the distribution system operator (DSO); (ii) arrivalstate-of-charge (SoC) conditions of plug-in electric vehicles (PEVs); (iii)power outputs of renewable energy sources (RESs); and (iv) load demand of otherappliances. Benders decomposition, such as column-and-constraint generation(C\&CG) algorithm, is used to solve the reformulated NP-hard min-max problem.Extensive simulation results demonstrate the effectiveness of the proposedoptimal energy management strategy for campus-based CBs in both minimizing O\&Mcosts and maximizing comprehensive comfort levels.

Adaptive Robust Energy Management Strategy for Campus-Based Commercial Buildings Considering Comprehensive Comfort Levels