Stochastic optimization for minimizing operational costs in smart hybrid energy networks considering electric vehicle

Abstract

The residential energy hub (REH) effectively satisfies power demands, but the incorporation of renewable energy sources (RES) and the increasing use of plug-in hybrid electric vehicles (PHEVs), with their unpredictable nature, complicates its optimal functionality and challenges the accurate modeling and optimization of REH. This work proposed a stochastic model for REH using mixed integer linear programming (MILP) to optimally handle the associated uncertainties of RES and PEHVs, which was then solved using GAMS software. Four case studies with varying conditions were conducted to verify the performance of the proposed scheme, and the results indicate that the approach is superior in optimally handling the system’s associated limitations. These limitations include the intermittency and variability of RES and the uncertainties associated with PHEVs, such as arrival time, travel distance, and departure time. Additionally, this work introduces a smart charging mechanism that charges and discharges PHEVs economically, both in terms of cost and reliability. The results indicate that incorporating a smart charging mechanism decreases the total operating cost of smart REH by 2.59% while maintaining the comfort level of the consumer and increasing the reliability of the overall system. Finally, smart REH adopts a demand response program (DRP), which further reduces the operational cost by 3.7%. Furthermore, the proposed approach demonstrates a significant reduction in operating costs and an improvement in the reliability of the smart REH.