Adaptive learning in agents behaviour: A framework for electricity markets simulation

Tiago Pinto, Zita Vale, Tiago M. Sousa, Isabel Praça, Gabriel Santos, Hugo Morais

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.
Original languageEnglish
JournalIntegrated Computer-Aided Engineering
Volume21
Issue number4
Pages (from-to)399-415
ISSN1069-2509
DOIs
Publication statusPublished - 2014

Keywords

  • Adaptive learning
  • artificial intelligence
  • electricity markets
  • machine learning
  • multiagent simulation

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