Technological advancements, urbanization, high energy demand, and global requirements
to mitigate carbon footprints have led to the adoption of innovative green technologies for …

Due to the availability of more comprehensive measurement data in modern power systems,
there has been significant interest in developing and applying reinforcement learning (RL) …

Abstract Deep Reinforcement Learning (DRL) presents a promising avenue for optimizing
Energy Storage Systems (ESSs) dispatch in distribution networks. This paper introduces RL …

As the landscape of electric power systems is transforming towards decentralization, small-
scale electric power systems have garnered increased attention. Meanwhile, the …

The optimal dispatch of energy storage systems (ESSs) in distribution networks poses
significant challenges, primarily due to uncertainties of dynamic pricing, fluctuating demand …

Deep reinforcement learning (DRL) methods for microgrid economic dispatch often suffer
from reduced decision accuracy due to environmental changes within control periods. To …

The integration of distributed energy resources (DER) has escalated the challenge of
voltage magnitude regulation in distribution networks. Model-based approaches, which rely …

In light of the growing prevalence of distributed energy resources, energy storage systems
(ESs), and electric vehicles (EVs) at the residential scale, home energy management (HEM) …

The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due
to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and …

The global increase in energy demand has led to a growing focus on renewable energy
sources as a potential solution. This study examines the annual total cost of optimized off …