LAPSE:2019.0707
Published Article
LAPSE:2019.0707
Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid
Yeongho Choi, Yujin Lim, Hak-Man Kim
July 26, 2019
Abstract
A microgrid (MG) is a discrete energy system that can operate either in parallel with or independently from a main power grid. It is designed to enhance reliability, carbon emission reduction, diversification of energy sources, and cost reduction. When a power fault occurs in a grid, an MG operates in an islanded manner from the grid and protects its power generations and loads from disturbance by means of intelligent load shedding. A load shedding is a control procedure that results in autonomous decrease of the power demands of loads in an MG. In this study, we propose a load shedding algorithm for the optimization problem to maximize the satisfaction of system components. The proposed algorithm preferentially assigns the power to the subdemand with a high preference to maximize the satisfaction of power consumers. In addition, the algorithm assigns the power to maximize the power sale and minimize the power surplus for satisfaction of power suppliers. To verify the performance of our algorithm, we implement a multi-agent system (MAS) on top of a conventional development framework and assess the algorithm’s adaptability, satisfaction metric, and running time.
Keywords
load shedding, microgrid (MG), multi-agent system (MAS), optimization algorithm
Suggested Citation
Choi Y, Lim Y, Kim HM. Optimal Load Shedding for Maximizing Satisfaction in an Islanded Microgrid. (2019). LAPSE:2019.0707
Author Affiliations
Choi Y: Department of Information Technology Engineering, Sookmyung Women’s University, Seoul 04310, Korea
Lim Y: Department of Information Technology Engineering, Sookmyung Women’s University, Seoul 04310, Korea [ORCID]
Kim HM: Department of Electrical Engineering, Incheon National University, Incheon 22012, Korea
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Journal Name
Energies
Volume
10
Issue
1
Article Number
E45
Year
2017
Publication Date
2017-01-03
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en10010045, Publication Type: Journal Article
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LAPSE:2019.0707
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https://doi.org/10.3390/en10010045
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Jul 26, 2019
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CC BY 4.0
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Jul 26, 2019
 
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Calvin Tsay
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