2013 IEEE Conference on Clean Energy and Technology (CEAT) 2013
DOI: 10.1109/ceat.2013.6775604
|View full text |Cite
|
Sign up to set email alerts
|

Economic dispatch in a microgrid through an iterated-based algorithm

Abstract: This paper presents a new algorithm to solve economic dispatch (ED) as an optimization problem in power systems. A simple iterated-based algorithm has been developed. It starts with a guess and converges to the optimal point in such a way that the total generation cost of a microgrid is minimized. Since microgrid is considered as a lossless small network, the algorithm is supposed to solve the lossless economic dispatch problem. The proposed method applied on a microgrid consisting of three distributed energy … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2017
2017
2022
2022

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 18 publications
(7 citation statements)
references
References 19 publications
0
7
0
Order By: Relevance
“…(2). Constant parameters values and power generation limits for each DG can be found in the study [30].…”
Section: Dg Test Systemmentioning
confidence: 99%
“…(2). Constant parameters values and power generation limits for each DG can be found in the study [30].…”
Section: Dg Test Systemmentioning
confidence: 99%
“…Clustered-based approach is novel in this teacher-learner method, where learners learn through interaction with the teacher and also learn via interaction among different learners. A PV, FC, and biomass type microgrid seen in real-valued cultural algorithm (RVCA), ant colony optimisation (ACO) in [115,116], unlike linear programming and conventional evolutionary methods, this approach is better to avoid premature convergence. Finally, with RVCA, the authors witnessed to reduce the annual operational cost of microgrid with solar, biomassgasifier unit for same load profile.…”
Section: Ed and Ucmentioning
confidence: 99%
“…In this respect, Modiri-Delshad et al (2016) [44] used back-tracking search algorithm; Kaboli et al (2016) [45] proposed an arti cial cooperative search algorithm; Ra eerad et al (2017) [46] applied a multi-objective Particle Swarm Optimization (PSO); [47] introduced rain-fall optimization algorithm; moreover, [48] proposed a gene expression programming for electrical consumption forecasting; Sebtahmadi et al (2018) [49] used PSO-DQ Current Control Scheme; Mansouri et al (2012) [50] presented a hybrid neuro-fuzzy-P.I. fed Controller for controlling the rpm of brush-less D.C. motors; Modiri-Delshad et al (2013) [51] proposed an iterative algorithm for an economic dispatch in a micro grid. They further used the algorithm to address the economic dispatch in a power system.…”
Section: Literature Reviewmentioning
confidence: 99%