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 …

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 …

Reinforcement learning (RL) is essential for the computation of game equilibria and the
estimation of payoffs under incomplete information. However, it has been a challenge to …

Great Advancements in Electric Vehicles (EVs) as well as their increasing utilization in smart
grids has highlighted the key role of EV aggregators. As a mediator, EV aggregators are …

Reactive power compensation is an important challenge in smart power systems. However,
in the context of reactive power compensation, most existing studies assume that customers …

An important goal of China's electric power system reform is to create a double-side day-
ahead wholesale electricity market in the future, where the suppliers (represented by …

In the context of an eco-responsible production and distribution of electrical energy at the
local scale of an urban territory, we consider a smart grid as a system interconnecting …

In this paper, we describe an adaptive trading agent which can sell and buy electric power
effectively in a locally produced and consumed electric energy network. The trading agents …

Local energy markets are emerging as a tool for coordinating generation, storage, and
consumption of energy from distributed resources. In combination with automation, they …

In liberalized, regulated energy markets, the different participants-namely producers and
consumers of energy, transmission and distribution system operators as well as regulatory …