Smart energy hub optimization in presence of stochastically modeled renewables and loads

Integration of information technology with multi-carrier energy systems has paved the way for smart energy hubs (SEHs). Such energy hubs require stochastic modeling of intermittent renewable energy resources (RERs) and fluctuating demands to realize their optimal operation. Many excellent works are available related to their optimization but none of the existing works presents a comprehensive model which not only incorporates the stochastic nature of RERs and demands but also taps demand response potential in the presence of system storages. To fill this gap, this paper proposes a comprehensive model for an SEH which incorporates the stochastic nature of electrical, heating, and cooling demands in the presence of RERs, batteries, and thermal storages. In addition, optimal shifting of electrical, heating, and cooling loads are performed to further reduce the operational cost of SEH considering a price-based demand response program (DRP). The cost minimization objective is formulated as a mixed-integer linear programming (MILP) optimization problem. Five different cases are analyzed considering the inclusion and exclusion of storage and RERs with different percentages of shiftable loads for DRP. The proposed complex model is solved using CPLEX in GAMS environment. With RERs, cost reductions of 5.25% and 18.96% are observed in the operational cost of SEH without tapping demand response (DR) potential. When 10% and 20% loads are deemed to be shiftable for DR, operational costs reduce to 20.15% and 21.43% respectively with RERs and energy storages. Results of the study can support energy managers in the optimal operation of multi-carrier energy systems.