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Title: A robust optimization approach for energy generation scheduling in microgrids
Authors: Wang, Ran
Wang, Ping
Xiao, Gaoxi
Keywords: Robust optimization
Uncertainty set
Reference distribution
Energy generation scheduling
Demand uncertainties
Issue Date: 2015
Source: Wang, R., Wang, P., & Xiao, G. (2015). A robust optimization approach for energy generation scheduling in microgrids. Energy Conversion and Management, 106, 597-607.
Series/Report no.: Energy Conversion and Management
Abstract: In this paper, a cost minimization problem is formulated to intelligently schedule energy generations for microgrids equipped with unstable renewable sources and combined heat and power generators. In such systems, the fluctuant net demands (i.e., the electricity demands not balanced by renewable energies) and heat demands impose unprecedented challenges. To cope with the uncertainty nature of net demand and heat demand, a new flexible uncertainty model is developed. Specifically, we introduce reference distributions according to predictions and field measurements and then define uncertainty sets to confine net and heat demands. The model allows the net demand and heat demand distributions to fluctuate around their reference distributions. Another difficulty existing in this problem is the indeterminate electricity market prices. We develop chance constraint approximations and robust optimization approaches to firstly transform and then solve the prime problem. Numerical results based on real-world data evaluate the impacts of different parameters. It is shown that our energy generation scheduling strategy performs well and the integration of combined heat and power generators effectively reduces the system expenditure. Our research also helps shed some illuminations on the investment policy making for microgrids.
ISSN: 0196-8904
DOI: 10.1016/j.enconman.2015.09.066
Schools: School of Computer Engineering 
School of Electrical and Electronic Engineering 
Rights: © 2015 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Energy Conversion and Management, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
SCSE Journal Articles

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