國立虎尾科技大學 |

Optimizing Renewable Energy Utilization Ratio with Model Predictive Control.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Optimizing Renewable Energy Utilization Ratio with Model Predictive Control./
Author:	Hockman, Michael.
Description:	1 online resource (34 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379409401

Optimizing Renewable Energy Utilization Ratio with Model Predictive Control.
Hockman, Michael.

Optimizing Renewable Energy Utilization Ratio with Model Predictive Control. - 1 online resource (34 pages)

Source: Masters Abstracts International, Volume: 84-10.

Thesis (M.S.)--University of Washington, 2023.

Includes bibliographical references

This work focuses on optimizing the performance of power networks by maximizing and optimizing the utilization of renewable energy sources (RESs). In order to accomplish this, a cooperative distributed model predictive control scheme is used in which each microgrid subsystem consists of a controllable load, an energy storage system (ESS), and a non-renewable controllable generator. This thesis will also be looking at methods of increasing the computational efficiency of previously established algorithms. The result is better utilization of available RESs while also keeping supply-demand balance satisfied all in a more computationally efficient manner than would be otherwise possible. Simulated results are promising, showing that the utilization of RESs in the network as a whole is increased while also preventing deep discharging of the ESSs. This demonstrates the feasibility of the project as a whole.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379409401Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Model predictive controlIndex Terms--Genre/Form:

554714
Electronic books.

Optimizing Renewable Energy Utilization Ratio with Model Predictive Control.
LDR:02311ntm a22003977 4500 001 1142913
005 20240513061023.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379409401
035 $a (MiAaPQ)AAI30241363
035 $a AAI30241363
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Hockman, Michael. $3 1467441
245 1 0 $a Optimizing Renewable Energy Utilization Ratio with Model Predictive Control.
264 0 $c 2023
300 $a 1 online resource (34 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 84-10.
500 $a Advisor: Logentitiran, Thillainathan;Sheng, Jie.
502 $a Thesis (M.S.)--University of Washington, 2023.
504 $a Includes bibliographical references
520 $a This work focuses on optimizing the performance of power networks by maximizing and optimizing the utilization of renewable energy sources (RESs). In order to accomplish this, a cooperative distributed model predictive control scheme is used in which each microgrid subsystem consists of a controllable load, an energy storage system (ESS), and a non-renewable controllable generator. This thesis will also be looking at methods of increasing the computational efficiency of previously established algorithms. The result is better utilization of available RESs while also keeping supply-demand balance satisfied all in a more computationally efficient manner than would be otherwise possible. Simulated results are promising, showing that the utilization of RESs in the network as a whole is increased while also preventing deep discharging of the ESSs. This demonstrates the feasibility of the project as a whole.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Computer engineering. $3 569006
653 $a Model predictive control
653 $a Electrical Power
653 $a Microgrid
653 $a Renewable energy sources
653 $a Power networks
653 $a Energy storage system
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0464
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Washington. $b Electrical Engineering. $3 1180628
773 0 $t Masters Abstracts International $g 84-10.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30241363 $z click for full text (PQDT)