LAPSE:2019.0125
Published Article

LAPSE:2019.0125
DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids
January 7, 2019
Abstract
Micro energy grids have many merits and promising applications under the smart grid vision. There are demanding procedures for their optimal planning and performance enhancement. One of the key features of a micro energy grid is its ability to separate and isolate itself from the main electrical network to continue feeding its own islanded portion. In this paper, an optimal sizing and operation strategy for micro energy grids equipped with renewable and non-renewable based distributed generation (DG) and storage are presented. The general optimization objective is to define the best DG mix and energy storage units for self-sufficient micro energy grids. A multi-objective genetic algorithm (GA) was applied to solve the planning problem at a minimum optimization goal of overall cost (including investment cost, operation and maintenance cost, and fuel cost) and carbon dioxide emission. The constraints include power and heat demands constraints, and DGs capacity limits. The candidate technologies include CHPs (combined heat and power) with different characteristics, boilers, thermal and electrical storages, and renewable generators (wind and photovoltaic). In order to assess different configuration options and components sizes, several case studies for a typical micro energy grid have been presented.
Micro energy grids have many merits and promising applications under the smart grid vision. There are demanding procedures for their optimal planning and performance enhancement. One of the key features of a micro energy grid is its ability to separate and isolate itself from the main electrical network to continue feeding its own islanded portion. In this paper, an optimal sizing and operation strategy for micro energy grids equipped with renewable and non-renewable based distributed generation (DG) and storage are presented. The general optimization objective is to define the best DG mix and energy storage units for self-sufficient micro energy grids. A multi-objective genetic algorithm (GA) was applied to solve the planning problem at a minimum optimization goal of overall cost (including investment cost, operation and maintenance cost, and fuel cost) and carbon dioxide emission. The constraints include power and heat demands constraints, and DGs capacity limits. The candidate technologies include CHPs (combined heat and power) with different characteristics, boilers, thermal and electrical storages, and renewable generators (wind and photovoltaic). In order to assess different configuration options and components sizes, several case studies for a typical micro energy grid have been presented.
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Keywords
combined heat and power, gas-power, Genetic Algorithm, micro energy grid, multi-objective, renewable, self-sufficient
Subject
Suggested Citation
Zidan A, Gabbar HA. DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids. (2019). LAPSE:2019.0125
Author Affiliations
Zidan A: Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada; Department of Electrical Engineering, Faculty of Engineering, Assiut University, Assiut 71515, Egypt
Gabbar HA: Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada; Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Stre
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Gabbar HA: Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada; Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Stre
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Journal Name
Energies
Volume
9
Issue
8
Article Number
E616
Year
2016
Publication Date
2016-08-04
ISSN
1996-1073
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Original Submission
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PII: en9080616, Publication Type: Journal Article
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Published Article

LAPSE:2019.0125
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https://doi.org/10.3390/en9080616
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[v1] (Original Submission)
Jan 7, 2019
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Jan 7, 2019
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Record Owner
Calvin Tsay
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