A new approach for multi-agent coalition formation and management in the scope of electricity markets

被引:77
作者
Pinto, T. [1 ]
Morais, H. [1 ]
Oliveira, P. [1 ]
Vale, Z. [1 ]
Praca, I. [1 ]
Ramos, C. [1 ]
机构
[1] Polytech Porto IPP, GECAD Knowledge Engn & Decis Support Res Ctr, P-4200072 Oporto, Portugal
关键词
Multi-agent systems; Virtual power producers; Distributed generation; Competitive electricity markets; Intelligent decision making; Agent negotiation strategies; SYSTEM;
D O I
10.1016/j.energy.2011.05.045
中图分类号
O414.1 [热力学];
学科分类号
070201 [理论物理];
摘要
This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:5004 / 5015
页数:12
相关论文
共 26 条
[1]
Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm [J].
Amjady, Nima ;
Keynia, Farshid .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2009, 24 (01) :306-318
[2]
[Anonymous], 2007, Managing business complexity: Discovering strategic solutions with agent-based modeling and simulation
[3]
[Anonymous], 1991, Game Theory
[4]
A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets [J].
Azevedo, Filipe ;
Vale, Zita A. ;
de Moura Oliveira, P. B. .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2007, 22 (03) :995-1003
[5]
Chrysopoulos AC, 2009, LECT NOTES COMPUT SC, V5680, P111, DOI 10.1007/978-3-642-03603-3_9
[6]
Electricity Advisory Committee, 2009, KEEP LIGHTS NEW WORL
[7]
An electric energy consumer characterization framework based on data mining techniques [J].
Figueiredo, V ;
Rodrigues, F ;
Vale, Z ;
Gouveia, JB .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (02) :596-602
[8]
Modeling energy market dynamics using discrete event system simulation [J].
Gutierrez-Alcaraz, G. ;
Sheble, G. B. .
ENERGY, 2009, 34 (10) :1467-1476
[9]
Ilic M., 1998, Power systems restructuring: Engineering and economics
[10]
Strategic Bidding and Risk Assessment Using Genetic Algorithm in Electricity Markets [J].
Jain, Arvind Kumar ;
Srivastava, S. C. .
INTERNATIONAL JOURNAL OF EMERGING ELECTRIC POWER SYSTEMS, 2009, 10 (05)