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

Pinto, Tiago; Vale, Zita

http://hdl.handle.net/10400.22/16864

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
COM_GECAD_2019_p2375.pdf		1.11 MB	Adobe PDF	Download

Send Feedback

Authors

Pinto, Tiago

Vale, Zita

Abstract(s)

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.