Document Type: Original Article
Department of Renewable Energy and Environmental Eng., Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Department of Renewable Energies and Environmental Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz , Tabriz, Iran
Department of Earth Resource Engineering and Environmental Science, Graduate School of International Resource Sciences, Akita University, Akita, Japan
Energy Hub is an appropriate framework for modeling and optimal scheduling of multi-energy systems (MES). However, the optimal scheduling problem in the energy hub models is fed by various technical, economic, social and environmental parameters. Many of these parameters are mainly subject to uncertainties. Fluctuating nature of renewable energy sources (RES), energy prices in deregulated markets, the behavior of consumers, simplifications and approximations in modeling, linguistic terms of experts, etc. are just a few examples of uncertainty sources in the optimal scheduling problem of energy hub. Ignoring such uncertainties in the process of modeling and optimization of energy hub results in unrealistic models and inaccurate results. On the other hand addressing these uncertainties leads to increased complexity of modeling and optimization. Therefore, identifying appropriate methods to address uncertainties is essential to achieve a realistic model of MES in the framework of energy hub. This paper reviews the different uncertainty modeling methods in optimal scheduling of energy hub. In this paper, different modeling and optimization methods of energy hub are discussed and strengths and weaknesses of these methods are demonstrated. A classification and review of the various uncertainty modeling methods implemented in the most recent research on MES are done to identify efficient methods for using in energy hub models. This paper concludes that future energy hub models need to realistic scheduling and modeling of MES to be able to achieve a realistic and comprehensive model of future sustainable energy systems.