Optimal electricity supply bidding by Markov decision process

By Haili Song, Chen-Ching Liu, Jacques Lawarree, R. Dahlgren. 2000

254 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2fb30f1c758e0182656ce1354ba7db5c386323a1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Antonio J. Conejo
2. Francisco Javier Nogales
3. Seema Singh
4. Prabodh Bajpai
5. Jacques Lawarree

In Electrical and Electronic Engineering

1. Antonio J. Conejo
2. J. M. Arroyo
3. Luis Baringo
4. Francisco Javier Nogales
5. Javier Contreras
6. Nima Amjady
7. Rosa Espínola
8. Rafal Weron

Citations in the corpus

• Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market (2007, methodology): “Markov decision process was applied in [6] to solve multistage probabilistic bidding decision problem.”
• Risk assessment in energy trading (2003, methodology): “Reference [ 11 ] develops a Markov Decision Process model that incorporates the market states and an algorithm for suppliers to optimize their decisions in order to maximize the profit.”
• Offering Strategy Via Robust Optimization (2011, background): “Reference [16] applies a Markov decision process to calculate offering decisions.”
• Modeling Real-Time Balancing Power Market Prices Using Combined SARIMA and Markov Processes (2008, unknown): “Such tools are presented in [14], [27], [28] and [29].”
• Bidding strategies for electricity producers in a competitive electricity marketplace (2004, unknown): “The need for such an investigation is stated in [17] where risk neutral market participants are considered.”
• Nash Equilibrium Bidding Strategies in a Bilateral Electricity Market (2002, background): “A considerable amount of research has been conducted recently to study the participants’ bidding strategy and behavior in this environment, e.g., [ 1 ]‐[3].”
• Price-Taker Bidding Strategy under Price Uncertainty (2002, methodology): “Other methods such as ordinal optimization [23], Lagrangian relaxation [24], stochastic optimization [25], and Markov decision process [26] have also been applied to solve the optimal bidding strategy problem.”
• ARIMA Models to Predict Next-Day Electricity Prices (2002, methodology): “Other methods such as ordinal optimization [23], Lagrangian relaxation [24], stochastic optimization [25], and Markov decision process [26] have also been applied to solve the optimal bidding strategy problem.”