Min-max regret bidding strategy for thermal generator considering price uncertainty

Lei Fan, Jianhui Wang, Ruiwei Jiang, Yongpei Guan

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

The electricity price volatility brings challenges to bidding strategies in the electricity markets. In this paper, we propose a minimax regret approach for a market participant to obtain an optimal bidding strategy and the corresponding self-scheduled generation plans. Motivated by recently proposed robust optimization approaches, our approach relies on the confidence intervals of price forecasts rather than point estimators. We reformulate the minimax regret model as a mixed-integer linear program (MILP), and solve it by the Benders' decomposition algorithm. Moreover, we design a bidding strategy based on the price forecast confidence intervals to generate the offer curve. Finally, we numerically test the minimax regret approach, in comparison with the robust optimization approach, on three types of thermal generators by using real electricity price data from PJM to verify the effectiveness of our proposed approach.

Original languageEnglish (US)
Article number6767152
Pages (from-to)2169-2179
Number of pages11
JournalIEEE Transactions on Power Systems
Volume29
Issue number5
DOIs
StatePublished - Sep 2014

Keywords

  • Benders' decomposition
  • bidding strategy
  • electricity markets
  • min-max regret
  • self-scheduling
  • uncertainty

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Min-max regret bidding strategy for thermal generator considering price uncertainty'. Together they form a unique fingerprint.

Cite this