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Abstract
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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 bilevel 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
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