Optimal planning and scheduling of energy hub in presence of wind, storage and demand response under uncertainty

Energy Hub (EH) approach streamlines interconnection of heterogeneous energy infrastructures. The insight facilitates integration of Renewable Energy Resources (RERs) to the infrastructures. Consisting of different technologies, EH satisfies the hub output demands through transferring, converting, or storing the hub input energy carriers. Overall performance of power system depends upon optimal implementation of individual EHs. In this paper, a mathematical formulation is presented for optimal planning of a developed EH considering operation constraints. Two Objective Functions (OFs) are represented for deterministic and stochastic circumstances of wind power, electricity price, and the hub electricity demand. The OFs include costs associated with the hub investment, operation, reliability, and emission. The EH is constructed by Transformer (T), Combined Heat and Power (CHP), Boiler (B), and Thermal Storage (TS). The EH is developed by Wind Turbine (WT), Energy Storage (ES), and Demand Response programs (DR). The hub input energy carriers are electricity, gas, and water. The hub output demands are electricity, heat, gas, and water. CPLEX solver of GAMS is employed to solve Mixed Integer Linear Programming (MILP) model of the developed hub. A Monte Carlo simulation is used to generate scenarios trees for the wind, price, and demand. SCENRED tool and Backward/Forward technique of GAMS reduce scenarios to best ten scenarios. Simulation results demonstrate what technology with what capacity should be installed in the EH. The results substantiate when min/max capacities of the hub technologies are required to be installed in the hub. In the meantime, the results manifest when, what technology, and how much energy carrier should be operated to minimize the costs pertained to the hub investment, operation, reliability, and emission. Effectiveness of WT, ES, and DR in the deterministic and stochastic circumstances and influence of uncertainties of the wind, price, and demand are assessed on the hub planning. Finally, effect of gas network capacity and CHP is evaluated on the hub planning. (c) 2016 Elsevier Ltd. All rights reserved.