Probabilistic energy management with emission of renewable micro-grids including storage devices based on efficient salp swarm algorithm

Ehab E. Elattar and Salah K. ElSayed

Renewable Energy, 2020, vol. 153, issue C, 23-35

Abstract: In this paper, the efficient salp swarm algorithm (ESSA) is proposed to solve the energy management (EM) with emission problem of renewable micro-grid (MG) including storage devices. Because of the uncertainties in the renewable energy sources (RESs), load demand and market prices, the probabilistic approach based on (2m + 1) point estimate method and ESSA is employed to solve the probabilistic EM problem. The proposed ESSA can be derived by introducing two modifications on the conventional salp swarm algorithm (SSA) to improve the balance between exploration and exploitation, speed up the convergence and avoiding the stuck in local optima of the SSA. The ESSA is employed to solve the deterministic and probabilistic EM with emission problem. Where the multi-objective optimization problem of cost and emission functions is transferred into a single objective function to minimize the total operating cost of the MG. The proposed ESSA is evaluated using a typical grid-connected MG with energy storage devices and compared with other methods. The results verify the superiority of the ESSA to solve the EM problem of the MG over other methods.

Keywords: Efficient salp swarm algorithm; Probabilistic energy management; Micro-grid; Emission; Renewable energy sources; 2m + 1 point estimate method (search for similar items in EconPapers)
Date: 2020
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/S096014812030166X
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:renene:v:153:y:2020:i:c:p:23-35

DOI: 10.1016/j.renene.2020.01.144

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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