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Generation of realistic scenarios for multi-agent simulation of electricity markets
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Orientador(es)
Resumo(s)
Most market operators provide daily data on several market processes, including the results of all market
transactions. The use of such data by electricity market simulators is essential for simulations quality,
enabling the modelling of market behaviour in a much more realistic and efficient way. RealScen
(Realistic Scenarios Generator) is a tool that creates realistic scenarios according to the purpose of the
simulation: representing reality as it is, or on a smaller scale but still as representative as possible. This
paper presents a novel methodology that enables RealScen to collect real electricity markets information
and using it to represent market participants, as well as modelling their characteristics and behaviours.
This is done using data analysis combined with artificial intelligence. This paper analyses the way players'
characteristics are modelled, particularly in their representation in a smaller scale, simplifying the
simulation while maintaining the quality of results. A study is also conducted, comparing real electricity
market values with the market results achieved using the generated scenarios. The conducted study
shows that the scenarios can fully represent the reality, or approximate it through a reduced number of
representative software agents. As a result, the proposed methodology enables RealScen to represent
markets behaviour, allowing the study and understanding of the interactions between market entities,
and the study of new markets by assuring the realism of simulations.
Descrição
Palavras-chave
Data-Mining Electricity markets Knowledge discovery Machine learning Multi-agent simulation Scenarios generation
Contexto Educativo
Citação
Editora
Elsevier