Scenarios generation for multi-agent simulation of electricity markets based on intelligent data analysis

Santos, Gabriel; Praça, Isabel; Pinto, Tiago; Ramos, Sérgio; Vale, Zita

doi:10.1109/ia.2013.6595183

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…This integration also provides the means for ALBidS' performance to be tested in realistic simulation scenarios. MASCEM is equipped with a data-extraction tool [49] that gathers real electricity markets data from the websites of several market operators, and feeds a realistic scenarios generator [56], which allow MASCEM simulation scenarios to be as representative of the reality as possible. This way, the performance of ALBidS when supporting decisions of market players can be extrapolated to the reality, being possible to analyze the impact of the decisions in a real environment.…”

Section: Albidsmentioning

confidence: 99%

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

Pinto

Vale

Sousa

et al. 2014

ICA

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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 multiagent 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.

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Section: Albidsmentioning

confidence: 99%