2021
DOI: 10.3390/en14185913
|View full text |Cite
|
Sign up to set email alerts
|

Environmental and Economic Optimization and Sizing of a Micro-Grid with Battery Storage for an Industrial Application

Abstract: This study focuses on the sizing and optimization of a micro-grid with storage, which is destined to supply the load of an economic activity zone (EAZ) in Sidi Bouzid, Tunisia. To solve this problem, a genetic algorithm is established and programmed into MATLAB. The objective functions are considered by providing three minimums, namely Greenhouse Gas emissions (GHG), Life Cycle Cost (LCC) and Embodied Energy (EE), for three values of loss of power supply probability (LPSP) previously fixed. The sizing and opti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 8 publications
(2 citation statements)
references
References 30 publications
0
1
0
Order By: Relevance
“…The microgid power flow method is necessary to understand the AC and DC power flow in the system [24]. The renewable based microgrid system for supplying power is highlighted [25][26][27]. The microgrid is designed in rural area for development with low cost [28].…”
Section: ░ 2 Literature Surveymentioning
confidence: 99%
“…The microgid power flow method is necessary to understand the AC and DC power flow in the system [24]. The renewable based microgrid system for supplying power is highlighted [25][26][27]. The microgrid is designed in rural area for development with low cost [28].…”
Section: ░ 2 Literature Surveymentioning
confidence: 99%
“…Microgrid optimization employing nature-inspired metaheuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) are also used to optimize the design, control, and operation of HREM [25][26][27][28][29]. Other nature-inspired metaheuristic algorithms, such as the strength Pareto evolutionary algorithm (SPEA), the firefly algorithm (FA), ant colony optimization (ACO), and grey wolf optimization (GWO) have also been used in recent studies [30][31][32][33].…”
Section: Introductionmentioning
confidence: 99%