Stochastic optimization for minimizing operational costs in smart hybrid energy networks considering electric vehicle
Nouman Qamar, Mohammed Alqahtani, Muhammad Rehan, Ijaz Ahmed, Muhammad Khalid, Zhengmao Li, Zhengmao Li, Zhengmao Li
TL;DR
This paper introduces a smart energy system that reduces costs and improves reliability by managing renewable energy and electric vehicle uncertainties using advanced optimization techniques.
Contribution
A novel stochastic optimization model using MILP for smart hybrid energy networks with PHEVs and RES uncertainties is proposed.
Findings
The proposed model reduces operational costs by 2.59% through smart charging mechanisms.
Incorporating a demand response program further reduces costs by 3.7%.
The model effectively handles RES intermittency and PHEV uncertainties.
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…
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Taxonomy
TopicsElectric Vehicles and Infrastructure · Smart Grid Energy Management · Transportation and Mobility Innovations