Simulating a Multi-agent Electricity Market

Trigo, Paulo; Coelho, Hélder

doi:10.1109/bwss.2010.14

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Results show a coherent market behavior as price reductions from competition favour consumers and harm producers that do not adapt their electricity bids. The comprehensive description of TEMMAS refers to [25,29].…”

Section: Decision-making For Electricity Marketsmentioning

confidence: 99%

“…The user is given the capability to configure the decision-making agents (specify the bidding strategies or choose from a set of predefined); also decision-making agents may be configured as learning or nonlearning agents. The system is being specified (from the beginning) as a multi--agent environment [25], modeled with INGENIAS, implemented with JADE [2] (using "INGENIAS to JADE" transformations and additional Java coding) and with R-Project [30] for the statistical computations and graphics' generation. Although TEMMAS is currently in a preliminary stage its architectural and design decisions are strongly founded in the MABS field and the initial results are an incentive to further extend the already implemented system.…”

Section: The Experimental Scenario (Iberian Electricity Market)mentioning

confidence: 99%

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Collective-Intelligence and Decision-Making

Trigo¹,

Coelho²

2010

Computational Intelligence for Engineering Systems

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Abstract. Decision-making, while performed by humans, is also expected to be found in most (artificial) intelligent systems. Usually, the cognitive research assumption is that the individual is the correct unit for the analysis of (human) intelligence. Yet, the multi-agent assumption is that of a society of interacting individuals (agency) that collectively supersedes individual capabilities. Therefore, the entire society of agents is, itself, an additional locus for the analysis of this collective-intelligence. In this paper we propose models that explore the agent-agency mutual influence from the decision-making perspective. We outline three case study scenarios used during the model's experimental evaluation: i) large-scale disasters, ii) electricity markets, and iii) Web-empowered knowledge and social connectivity. The scenario-driven evaluations are being used to provide the alignment weights for the effort along our research lines.

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Section: Decision-making For Electricity Marketsmentioning

confidence: 99%

Section: The Experimental Scenario (Iberian Electricity Market)mentioning

confidence: 99%

Collective-Intelligence and Decision-Making

Trigo¹,

Coelho²

2010

Computational Intelligence for Engineering Systems

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“…In this complex system, it is difficult to perform real tests on a controlled environment, so the main solution is the use of simulation processes. Simulation tools have been applied with success to several areas, such as military systems [3], information retrieval systems [4], energy market [5], social analysis [6], crime [7], decision support systems [8], supply chain [9] and many more domains [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Electrical Distributed Energy Resources for Electrical Vehicles Charging Process Strategy

Ferreira¹,

Trigo²,

Silva³

et al. 2010

2010 Second Brazilian Workshop on Social Simulation

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This paper presents a simulation platform for control and monitor the Electric Vehicle charging process, based on existing power distribution limitations and Microgeneration capacity. The goal of this research is to simulate the energy consumption and their unexpected behavior, using past experience and taking into account distribution network and home power limitation to find an intelligent charging pattern. This paper proposes a novel approach for this problem based on a simulation platform where stochastic process is adopted to perform unexpected user behavior. This simulation platform can be used to determine the capability of the actual electrical distribution network for supplying energy to the final consumers and for charging the bank of batteries of electrical vehicles, which can occur simultaneously.

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(Virtual) Agents for running electricity markets

Trigo

Marques

Coelho

2010

Simulation Modelling Practice and Theory

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This paper describes a multi-agent based simulation (MABS) framework to construct an artificial electric power market populated with learning agents. The artificial market, named TEMMAS (The Electricity Market Multi-Agent Simulator), explores the integration of two design constructs: (i) the specification of the environmental physical market properties and (ii) the specification of the decision-making (deliberative) and reactive agents. TEMMAS is materialized in an experimental setup involving distinct power generator companies that operate in the market and search for the trading strategies that best exploit their generating units' resources. The experimental results show a coherent market behavior that emerges from the overall simulated environment. (c)

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Simulating a Multi-agent Electricity Market

Cited by 5 publications

References 5 publications

Collective-Intelligence and Decision-Making

Collective-Intelligence and Decision-Making

Simulation of Electrical Distributed Energy Resources for Electrical Vehicles Charging Process Strategy

(Virtual) Agents for running electricity markets

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