A Reinforcement Learning Framework for Battery Energy Storage Management in Renewable-Integrated Smart Grids
TL;DR
Simulation results indicate that using RL to optimize BESS operation will improve the efficiency of dispatching energy, increase the percentage of renewable energy used, and decrease operating costs compared to traditional ways of controlling BESS.
Abstract
The goal of this research is to evaluate energy management issues in renewable-connected smart grids, especially when the power generation produced by solar and wind fluctuates, making it difficult to maintain balanced electricity supply. There is a need for battery energy storage systems (BESS) to operate optimally in order to minimize the dependency on electrical utilities, smooth out fluctuations, and increase the amount of energy generated from renewable sources. As such, we proposed a framework using reinforcement learning (RL) to create an intelligent BESS charge-discharge schedule that can adapt to the dynamic nature of the grid. Specifically, the model uses the state of the grid (the level of renewable power produced, how much electricity is being consumed, and the battery’s state of charge) to learn the best control actions to improve BESS operation. Our simulation results indicate that using RL to optimize BESS operation will improve the efficiency of dispatching energy, increase the percentage of renewable energy used, and decrease operating costs compared to traditional ways of controlling BESS. RL shows potential for use in adapting to energy management issues in smart grids.