Intelligent agents for negotiations in market games. I. Model

Krishna, V. Murali; Ramesh, V.

doi:10.1109/59.709106

Cited by 65 publications

(21 citation statements)

References 5 publications

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“…Without fuel and startup costs, the subproblem for pumpedstorage unit can be described as , with (13) subject to operation constraints (4)- (6). In view of the discontinuous operating regions as shown in Figs.…”

Section: B Solving Subproblemsmentioning

confidence: 99%

“…0885-8950/04$20.00 © 2004 IEEE Efforts have been made to address bidding problems ( [1], [4]- [8], [11], [13]- [15], [19], [21]). Under the assumption that the probabilistic distributions of competitors' offering prices are known, an optimal offering strategy for a single power block at a particular hour is derived by ignoring intertemporal unit constraints in [11].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Integrated Generation Bidding and Scheduling With Risk Management Under a Deregulated Power Market

Ni¹,

Luh

Rourke³

2004

IEEE Trans. Power Syst.

127

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Abstract-In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through auctions in a daily market. Developing effective strategies to optimize hourly offer curves for a hydrothermal power system to maximize profits has been one of the most challenging and important tasks for a GenCo. This paper presents an integrated bidding and scheduling algorithm with risk management under a deregulated market. A stochastic mixed-integer optimization formulation having a separable structure with respect to individual units is first established. A method combining Lagrangian relaxation and stochastic dynamic programming is then presented to select hourly offer curves for both energy and reserve markets. In view that pumped-storage units provide significant energy and reserve at generating and pumping, the offering strategies are specially highlighted in this paper. Numerical testing based on an 11-unit system with a major pumped-storage unit in the New England market shows that the algorithm is computationally efficient, and effective energy and reserve offer curves are obtained in 4-5 min on a 600-MHz Pentium III PC. The risk management method significantly reduces profit variances and, thus, bidding risks.

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Section: B Solving Subproblemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Integrated Generation Bidding and Scheduling With Risk Management Under a Deregulated Power Market

Ni¹,

Luh

Rourke³

2004

IEEE Trans. Power Syst.

127

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show abstract

“…For example, in [8,10], fuzzy reasoning with knowledge representing is used for modeling human behavior. In [9], a negotiation protocol based on the cooperative game theory was proposed for power markets. In [14], a fuzzy game theoretic approach was explored to model some Internet transactions, such as B-to-B price negotiation, B-to-C competition for customers, and C-to-C online auction.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design of an Agent Searching Algorithm for e-Marketplaces

et al. 2004

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Abstract.Recently, agent techniques in electronic marketplaces (e-marketplaces) bring B-to-B trading into a new era. However, not much analysis on the behavior of agents has been reported. In this paper, based on the ant algorithm in network routing, we introduce a jumping (searching) model for agents in an e-marketplace network. However, we should be aware that if there are too many agents in the e-marketplace network, they will use up all communication bandwidth and computing resource. It is inevitable to investigate the behavior of agents, such as agent population. Based on the existing results in the ant algorithm in network routing, we present the behavior of agents in an e-marketplace network. Hence, we can control the agent population by setting the appropriate agent generation rate.

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“…Researchers have investigated strategic behavior of agents under congestive grid, design of market structures, design of auction mechanisms for power and ancillary services, market power analyses, application of machine learning algorithms to generate bidding strategies, and also to demonstrate loopholes in market designs. Recent representative work in this area includes [Seeley, 2000;Singh, 1998;Richter, 1999;Krishna, 1998;Lane, 2000]. Lane, D.W. etc.…”

Section: Power Market Modeling and Simulationmentioning

confidence: 99%

Distributed decision-making in electric power system transmission maintenance scheduling using Multi-Agent Systems (MAS)

Zhang

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Intelligent agents for negotiations in market games. I. Model

Cited by 65 publications

References 5 publications

Optimal Integrated Generation Bidding and Scheduling With Risk Management Under a Deregulated Power Market

Optimal Integrated Generation Bidding and Scheduling With Risk Management Under a Deregulated Power Market

Analysis and Design of an Agent Searching Algorithm for e-Marketplaces

Distributed decision-making in electric power system transmission maintenance scheduling using Multi-Agent Systems (MAS)

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