An effective optimization strategy for design of standalone hybrid renewable energy systems

Hoda Abd El-Sattar, Salah Kamel, Mohamed H. Hassan and Francisco Jurado

Energy, 2022, vol. 260, issue C

Abstract: In this research, a hybrid algorithm called a Gradient Artificial Hummingbird Algorithm (GAHA) is developed for reducing energy cost (EC) of microgrid system; this hybrid approach is based on the combination between Gradient based optimizer (GBO) and Artificial Hummingbird Algorithm (AHA). This proposed GAHA is firstly tested on a group of 23 benchmark test functions, and its obtained results are compared with five well-known algorithms including supply-demand-based optimization (SDO), wild horse optimizer (WHO), grey wolf optimizer (GWO), tunicate swarm algorithm (TSA) and original AHA algorithm. The obtained results using the GAHA algorithm show better performance in most cases and comparative results in other cases. Moreover, the developed GAHA algorithm is applied to obtain the optimal configuration of an isolated hybrid system, which is consists of photovoltaic (PV) modules, wind turbine (WT), biomass system, and battery storage system. The proposed standalone hybrid system is implemented for feeding loads in the new Tiba city, in northeast of Luxor in southern Egypt. GAHA technology is applied to four different system configurations to obtain the optimal system design. The GAHA optimization findings are compared with the findings from other optimization algorithms namely the original AHA, Sine Cosine Algorithm (SCA), and whale optimization algorithm (WOA) methods. The GAHA method achieved the best results for all the suggested configuration scenarios compared to the rest of the algorithms used.

Keywords: Gradient; Artificial hummingbird; Algorithm; PV; Wind; Biomass; Battery; Optimal sizing; Energy cost (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222018047
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018047

DOI: 10.1016/j.energy.2022.124901

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().