"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.
Kimball, Jonathan W.
Landers, Robert G.
Electrical and Computer Engineering
M.S. in Electrical Engineering
National Science Foundation (U.S.)
Missouri University of Science and Technology
x, 85 pages
© 2018 Prateek Jain, All rights reserved.
Thesis - Open Access
Electronic OCLC #
Jain, Prateek, "Battery optimization in microgrids using Markov decision process integrated with load and solar forecasting" (2018). Masters Theses. 7762.