A CVaR-Robust-Based Multi-Objective Optimization Model for Energy Hub Considering Uncertainty and E-Fuel Energy Storage in Energy and Reserve Markets

The increasing demand for energy carriers has expanded the use of hubs that employ distributed response programs to improve power system reliability and efficiency. Moreover, unstable behavior renewable resources, as well indeterminate electrical thermal demands, create major problems hub operation. Inspired by this, this paper presents a day-ahead scheduling framework (EH) in reserve markets considering two main objectives economy pollution emission. studied consists novel hybrid storage facility based on fuel cell, wind power, photovoltaic energy, particular cell unit presence elastic demand. This multi-component participates single entity optimize proposed method also models uncertainty speed, irradiance, load using Mont-Carlo method. risk level is analyzed conditional value at (CVaR) approach increase EH operation multi-objective model solved MINLP General Algebraic Modeling System (GAMS) minimize cost Finally, prove effectiveness adding new E-fuel uncertainties operation, compared with other reported models.