Optimizing solar-wind hybrid energy systems for sustainable charging stations and commercial applications: A two-stage framework with ebola-inspired optimization


Zhu G., Yan G., Garmroudi D.

Expert Systems with Applications, vol.246, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 246
  • Publication Date: 2024
  • Doi Number: 10.1016/j.eswa.2024.123180
  • Journal Name: Expert Systems with Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Ebola searching optimization algorithm, Electric vehicle charging station, Reliability analysis, Renewable energy optimization, Solar-wind hybrid energy system, Sustainability and cost efficiency
  • Istanbul Technical University Affiliated: Yes

Abstract

This paper presents a novel approach to designing and optimizing a Solar-Wind Hybrid Energy System (SWHS) for an Electric Vehicle Charging Station (EVCS) and a university shopping complex in India. Objectives include addressing sustainable energy challenges, ensuring reliability, and minimizing the Levelized Power Supply Price (LPSP). Key contributions encompass a two-stage framework for optimal SWHS configuration, consideration of uncertainties in renewable energy generation, and the use of the Ebola Optimization Search Algorithm (ESOA). The first stage models SWHS components, formulates an objective function, and addresses reliability to minimize LPSP. The second stage employs ESOA, inspired by Ebola spread, exploring both local and global search spaces. Simulation results and a load profile analysis validate the proposed framework and ESOA's performance, observing a significant improvement of 15% in LPSP and a 10% increase in system reliability. The study provides insights into optimal Distributed Generation (DG) sizing in SWHS, ensuring reliable and cost-effective power supply for EVCS and commercial applications, contributing to the renewable energy system design and optimization field. The analysis indicates an enhanced system reliability by 10 % and a notable reduction in LPSP by 15 %.