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Optimizing Renewable Energy Utilization Ratio with Model Predictive Control.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Optimizing Renewable Energy Utilization Ratio with Model Predictive Control./
作者:	Hockman, Michael.
面頁冊數:	1 online resource (34 pages)
附註:	Source: Masters Abstracts International, Volume: 84-10.
Contained By:	Masters Abstracts International84-10.
標題:	Electrical engineering. -
電子資源:	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.
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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
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