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


Rising climatic concerns call for unconventiona/renewable energy sources which reduce the carbon footprint. Microgrids with battery systems that integrate a variety of renewable energy resources play a key role in utilizing these energies in a more efficient and environmentally friendly manner. This paper 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 is implemented using time series model and PV output forecasting is implemented using regression model. 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. A simulation covering one month yielded results showing that a microgrid with battery bank controlled by the MDP algorithm reduces the maximum load demand by 23.3%, leading to a cost saving of 33.1%.

Meeting Name

9th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2018 (2018: Jun. 25-28, Charlotte, NC)


Mechanical and Aerospace Engineering

Second Department

Electrical and Computer Engineering

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


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

Keywords and Phrases

Carbon Footprint; Charging (Batteries); Cost Reduction; Distributed Power Generation; Electric Power System Interconnection; Forecasting; Markov Processes; Power Electronics; Regression Analysis; Renewable Energy Resources; Secondary Batteries, Battery Optimization; Load Forecasting; Markov Decision Processes; Optimal Charging; Peak Load Demand; Regression Model; Solar Forecasting; Time Series Modeling, Battery Management Systems

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2018