Discovering emergence and bidding behaviour in competitive electricity market using agent-based simulation

Ly-Fie Sugianto, Zhigang Liao

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review


The aim of this paper is to explore the implication of multi agent interaction, learning and competing in a repetitive trading environment. Using the complex systems paradigm, the study attempts to observe the behavior of the agents and the emergence phenomena resulting from the multi agent interaction. Using Q-learning, generator agents can rapidly learn the market mechanism and auction rules as they seek to maximize their revenue by modifying their bidding strategies. In this paper, we experiment with different pricing rule to observe the impact on agents' behavior. The paper also describes the types of agents in each domain, together with the properties, relationships, processes and events associated with the agents. Emergence from this study includes collusion and capacity withholding to inflate price. The emergence is evidence that we can gain new knowledge from the Sciences of the Artificial.
Original languageEnglish
Title of host publicationProceedings of ELM-2016
EditorsJiuwen Cao, Erik Cambria, Amaury Lendasse, Yoan Miche, Chi Man Vong
Place of PublicationCham Switzerland
Number of pages14
ISBN (Electronic)9783319574219
ISBN (Print)9783319574202
Publication statusPublished - 2018
EventExtreme Learning Machines 2016 - Expo and Convention Centre, Marina Bay Sands, Singapore, Singapore
Duration: 13 Dec 201615 Dec 2016

Publication series

NameProceedings in Adaptation, Learning and Optimization
ISSN (Print)2363-6084
ISSN (Electronic)2363-6092


ConferenceExtreme Learning Machines 2016
Abbreviated titleELM2016
Internet address


  • Agent-based modelling
  • Auction
  • Artificial intelligence (AI)
  • Complex Adaptive Systems (CAS)

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