Dynamic Energy Management for IoT-enabled Smart Microgrid using Deep Reinforcement Learning

Smart Microgrid with distributed energy resources is prevalent due to its flexibility and adaptability. Benefiting from Internet of Things technology, massive real-time energy data can be gathered for intelligent energy management. However, the high stochasticity of renewable energy resources makes it difficult to design an optimal scheduling strategy for smart microgrid. To address this problem, a dynamic energy management strategy based on model-free reinforcement leaning is proposed. Considering the uncertainties of renewable resources and energy demands, the dynamic energy management problem is formulated as a Markov decision process with unknown transition probabilities. Then, an intelligent energy management strategy based on soft actor-critic is proposed. Moreover, gate recurrent unit is incorporated to enhance the extraction of time-series characteristics. Finally, simulation results demonstrate that the proposed method significantly reduces the energy bills by up to 24.28%, compared with other strategies.

I. Introduction

In recent years, the exponential growth in energy demands and the massive integration of renewable energy sources have raised concerns about the adaptability of the traditional micro-grid [1]. To improve the energy efficiency and adaptability of microgrid, smart grid is becoming prevalent as an emerging paradigm [2]. With the Internet of Things (IoT) technology, smart grid can implement intelligent monitoring and real-time energy management, providing a more flexible operation of the microgrid [3]. However, the integration of a large number of distributed energy resources, such as photovoltaic and wind generation, poses tremendous challenges for the energy management in IoT-enabled smart microgrid [4]. This is because renewable energy sources and energy demands have significant dynamic stochasticity. Therefore, it is essential to design a dynamic energy management framework that can accommodate various uncertainties in the microgrid.