A RA-IGDT model for stochastic optimal operation of a microgrid based on energy hub including cooling and thermal energy storages - Benyaghoob-Sani, Amirhossein; Sedighizadeh, Mostafa; Sedighizadeh, Davoud; Abbasi, Rezvan (International journal of electrical power & energy systems, 2021)
[Display omitted] •Modeling a CCHP-based EH optimal operation involving a SPCASE and ISC;•Making use of the RA-IGDT as an approach in combination with the AUGMECON method;•Propounding a bi-level optimization problem based on RA-IGDT; and.•Minimizing the operational and environmental costs of the whole system. Energy Hub (EH) is known as a complex system capable of transferring, conversion, and saving various energies in a Microgrid (MG). An important issue for investors is taking the advantage of EH for optimal management of all energy carriers, particularly in anticipating energy price. In this paper, a model for EH is considered for the purpose of the optimal operation of MG with multiple energy carrier infrastructures for day-ahead. The goal of optimization is to minimize operational and environmental costs subject to numerous technical constraints. The proposed EH manages dispatchable generation, i.e. Combined Cooling, Heat and Power (CCHP) as well as non-dispatchable generations, i.e., Wind Turbine (WT) and Photovoltaic (PV). It considers an Ice Storage Conditioner (ISC) along with a Thermal Energy Storage System (TESS) as the Energy Storage System (ESS). In particular, the effects of Solar-Powered Compressed Air Energy Storage (SPCAES) as a novel ESS are studied on the performance and efficiency of the EH operational and environmental costs. A bi-level stochastic optimization problem based on information gap decision theory (IGDT) with risk averse (RA) strategy is considered to save the Microgrid operator (MGO) from the risks of information gap between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is reorganized into a single level problem obtained by Karush-Kuhn-Tucker method. As uncertainty variables compete to expand their enveloped-bounds, the augmented ε-constraint method is employed to address the multifaceted RA-IGDT-based stochastic optimization problem proposed in the study. Implementation of the proposed framework on the typical EH shows the efficacy of the ESSs to reduce the operation costs as well as the emissions in the day-ahead energy management.