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

To mitigate global warming and energy shortage, integration of renewable energy
generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been …

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

Electric vehicle (EV) has become one of the most critical components in the smart grid with
the applications of the Internet-of-Things (IoT) technologies. Real-time charging control is …

With the application of renewable energy in single office buildings, an increasing number of
power grids require building systems coupled with renewable energy to realize off-grid …

In this paper, we study an optimal day-ahead price-based power scheduling problem for a
community-scale microgrid (MG). The proposed optimization framework aims to balance …

Electricity price forecasting in deregulated open power markets using neural networks is
presented. Forecasting electricity price is a challenging task for on-line trading and e …

Abstract In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding
strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major …

Participants in a competitive electricity market develop bidding strategies in order to
maximize their own profits. On the other hand, it is necessary for regulators to investigate …

This paper provides a framework to obtain the optimal bidding strategy of a price-taker
producer. An appropriate forecasting tool is used to estimate the probability density functions …