The priority of financial aspects of energy systems led the optimization goals to be focused on reducing operation
cost as much as possible. The uncertainty of electricity price is one of the main issues of supplying, and modeling
the energy systems. To overcome this issue, providing one model, which handles the unpredictable deviations of
some basic parameters such as electricity price, is of special importance. In this article, an information gap
decision theory (IGDT) has been applied to cope with the electricity price uncertainty. IGDT proposes two
functions for two different strategies namely robustness and opportunity which are risk-averse and risk-taker to
model the optimization operation in the uncertain environment, respectively. Three energy hubs in a multicarrier
energy system, combined cooling, heating and power (CCHP) integrated with renewable energies such as
photovoltaic (PV) and wind turbine (WT) are implemented in a micro energy grid to model this particular
system. In addition, the real-time demand response program (DRP) is implemented to manage the loads in
different periods. The risk-constrained operation optimization of energy hub model based on proposed IGDT
approach has been solved in GAMS software using mixed integer linear programming and results have proven
the DRP sufficiency.