Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in
driving sustainability across various sectors. This paper reviews recent advancements in AI …

The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent
energy management, and distributed energy integration poses new requirements for …

The demand response model proposed in this work offers a game-changing solution to the
challenges posed by the unpredictability of renewable energy sources. By combining both …

Identifying statistical trends for a wide range of practical power system applications,
including sustainable energy forecasting, demand response, energy decomposition, and …

Power flow control is a critical aspect of preventing overloads in electrical networks, which
can lead to severe consequences such as disconnections, cascading outages, and system …

Efficient yet sufficient exploration remains a critical challenge in reinforcement learning (RL),
especially for Markov Decision Processes (MDPs) with vast action spaces. Previous …

Real-time power network dispatching and control (PDC) presents unique challenges that
traditional methods cannot effectively address due to the consideration of temporal dynamic …

Deep Q-learning based algorithms have been applied successfully in many decision making
problems, while their theoretical foundations are not as well understood. In this paper, we …

To alleviate the power flow congestion in active distribution networks (ADNs), this paper
proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First …

Ongoing global decarbonization and energy transition have led to significant changes in the
traditional power grid, resulting in new challenges for grid operators. These challenges …