Multi-objective optimization of virtual energy hub plant integrated with data center and plug-in electric vehicles under a mixed robust-stochastic model

Abstract

Nowadays, energy system planners are looking for effective solutions to take advantage of the opportunities inherent in integrated energy systems. Data centers and electric vehicles are appealing candidates for implementing integrated energy management strategies due to their high energy capacity and high flexibility potential. Hence, this paper presents a multi-objective optimization framework for optimal energy management of a virtual energy hub (VEH) plant to manage and meet the required demands of data centers, adjacent buildings, and plug-in electric vehicles (PEVs). To this end, a mixed robust-stochastic strategy is established to solve the unit commitment problem in the context of the VEH plant and implement an integrated demand response program (DRP) to maximize the VEH plant's profit, minimize carbon emission, and mitigate the risk of uncertainties. The considered VEH plant can serve the demands using the unique features of energy conversion equipment, i.e., electrical chiller, electrical boiler, combined heat and power, and renewable energy sources. The Pareto optimal solution is employed to handle the multi-objective problem with the ε-constrained method's means as mixed-integer linear programming. The presented structure is solved with a powerful commercial optimization tool, namely GAMS software. The simulation results confirm the effectiveness of the proposed strategy in the presence of the integrated DRP and up-to-date energy storage systems by increasing the profit of the VEH plant by up to 7.14% and reducing carbon emissions by up to 1.02% compared to the base case study in meeting the demands of the data center and PEVs.