Deep reinforcement learning for joint bidding and pricing of load serving entity
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 …
wholesale electricity market (WEM) and the energy price charged in the retailed electricity …
Deep reinforcement learning for strategic bidding in electricity markets
Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art
frameworks for modeling strategic bidding decisions in deregulated electricity markets …
frameworks for modeling strategic bidding decisions in deregulated electricity markets …
Approximating Nash equilibrium in day-ahead electricity market bidding with multi-agent deep reinforcement learning
In this paper, a day-ahead electricity market bidding problem with multiple strategic
generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov …
generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov …
Joint bidding and pricing for electricity retailers based on multi-task deep reinforcement learning
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 …
bidding and pricing problem for the electricity retailer in a hierarchical electricity market by …
Research on bidding strategy of thermal power companies in electricity market based on multi-agent deep deterministic policy gradient
D Liu, Y Gao, W Wang, Z Dong - IEEE access, 2021 - ieeexplore.ieee.org
With the continuous improvement of new energy penetration in the power system, the price
of the spot market of power frequently fluctuates greatly, which damages the income of a …
of the spot market of power frequently fluctuates greatly, which damages the income of a …
Deep inverse reinforcement learning for objective function identification in bidding models
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 …
has gained prominence. One crucial element in these studies is accurately defining and …
Agent-based modeling in electricity market using deep deterministic policy gradient algorithm
Game theoretic methods and simulations based on reinforcement learning (RL) are often
used to analyze electricity market equilibrium. However, the former is limited to a simple …
used to analyze electricity market equilibrium. However, the former is limited to a simple …
Deep reinforcement learning based bidding strategy for EVAs in local energy market considering information asymmetry
With the increasing penetration of distributed energy resources (DERs) in smart grids,
customers can be aggregated to participate in the local energy market (LEM). In the LEM, on …
customers can be aggregated to participate in the local energy market (LEM). In the LEM, on …
Multi-agent deep reinforcement learning for efficient multi-timescale bidding of a hybrid power plant in day-ahead and real-time markets
Effective bidding on multiple electricity products under uncertainty would allow a more
profitable market participation for hybrid power plants with variable energy resources and …
profitable market participation for hybrid power plants with variable energy resources and …
Distributed machine learning for energy trading in electric distribution system of the future
Abstract Machine Learning (ML) has seen a great potential to solve many power system
problems along with its transition into Smart Grid. Specifically, electric distribution systems …
problems along with its transition into Smart Grid. Specifically, electric distribution systems …