Artificial power markets and unintended consequences

Jacobs, Jonathan M.

doi:10.1109/59.589794

Cited by 59 publications

(19 citation statements)

References 4 publications

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“…The applicability was also found in power trading. For example, the research efforts [10][11][12][13] examined the dynamic change of a power exchange market, considering that the market was a multi-agent adaptive system. Detailed discussions on historical and current issues on the agent-based approach could be found in [14][15][16][17].…”

Section: Previous Researchmentioning

confidence: 99%

Wholesale Power Price Dynamics Under Transmission Line Limits: A Use of an Agent-Based Intelligent Simulator

Sueyoshi

Tadiparthi

2008

IEEE Trans. Syst., Man, Cybern. C

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Section: Previous Researchmentioning

confidence: 99%

Wholesale Power Price Dynamics Under Transmission Line Limits: A Use of an Agent-Based Intelligent Simulator

Sueyoshi

Tadiparthi

2008

IEEE Trans. Syst., Man, Cybern. C

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“…The problem as presented in [5] is with (6) This minimization is subject to (2)-(4). Here is not equivalent to the marginal cost but is defined per (5).…”

Section: B Payment Cost Minimization Problemmentioning

confidence: 99%

“…Let multipliers and relax coupling system demand constraints (2) and MCP-offer inequality constraints (8), 6 respectively, then the augmented Lagrangian is formed as (9) where is a positive penalty coefficient, and are nonnegative slack variables converting inequality constraints (8) to equality constraints. 7 By minimizing analytically [17, pp.…”

Section: A Augmented Lagrangianmentioning

confidence: 99%

“…Rather, the relaxed problem is taken as a whole and optimized with respect to a particular offer or a particular MCP. Furthermore for an offer subproblem with a given set of multipliers at iteration 6 The presence of the MCP-offer inequality constraints in the augmented Lagrangian brings offer-specific cost information into the solution process. 7 One way to update the Lagrangian multipliers f(t)g and f (t)g in the augmented Lagrangian is to use the method of multipliers [17].…”

Section: B Surrogate Optimization For Offer Subproblemsmentioning

confidence: 99%

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Payment Cost Minimization Auction for Deregulated Electricity Markets Using Surrogate Optimization

Luh

Blankson

Chen

et al. 2006

IEEE Trans. Power Syst.

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Abstract-Deregulated electricity markets use an auction mechanism to select offers and their power levels for energy and ancillary services. A settlement mechanism is then used to determine the payments resulting from the selected offers. Currently, most independent system operators (ISOs) in the United States use an auction mechanism that minimizes the total offer costs but determine payment costs using a settlement mechanism that pays uniform market clearing prices (MCPs) to all selected offers. Under this setup, the auction and settlement mechanisms are inconsistent since minimized costs are different from payment costs. Illustrative examples in the literature have shown that for a given set of offers, if an auction mechanism that directly minimizes the payment costs is used, then payment costs can be significantly reduced as compared to minimizing offer costs. This observation has led to discussions among stakeholders and policymakers in the electricity markets as to which of the two auction mechanisms is more appropriate for ISOs to use. While methods for minimizing offer costs abound, limited approaches for minimization of payment costs have been reported. This paper presents an effective method for directly minimizing payment costs. In view of the specific features of the problem including the nonseparability of its objective function, the discontinuity of offer curves, and the maximum term in defining MCPs, our key idea is to use augmented Lagrangian relaxation and to form and solve offer and MCP subproblems by using the surrogate optimization framework. Numerical testing results demonstrate that the method is effective, and the resulting payment costs are significantly lower than what are obtained by minimizing the offer costs for a given set of offers.Index Terms-Augmented Lagrangian relaxation, deregulated electricity markets, market clearing price (MCP), offer cost minimization, payment cost minimization, surrogate optimization.

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“…Agent-based Analysis: This group developed multi-agent adaptive systems that investigated a dynamic bidding process for electric power trading [4,[21][22][23][24][25]. In this analysis, traders are adaptive agents with different bidding preferences and strategies.…”

Section: B Previous Research On Power Tradingmentioning

confidence: 99%

Agent-Based Approach to Handle Business Complexity in U.S. Wholesale Power Trading

Sueyoshi

Tadiparthi²

2007

IEEE Trans. Power Syst.

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Abstract--This study documents the practicality of an agentbased approach by examining how two groups of agents handle business complexity related to power trading. Three important findings are identified in this research. First, the proposed approach can estimate fluctuations of electricity prices as well as other well-known methods such as neural networks and genetic algorithms. Second, multiple learning capabilities incorporated in adaptive agents do not have an advantage over limited learning capabilities in predicting the market price of electricity. Finally, a theoretical extension of multiple learning capabilities may have potential for developing the agent-based approach for power trading.

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Artificial power markets and unintended consequences

Cited by 59 publications

References 4 publications

Wholesale Power Price Dynamics Under Transmission Line Limits: A Use of an Agent-Based Intelligent Simulator

Wholesale Power Price Dynamics Under Transmission Line Limits: A Use of an Agent-Based Intelligent Simulator

Payment Cost Minimization Auction for Deregulated Electricity Markets Using Surrogate Optimization

Agent-Based Approach to Handle Business Complexity in U.S. Wholesale Power Trading

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