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Battery Optimization in Microgrids U...

Jain, Prateek.

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Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting./
Author:	Jain, Prateek.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	95 p.
Notes:	Source: Masters Abstracts International, Volume: 57-06.
Contained By:	Masters Abstracts International57-06(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10793304
ISBN:	9780438114166

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.
Jain, Prateek.

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 95 p.

Source: Masters Abstracts International, Volume: 57-06.

Thesis (M.S.)--Missouri University of Science and Technology, 2018.

Rising climatic concerns call for unconventional/renewable energy sources which reduce the carbon footprint. Microgrids that integrate a variety of renewable energy resources play a key role in utilizing these energy resources in a more efficient and environmentally friendly manner. Battery systems effectively help to utilize these energy resources more efficiently. This research work presents a framework based on Markov Decision Process (MDP) integrated with load and solar forecasting to derive an optimal charging/discharging action of Battery with rolling horizon implementation. The load forecasting regression models are discussed and developed. Also, various solar forecasting models like clear sky, multi-regression and Non-Linear Autoregressive Neural Network model with Exogenous time-series are discussed and compared. The control algorithm is developed to reduce the monthly billing cost by reducing the peak load demand while also maintaining the state of charge of the battery. The presented work simulates the control algorithm for one month based on historic load and solar data. The results indicate substantial cost savings are possible with the proposed algorithm.

ISBN: 9780438114166Subjects--Topical Terms:

649834
Electrical engineering.

Battery Optimization in Microgrids Using Markov Decision Process Integrated with Load and Solar Forecasting.
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300 $a 95 p.
500 $a Source: Masters Abstracts International, Volume: 57-06.
500 $a Adviser: Jonathan Kimball.
502 $a Thesis (M.S.)--Missouri University of Science and Technology, 2018.
520 $a Rising climatic concerns call for unconventional/renewable energy sources which reduce the carbon footprint. Microgrids that integrate a variety of renewable energy resources play a key role in utilizing these energy resources in a more efficient and environmentally friendly manner. Battery systems effectively help to utilize these energy resources more efficiently. This research work presents a framework based on Markov Decision Process (MDP) integrated with load and solar forecasting to derive an optimal charging/discharging action of Battery with rolling horizon implementation. The load forecasting regression models are discussed and developed. Also, various solar forecasting models like clear sky, multi-regression and Non-Linear Autoregressive Neural Network model with Exogenous time-series are discussed and compared. The control algorithm is developed to reduce the monthly billing cost by reducing the peak load demand while also maintaining the state of charge of the battery. The presented work simulates the control algorithm for one month based on historic load and solar data. The results indicate substantial cost savings are possible with the proposed algorithm.
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