Carbon neutrality goals are driving the increased use of renewable energy (RE). Large-
scale use of RE requires accurate energy generation forecasts; optimized power dispatch …

Over the past decade, bidding in power markets has attracted widespread attention.
Reinforcement Learning (RL) has been widely used for power market bidding as a powerful …

In recent research addressing energy arbitrage with energy storage systems (ESS s),
discrete reinforcement learning (RL) has often been employed, while the underlying reasons …

The rapid increase in distributed energy resources has augmented the significance of virtual
power plants (VPPs), which are essential for the aggregation and management of variable …

We propose a framework for applying reinforcement learning to contextual two-stage
stochastic optimization and apply this framework to the problem of energy market bidding of …

The growing penetration of renewable energy in modern power systems requires energy
storage to take on more responsibilities in multiple regulation services. Battery energy …

The battery energy storage system (BESS) has immense potential for enhancing grid
reliability and security through its participation in the electricity market. BESS often seeks …

Energy storage devices, such as batteries, thermal energy storages, and hydrogen systems,
can help mitigate climate change by ensuring a more stable and sustainable power supply …

The increased use of renewable energy sources has brought numerous environmental
benefits. However, a significant challenge with renewable energy is the inability to control …

As electricity prices become increasingly volatile due to the growing penetration of
renewable energy sources, Energy Storage Systems (ESS) are encountering greater …