Masters Theses

Prateek Jain

Abstract

"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"--Abstract, page iii.

Advisor(s)

Kimball, Jonathan W.

Committee Member(s)

Ferdowsi, Mehdi
Landers, Robert G.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering

Sponsor(s)

National Science Foundation (U.S.)

Comments

This work was supported in part by the National Science Foundation, award 1610396.

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2018

Pagination

x, 85 pages

Note about bibliography

Includes bibliographical references (pages 82-84).

Rights

© 2018 Prateek Jain, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 11286

Electronic OCLC #

1041858141

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