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Optimization-based design of microgrids for critical loads May Alvarez, Jorge Alejandro
Abstract
With the increasing number of extreme weather events caused by global warming, there is an escalating need for reliable and sustainable alternatives for powering up critical loads. In recent years, microgrids with renewable generation have emerged as a promising technology for powering critical loads as they can achieve high power supply reliability and low carbon footprint. To meet the requirements of critical loads and to justify their economic viability, microgrids should be carefully planned and optimized. Existing tools for designing microgrids focus mainly on improving the design’s financial aspects by optimally sizing its energy sources; although these tools provide satisfactory results for conventional applications, they are not suitable for critical loads as reliability is not included in the design process. This work presents a new Vectorial Microgrid Optimization (VMO) design method for critical loads. The proposed VMO method improves the microgrid design by 1) incorporating the selection of the microgrid power conversion architecture and the size of the energy sources into a unified design strategy, 2) include the microgrid reliability, net present cost, and energy efficiency as the performance metrics. Multiobjective optimization is implemented as the decision-making tool to fulfill the critical load power requirements and reach the desire balance between the performance metrics. To highlight the benefits of the proposed method, an example of a critical load is analyzed to find its optimal microgrid design. The results indicate that the proposed approach produces a microgrid design with a 100 times lower downtime than those obtained with existing microgrid design tools while maintaining an energy efficiency of 93.76% and reducing the net present cost by 14%.
Item Metadata
Title |
Optimization-based design of microgrids for critical loads
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2021
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Description |
With the increasing number of extreme weather events caused by global warming, there is an escalating
need for reliable and sustainable alternatives for powering up critical loads. In recent years,
microgrids with renewable generation have emerged as a promising technology for powering critical
loads as they can achieve high power supply reliability and low carbon footprint. To meet the
requirements of critical loads and to justify their economic viability, microgrids should be carefully
planned and optimized. Existing tools for designing microgrids focus mainly on improving the
design’s financial aspects by optimally sizing its energy sources; although these tools provide satisfactory
results for conventional applications, they are not suitable for critical loads as reliability
is not included in the design process.
This work presents a new Vectorial Microgrid Optimization (VMO) design method for critical
loads. The proposed VMO method improves the microgrid design by 1) incorporating the selection
of the microgrid power conversion architecture and the size of the energy sources into a unified
design strategy, 2) include the microgrid reliability, net present cost, and energy efficiency as the
performance metrics. Multiobjective optimization is implemented as the decision-making tool to
fulfill the critical load power requirements and reach the desire balance between the performance
metrics. To highlight the benefits of the proposed method, an example of a critical load is analyzed
to find its optimal microgrid design. The results indicate that the proposed approach produces a
microgrid design with a 100 times lower downtime than those obtained with existing microgrid
design tools while maintaining an energy efficiency of 93.76% and reducing the net present cost by
14%.
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Genre | |
Type | |
Language |
eng
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Date Available |
2021-10-29
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0402667
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2022-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International