Compartir
Título
Context aware Q-Learning-based model for decision support in the negotiation of energy contracts
Autor(es)
Materia
Bilateral energy transactions
Reinforcement learning algorithm
Electricity markets
Decision support
Context awareness
Bilateral contracts
Automated negotiation
Clasificación UNESCO
3322.02 Generación de Energía
1203.17 Informática
Fecha de publicación
2019
Editor
Electrical Power and Energy Systems
Citación
J. Rodriguez-Fernandez, T. Pinto, F. Silva, I. Praça, Z. Vale, J.M. Corchado, Context aware Q-Learning-based model for decision support in the negotiation of energy contracts, International Journal of Electrical Power & Energy Systems, Volume 104, 2019, https://doi.org/10.1016/j.ijepes.2018.06.050.
Resumen
[EN] Automated negotiation plays a crucial role in the decision support for bilateral energy transactions. In fact, an adequate analysis of past actions of opposing negotiators can improve the decision-making process of market players, allowing them to choose the most appropriate parties to negotiate with in order to increase their outcomes. This paper proposes a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process. The proposed approach is based on an adaptation of the Q-Learning reinforcement learning algorithm to choose the best scenario (set of forecast contract prices) from a set of possible scenarios that are determined using several forecasting and estimation methods. The learning process assesses the probability of occurrence of each scenario, by comparing each expected scenario with the real scenario. The final chosen scenario is the one that presents the higher expected utility value. Besides, the learning method can determine which is the best scenario for each context, since the behaviour of players can change according to the negotiation environment. Consequently, these conditions influence the final contract price of negotiations. This approach allows the supported player to be prepared for the negotiation scenario that is the most probable to represent a reliable approximation of the actual negotiation environmen
URI
ISSN
0142-0615
DOI
10.1016/j.ijepes.2018.06.050
Versión del editor
Colecciones
- BISITE. Artículos [292]