ADMM based demand response scheme design for the future smart grid
This is a final year project report that displaysand presentsthe research and work that have been completed pertaining on the ADMM based demand response scheme design for the future smart grid. To develop an Alternating Direction Method of Multipliers (ADMM) based demand response algorithm which c...
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sg-ntu-dr.10356-1393782023-07-07T18:54:07Z ADMM based demand response scheme design for the future smart grid Muhammad Mustaqim Mustafah Soh Cheong Boon School of Electrical and Electronic Engineering ecbsoh@ntu.edu.sg Engineering::Electrical and electronic engineering This is a final year project report that displaysand presentsthe research and work that have been completed pertaining on the ADMM based demand response scheme design for the future smart grid. To develop an Alternating Direction Method of Multipliers (ADMM) based demand response algorithm which could illuminate the current optimization problem is the main goal for this project.. Severaldemand response programsimplemented bytheutility companyon these days. Demand response (DR) is implemented to manage the energy consumption at the customer side to benefitand profitboth customers and utility companies through an intelligent resource scheduling method. The current programsolves the optimization problem in a centralized manner. DR permits users/consumers to controltheir energy and power consumptions expenditure which will benefit both the buyer and utilities company. The goal is to use the ADMM based response scheme to solve the optimization problem above in adecentralizedand parallel way which guarantees user’s confidentialityand relieves computational stress[1].The ADMMis an algorithm that solves convex optimization issues. It breaksthem down into smaller problemso that they can be managed easily. It permits the optimization issues to be performedin parallel and it is conjointly extraordinarily economical in updating the target operation. It extends the decomposition plan to augmented Lagrangian where it iteratively solves a smaller problem with relevance to the target operation. On the other hand, as the Plug-in Electric Vehicles (PEV) became significantly popular in recent years, thanks to its energy efficiency, quiet driving experience, elimination of harmful CO2 emission and, a less expensive alternative to their gasoline-powered counterparts. Subsequently, the rise in PEV population willhave a significant effect on the electrical grid with a surge in electrical demand.Consequently, the demand response of the amalgamation of plug-in electrical vehicles (PEV) will be achieved into the power network. In addition, consumers have the opportunity and flexibility in changing the usage pattern by scheduling their load wisely and to charge once electrical price is low. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-05-19T05:52:13Z 2020-05-19T05:52:13Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/139378 en A1145-191 application/pdf Nanyang Technological University |
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Engineering::Electrical and electronic engineering Muhammad Mustaqim Mustafah ADMM based demand response scheme design for the future smart grid |
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This is a final year project report that displaysand presentsthe research and work that have been completed pertaining on the ADMM based demand response scheme design for the future smart grid. To develop an Alternating Direction Method of Multipliers (ADMM) based demand response algorithm which could illuminate the current optimization problem is the main goal for this project.. Severaldemand response programsimplemented bytheutility companyon these days. Demand response (DR) is implemented to manage the energy consumption at the customer side to benefitand profitboth customers and utility companies through an intelligent resource scheduling method. The current programsolves the optimization problem in a centralized manner. DR permits users/consumers to controltheir energy and power consumptions expenditure which will benefit both the buyer and utilities company. The goal is to use the ADMM based response scheme to solve the optimization problem above in adecentralizedand parallel way which guarantees user’s confidentialityand relieves computational stress[1].The ADMMis an algorithm that solves convex optimization issues. It breaksthem down into smaller problemso that they can be managed easily. It permits the optimization issues to be performedin parallel and it is conjointly extraordinarily economical in updating the target operation. It extends the decomposition plan to augmented Lagrangian where it iteratively solves a smaller problem with relevance to the target operation. On the other hand, as the Plug-in Electric Vehicles (PEV) became significantly popular in recent years, thanks to its energy efficiency, quiet driving experience, elimination of harmful CO2 emission and, a less expensive alternative to their gasoline-powered counterparts. Subsequently, the rise in PEV population willhave a significant effect on the electrical grid with a surge in electrical demand.Consequently, the demand response of the amalgamation of plug-in electrical vehicles (PEV) will be achieved into the power network. In addition, consumers have the opportunity and flexibility in changing the usage pattern by scheduling their load wisely and to charge once electrical price is low. |
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Soh Cheong Boon |
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Soh Cheong Boon Muhammad Mustaqim Mustafah |
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Final Year Project |
author |
Muhammad Mustaqim Mustafah |
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Muhammad Mustaqim Mustafah |
title |
ADMM based demand response scheme design for the future smart grid |
title_short |
ADMM based demand response scheme design for the future smart grid |
title_full |
ADMM based demand response scheme design for the future smart grid |
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ADMM based demand response scheme design for the future smart grid |
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ADMM based demand response scheme design for the future smart grid |
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admm based demand response scheme design for the future smart grid |
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Nanyang Technological University |
publishDate |
2020 |
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https://hdl.handle.net/10356/139378 |
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1772825298515001344 |