This study investigates the strategic scheduling of a multi-energy system (MES) in the day-ahead wholesale market (DWM) as a price-maker that can submit offers/bids to purchase/sell energy. In this regard, the proposed model presents a bi-level optimization problem, wherein the upper-level is the cost minimization objective of the MES, while the lower-level is considered as the wholesale market operator (WMO) that clears the market according to the received offers/bids from producers/consumers intending to maximize public satisfaction. The Karush-Kuhn-Tucker (KKT) conditions are utilized to convert the bi-level nonlinear problem into a single level mixed-integer linear problem (MILP). A combined heat and power (CHP) unit and wind turbines (WT) are integrated into MES as the production units, while various storage technologies, such as hydrogen energy storage (HES), natural gas storage (GS) and thermal energy storage (TES), as well as demand response program (DRP), are integrated to increase the flexibility of the system. A hybrid robust optimization (RO) and stochastic programming (SP) method is deployed to deal with uncertainties of MES. The results illustrate the efficacy of this model in manipulating market clearing price in favor of the MES, while different case studies show the privileges of utilizing a hybrid RO-SP method.