to minimize social costs, we propose a new mechanism where storage owners bid using an energy-cycling function. This function maps prices (in dollars per cycle depth) to the

With the growing penetration of renewable energy resource, electricity market prices have exhibited greater volatility. Therefore, it is important for Energy

Considering the cooperation of wind power bidding and energy storage system (ESS) operation with uncertainty, this paper proposes a coordinated

NNEBs refer to market bids that are represented by monotonic neural networks with discrete outputs. To achieve effective learning of NNEBs, we first learn a neural network as a

Under this context, a joint bidding strategy for battery energy storage in the regulation and energy electricity market is proposed in this paper. Firstly, a deep neural network

To address this challenge, we modify the common reinforcement learning(RL) process by proposing a new bid representation method called Neural Network Embedded Bids

Using deep reinforcement learning (DRL), we present a BESS bidding strategy in the joint spot and contingency FCAS markets, leveraging a transformer-based temporal feature

To this end, in this research, we develop a constrained deep Q-learning based bidding algorithm to determine the optimal bidding strategy in the day-ahead electricity market.

This work modifications the common reinforcement learning (RL) process by proposing a new bid representation method called Neural Network Embedded Bids

Abstract: This paper proposes the use of Artificial Neural Networks (ANN) for the efficient bidding of a Photovoltaic power plant with Energy Storage System (PV-ESS)