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LEARNING AND OPTIMIZATION FOR REAL-TIME MICROGRID ENERGY MANAGEMENT SYSTEMS

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Date Issued:
2021
Summary:
Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches. For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study.
Title: LEARNING AND OPTIMIZATION FOR REAL-TIME MICROGRID ENERGY MANAGEMENT SYSTEMS.
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Name(s): Das, Avijit, author
Ni, Zhen, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2021
Date Issued: 2021
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 130 p.
Language(s): English
Summary: Microgrid is an essential part of the nation’s smart grid deployment plan, recognized especially for improving efficiency, reliability, flexibility, and resiliency of the electricity system. Since microgrid consists of different distributed generation units, microgrid scheduling and real-time dispatch play a crucial role in maintaining economic, reliable, and resilient operation. The control and optimization performances of the existing online approaches degrade significantly in microgrid applications with missing forecast information, large state space, and multiple probabilistic events. This dissertation focuses on these challenges and proposes efficient online learning and optimization-based approaches. For addressing the missing forecast challenges on online microgrid operations, a new fitted rolling horizon control (fitted-RHC) approach is proposed in Chapter 2. The proposed fitted-RHC approach is designed with a regression algorithm that utilizes the empirical knowledge obtain from the day-ahead forecast to make microgrid real-time decisions whenever the intra-day forecast data is unavailable. Simulation results show that the proposed fitted-RHC approach can achieve the optimal policy for the deterministic case study and perform efficiently with the uncertain environment in the stochastic case study.
Identifier: FA00013671 (IID)
Degree granted: Dissertation (PhD)--Florida Atlantic University, 2021.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Microgrids (Smart power grids)
Renewable energy
Fuzzy systems
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013671
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.