Reinforcement learning (RL) techniques have emerged as powerful tools for optimizing
energy systems, offering the potential to enhance efficiency, reliability, and sustainability …

Reinforcement learning (RL) is a potential method for tackling difficult decision-making
problems in power and energy systems. This review paper presents a thorough examination …

Energy systems undergo major transitions to facilitate the large-scale penetration of
renewable energy technologies and improve efficiencies, leading to the integration of many …

With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …

With the growing integration of distributed energy resources (DERs), flexible loads, and
other emerging technologies, there are increasing complexities and uncertainties for …

As buildings account for approximately 40% of global energy consumption and associated
greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The …

With the development of smart grid technologies an increasing number of new devices and
participants have joined modern energy systems and are inevitably making them more …

The ongoing energy transition drives the development of decentralised renewable energy
sources, which are heterogeneous and weather-dependent, complicating their integration …

The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …

The dynamic nature of sustainable energy and electric systems can vary significantly along
with the environment and load change, and they represent the features of multivariate, high …