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Hybrid Microgrid Configuration Optim...
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ProQuest Information and Learning Co.
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms./
Author:
Lopez, Nicolas.
Description:
1 online resource (641 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:
Dissertation Abstracts International78-11B(E).
Subject:
Energy. -
Online resource:
click for full text (PQDT)
ISBN:
9780355032833
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
Lopez, Nicolas.
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
- 1 online resource (641 pages)
Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Thesis (Ph.D.)
Includes bibliographical references
This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355032833Subjects--Topical Terms:
784773
Energy.
Index Terms--Genre/Form:
554714
Electronic books.
Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
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Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
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This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.
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click for full text (PQDT)
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