Researchers Fulong Yao, Wanqing Zhao, and Matthew Forshaw from the University of Hull have developed a new algorithm aimed at improving the efficiency of microgrid operations. Their work, published in the journal IEEE Transactions on Industrial Informatics, focuses on enhancing deep reinforcement learning (DRL) techniques to better handle uncertainties in predictive control approaches for microgrids.
Microgrids, which are localized energy networks that can operate independently or in conjunction with the main power grid, often integrate renewable energy sources (RES) and energy storage systems (ESS). These systems require sophisticated control strategies to optimize their performance, particularly given the variable nature of renewable energy. The researchers modeled a microgrid system and its associated Markov decision process (MDP) to better understand these complexities.
The team introduced a new error temporal difference (ETD) algorithm designed to address the uncertainties that arise from imperfect prediction models. This algorithm is incorporated into a predictive control approach based on a deep Q network (DQN). The researchers also developed a weighted average algorithm to quantify these uncertainties. By integrating these innovations, the ETD algorithm aims to improve the overall performance of DRL in optimizing microgrid operations.
Simulations using real-world data from the United States demonstrated that the ETD algorithm effectively enhances the performance of DRL. This improvement can lead to more efficient and reliable microgrid operations, which is crucial for the energy sector as it increasingly relies on renewable energy sources and advanced energy storage technologies. The practical applications of this research include better management of energy resources, reduced operational costs, and enhanced grid stability, all of which are critical for the future of energy systems.
The research was published in the IEEE Transactions on Industrial Informatics, a reputable journal known for its focus on industrial applications of advanced technologies. This work highlights the potential of advanced algorithms to address the challenges posed by the integration of renewable energy sources into microgrids, offering a promising avenue for improving energy efficiency and reliability.
This article is based on research available at arXiv.