國立虎尾科技大學 |

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms./
作者:	Lopez, Nicolas.
面頁冊數:	1 online resource (641 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Energy. -
電子資源:	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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245 1 0 $a Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms.
264 0 $c 2017
300 $a 1 online resource (641 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Jose F. Espiritu.
502 $a Thesis (Ph.D.) $c The University of Texas at El Paso $d 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Energy. $3 784773
650 4 $a Operations research. $3 573517
650 4 $a Engineering. $3 561152
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0796
690 $a 0537
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710 2 $a The University of Texas at El Paso. $b Engineering. $3 1180191
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10279650 $z click for full text (PQDT)