The corrective control of a building-level multi-energy system (MES) for emergency load
shedding is essential to optimize the operating cost after contingency. For a Danish case …

The massive integration of renewable-based distributed energy resources (DERs) inherently
increases the energy system's complexity, especially when it comes to defining its …

Economic dispatch, as a crucial method for ensuring the normal operation of power systems,
is typically modeled as an optimization problem and solved using solvers. The introduction …

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

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

Multi-energy microgrid (MEMG) control plays a crucial role in meeting diverse energy
demands on the user side. Traditional methods face challenges in handling renewable …

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 study explores the use of deep reinforcement learning (DRL) for optimizing heating,
ventilation, and air conditioning systems to reduce energy consumption while maintaining …

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

With the rapid growth of green energy and customer-side intermittent loads, dealing with the
uncertainty and variability of the energy internet has become an urgent problem. In addition …