Risk-averse multi-objective optimization of multi-energy microgrids integrated with power-to-hydrogen technology, electric vehicles and data center under a hybrid robust-stochastic technique.

• Integrating power-to-hydrogen, electric vehicles and data center under in MEM. • Proposing robust-stochastic strategy to control decision-making in presence of various uncertainties. • Establishing a multi-objective robust-stochastic strategy for the MEM. • Electricity flow, heating flow, and cooling flow are provided to the data centers and adjacent commercial and residential buildings. To achieve zero net emission conditions, multi-energy microgrids (MEMs) have grown rapidly in recent years. Hydrogen-based technologies and energy conversion systems bring unique opportunities for decreasing carbon emissions in MEMs. Hence, this paper tries to optimize the scheduling of the integrated energy sources in a MEM coupled with promoted energy conversion facilities and the hybrid demand response (DR) scheme. To this end, a hybrid robust-stochastic methodology is established in the form of the multi-objective optimization problem to minimize the operation cost and emission. The developed energy management model for MEM is used to serve and manage the behavior of the data centers and adjacent buildings as the flexible/non-flexible demands. The proposed strategy provides a continuous control mechanism for MEM's operator in the presence of various energy suppliers like hydrogen-based technologies, i.e., power-to-hydrogen unit, hydrogen storage system (HSS), and fuel cell, battery electric vehicle (BEV), etc. Herein, the uncertain characteristics of the electricity market price, energy demands, and renewable power production are controlled by the robust approach and several scenarios with appropriate probabilities. The proposed strategy is applied to various case studies of a typical MEM associated with data centers in the day-ahead energy markets. According to the obtained simulation results, the proposed energy management strategy for MEM can reduce the operation cost and emission by up to 8.2% and 3.9%, respectively. [ABSTRACT FROM AUTHOR]