Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 12, Issue 6, Pages 5201-5211Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2021.3109111
Keywords
Electrostatic discharges; Uncertainty; Microgrids; Schedules; Privacy; Optimization; Numerical models; Day-ahead electricity market; Dantzig-Wolfe decomposition; hybrid stochastic-robust optimization; networked microgrids; price-maker
Categories
Funding
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- Saskatchewan Power Corporation (SaskPower)
- Social Sciences and Humanities Research Council (SSHRC) of Canada
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This paper introduces a price-maker bidding and offering model for networked microgrids in a day-ahead electricity market, aiming to maximize net revenue by coordinating individual microgrids. A hybrid optimization framework is used to account for uncertainties, with numerical simulations demonstrating the model's superiority over existing approaches.
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.
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