The concept of transactive energy market (TEM) has been introduced to efficiently balance
supply and demand across the electrical networks in a distributed manner. TEM allows …

Thanks to reinforcement learning (RL), decision‐making is more convenient and more
economical in different situations with high uncertainty. In line with the same fact, it is …

Electricity markets need to continuously evolve to address the growing complexity of a
predominantly renewable energy-driven, highly interconnected, and sector-integrated …

The single-task deep reinforcement learning (STDRL)-based methods solve the joint
bidding and pricing problem for the electricity retailer in a hierarchical electricity market by …

Driven by the global decarbonization effort, the rapid integration of renewable energy into
the conventional electricity grid presents new challenges and opportunities for the battery …

Due to the deregulation of power systems worldwide, bidding behavior simulation research
has gained prominence. One crucial element in these studies is accurately defining and …

In this paper, we address the problem of jointly determining the energy bid submitted to the
wholesale electricity market (WEM) and the energy price charged in the retailed electricity …

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art
frameworks for modeling strategic bidding decisions in deregulated electricity markets …

Developing optimal bidding strategies for the market participants plays a crucial role in
increasing the profit of the electricity market. For the complex double-sided auction market …

Massive penetration of distributed energy resources (DER) in distribution networks
necessitates the need for a dynamic marketplace in the bottom layer to balance the …