Risk-based performance of combined cooling, heating and power (CCHP) integrated with renewable energies using information gap decision theory

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 multi-carrier 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.