A second-order cone programming-based microgrid bidding strategy considering real-time market price correlation

This study establishes a non-deterministic microgrid bidding strategy methodology participating in a day-ahead energy market. In this regard, a stochastic programming-based model is mathematically constructed, fully considering the uncertainty of day-ahead market prices, electricity demand, and rene...

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Bibliographic Details
Published in:Electrical engineering Vol. 107; no. 6; pp. 8141 - 8154
Main Authors: El-Meligy, Mohammed A., Sharaf, Mohamed, Alnowibet, Khalid A., Abdelgawad, Abdelatty E.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2025
Springer Nature B.V
Subjects:
Alternative energy sources
Arbitrage
Bids
Correlation
Distributed generation
Economics and Management
Electric power demand
Electric vehicles
Electrical Engineering
Electrical Machines and Networks
Electricity
Energy industry
Energy management
Energy Policy
Energy resources
Energy storage
Engineering
Linear programming
Market prices
Mixed integer
Operating costs
Optimization
Original Paper
Power Electronics
Profits
Real time
Robustness
Stochastic programming
Uncertainty
Ellipsoidal uncertainty set
Correlation
Robust optimization
Microgrid bidding strategy
Second-order cone programming
Stochastic programming
ISSN:0948-7921, 1432-0487
Online Access:Get full text
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Summary:This study establishes a non-deterministic microgrid bidding strategy methodology participating in a day-ahead energy market. In this regard, a stochastic programming-based model is mathematically constructed, fully considering the uncertainty of day-ahead market prices, electricity demand, and renewable generation by creating several scenarios. The real-time electricity market is also considered, where the real-time price uncertainty is modeled using robust optimization. Even though robust optimization has already been employed to model real-time price uncertainty, the correlation among real-time prices at different hours is neglected. Thus, how real-time price correlation affects the optimal microgrid bidding strategy remains unclear. This study develops a novel mathematical model that captures real-time price correlations by constructing an ellipsoidal uncertainty set. In this context, unlike the current approaches in which the real-time price deviation ranges are modeled as constants at each hour, they are assumed to lie within a prespecified ellipsoidal uncertainty set. In doing so, a mixed-integer second-order cone programming problem is created, which existing solving methods can efficiently handle. The presented scheme is applied to a typical microgrid, and the efficiency and excellence of the presented framework are validated by comparing numerical results with traditional models.
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ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-025-02960-9