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Open AccessArticle

A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration

by Yuewen Jiang 1,*, Meisen Chen 1 and Shi You 2
1
College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350116, Fujian, China
2
Energy System Operation and Management, Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, Elektrovej, 2800 Kgs. Lyngby, Denmark
*
Author to whom correspondence should be addressed.
Academic Editor: Frede Blaabjerg
Energies 2017, 10(4), 554; https://doi.org/10.3390/en10040554
Received: 6 February 2017 / Revised: 31 March 2017 / Accepted: 12 April 2017 / Published: 18 April 2017
(This article belongs to the Section Electrical Power and Energy System)
In a conventional electricity market, trading is conducted based on power forecasts in the day-ahead market, while the power imbalance is regulated in the real-time market, which is a separate trading scheme. With large-scale wind power connected into the power grid, power forecast errors increase in the day-ahead market which lowers the economic efficiency of the separate trading scheme. This paper proposes a robust unified trading model that includes the forecasts of real-time prices and imbalance power into the day-ahead trading scheme. The model is developed based on robust optimization in view of the undefined probability distribution of clearing prices of the real-time market. For the model to be used efficiently, an improved quantum-behaved particle swarm algorithm (IQPSO) is presented in the paper based on an in-depth analysis of the limitations of the static character of quantum-behaved particle swarm algorithm (QPSO). Finally, the impacts of associated parameters on the separate trading and unified trading model are analyzed to verify the superiority of the proposed model and algorithm. View Full-Text
Keywords: day-ahead market; improved quantum-behaved particle swarm algorithm (IQPSO); real-time market; robust optimization; unified trading model; wind power day-ahead market; improved quantum-behaved particle swarm algorithm (IQPSO); real-time market; robust optimization; unified trading model; wind power
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Jiang, Y.; Chen, M.; You, S. A Unified Trading Model Based on Robust Optimization for Day-Ahead and Real-Time Markets with Wind Power Integration. Energies 2017, 10, 554.

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