Price-Maker Bidding and Offering Strategies for Networked Microgrids in Day-Ahead Electricity Markets

Bo Hu, Yuzhong Gong, C. Y. Chung, Bram F. Noble, Greg Poelzer

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

This paper proposes a price-maker bidding and offering model for networked microgrids (NMG) in a pool-based day-ahead electricity market. The objective of this model is to maximize the net revenue of NMG by coordinating the joined individual microgrids to submit aggregated offers/bids to the market operator. A hybrid stochastic-robust optimization framework is developed to offset multiple associated uncertainties. The bidding and offering model is first formulated as a hard-to-solve mixed-integer nonlinear programming (MINLP) problem, which is later converted to its easy-to-solve mixed-integer linear programming (MILP) counterpart. To resolve privacy concerns of each microgrid and improve the scalability of the proposed bidding and offering model, a coordinated scheduling framework for NMG based on the Dantzig-Wolfe decomposition (DWD) method is proposed to obtain the global optimum. Numerical simulations with real-world measured data validate the effectiveness of the proposed price-maker bidding model, which is shown to outperform existing price-taker models.

Original languageEnglish
Pages (from-to)5201-5211
Number of pages11
JournalIEEE Transactions on Smart Grid
Volume12
Issue number6
DOIs
Publication statusPublished - 1 Nov 2021
Externally publishedYes

Keywords

  • Dantzig-Wolfe decomposition
  • Day-ahead electricity market
  • hybrid stochastic-robust optimization
  • networked microgrids
  • price-maker

ASJC Scopus subject areas

  • General Computer Science

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