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Adaptive salp swarm algorithm for sustainable economic and environmental dispatch under renewable energy sources.

Authors :
Ahmed, Ijaz
Rehan, Muhammad
Basit, Abdul
Malik, Saddam Hussain
Ahmed, Waqas
Hong, Keum-Shik
Source :
Renewable Energy: An International Journal. Mar2024, Vol. 223, pN.PAG-N.PAG. 1p.
Publication Year :
2024

Abstract

Many developing nations face energy crises, in addition to the global warming issue, owing to the recent increase in oil prices, accordingly, identifying the alternate energy sources. As a result, scientists around the globe are investigating new computational techniques for the energy dispatch under hybrid power systems. The aim of this research is to explore the integration of green energy sources (GESs) such as wind and solar in the conventional hydro–thermal coordination problem (CHTCP) to reduce the energy production cost along with the environmental benefits. The proposed hybrid energy coordination problem considers a probabilistic model for incorporating GESs uncertainties by using the point-estimation technique. Weibull and Beta distribution functions are utilized for the treatment of uncertain input variables of wind and solar sources, and the overall energy production cost is optimized via an improved heuristic swarm-based paradigm, namely, adaptive salp swam algorithm (ASSA). Three complex test systems are chosen (with and without GESs) to demonstrate the effectiveness of ASSA on hybrid power systems. The control parameters of ASSA are modified to maintain a balance between the exploration and exploitation phases to improve the convergence and to achieve better solution. The simulation results indicate that the integration of GESs into CHTCP has lowered the operational expenses by 10 percent and emissions by 64 percent. The findings have been compared with the existing techniques to show the effectiveness of the proposed approach. • Optimization problem is devised for energy hubs under renewable sources. • Point estimate method is considered for uncertainty in renewable sources. • Adaptive Salp Swarm Algorithm is evaluated on benchmark test systems. • The approach achieved improved convergence and energy production cost. • ASSA achieved low greenhouse gasses emission for multi-energy scheduling. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09601481
Volume :
223
Database :
Academic Search Index
Journal :
Renewable Energy: An International Journal
Publication Type :
Academic Journal
Accession number :
175642780
Full Text :
https://doi.org/10.1016/j.renene.2024.119944