https://orcid.org/0000-0002-7063-627X
![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
ABSTRACT
This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
| Aw | = | Swept area |
| C | = | Cost ($) |
| CP | = | power coefficient |
| E | = | Energy (kWh) |
| f | = | Fitness weight |
| i | = | Existing search agent |
| Lf(δ) | = | Levy flight value |
| P | = | Power (kW) |
| t | = | Time/Existing repetition |
| v | = | Wind velocity (m/s) |
| w | = | Size step |
| Zi | = | Non-natural lookout bee |
| Zt,i | = | Initialize lookout bee members |
| = | Greatest global result’s objective value | |
| Greek symbols | = | |
| Δt | = | Unit optimization time |
| η | = | Efficiency (%) |
| ξ | = | Lévy index |
| ρa | = | Air density |
| ѓ | = | Gamma function |
| Subscripts | = | |
| b | = | battery |
| dch | = | discharge |
| DG | = | diesel generator |
| Abbreviations | = | |
| r | = | reducer |
| BESS | = | Battery Energy Storage System |
| CFDO | = | Contracted Fitness-Dependent Optimization Algorithm |
| COE | = | Cost Of Energy |
| DOD | = | Depth Of Discharge |
| ESS | = | Energy Storage System |
| FCR | = | Fuel Consumption Rate |
| GWO | = | Grey Wolf Optimizer |
| LHV | = | Lower Heation Value |
| MVO | = | Multi-Verse Optimizer |
| PIO | = | Pigeon-Inspired Optimization |
| POA | = | Pelican Optimization Algorithm |
| PV | = | Photovoltaic |
| RER | = | Renewable Energy Resource |
| SCA | = | Sine Cosine Algorithm |
| SHRES | = | Stand-Alone Hybrid Renewable Energy System |
| SSA | = | Salp Swarm Algorithm |
| TNAC | = | Total Net Annual Cost |
| TSA | = | Tunicate Swarm Algorithm |
| WT | = | Wind Turbine |
Additional information
Funding
Notes on contributors
Lirong Zhang
Lirong Zhang is from Beijing Polytechnic, Beijing 100176, China. His research interests are in the application of artificial intelligence and heuristic optimization methods to systems, operations, and planning. He has authored and co-authored several papers in international journals and conference proceedings. Also, he collaborates with several international journals as a reviewer board.
Ying Xiao
Ying Xiao is from Beijing Polytechnic, Beijing 100176, China. His research interests are in the application of artificial intelligence and heuristic optimization methods to systems, operations, and planning. He has authored and co-authored several papers in international journals and conference proceedings. Also, he collaborates with several international journals as a reviewer board.
Homayoun Ebrahimian
Homayoun Ebrahimian received the master and doctor degree in biomedical engineering from the Islamic Azad University Science and Research Branch, Iran. His current research interests include biomedical systems energy system analysis. He has authored and co-authored over 20 journal and conference papers.