Economic-Environmental Optimization of an Energy Hub with DRPs and Power-to-Gas Technology under Uncertainties with Introducing a Novel Optimization Algorithm

In this paper, a new framework for stochastic optimal economic-environmental energy management of an energy hub (EH) is proposed. The uncertain {Flexible loads, energy market prices, wind power generation and power to gas (P2G) technology} are also considered in the proposed structure. Here, an innovative objective function including both operating and emissions costs is suggested and a self-adaptive hybrid algorithm of hybrid bald eagle search and mutant grey wolf optimization (hBES-MGWO) is proposed for solving such optimization problem. The simulation results indicate that flexible loads and their participation in demand response programs (DRPs) can considerably reduce the operating costs of EH. More participation of flexible loads in DRPs will cause to the lower operation cost. In this paper, an economic model of responsive loads is employed based on elasticity of demand and customer benefit function. It is shown that considering the emission cost in objective function leads to an increasement in EH’ operation costs. It is also demonstrated that the proposed hBES-MGWO has higher accuracy in comparison with the other optimization algorithms.