ALBidS: A Decision Support System for Strategic Bidding in Electricity Markets

• Main Author: Pinto, Tiago
• Publication Date: 2019
• Other Authors: Vale, Zita
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10400.22/16864
• topic: Multi-agent simulation; Electricity markets; Decision support systems; Machine learning

This work demonstrates a system that provides decision support to players in electricity market negotiations. This contribution is provided by ALBidS (Adaptive Learning strategic Bidding System), a decision support system that includes a large number of distinct market negotiation strategies, and learns which should be used in each context in order to provide the best expected response. The learning process on the best negotiation strategies to use at each moment is developed by means of several integrated reinforcement learning algorithms. ALBidS is integrated with MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), which enables the simulation of realistic market scenarios using real data.