EconPapers    
Economics at your fingertips  
 

Scenario adjustable scheduling model with robust constraints for energy intensive corporate microgrid with wind power

Kun Liu and Feng Gao

Renewable Energy, 2017, vol. 113, issue C, 1-10

Abstract: With the development of wind power technology, wind power integration supplies an effective and practical way to decrease production cost for energy intensive corporate microgrid. Then the scenario adjustable scheduling model with robust constraints is built in this paper. In this model, both energy cost and wind power utilization are taken into account. Moreover, the objective is to minimize the electricity cost which is formulated by scenario tree and the wind power utilization is formulated as a robust constraint. In addition, the capacity of wind power accommodation is also analyzed. Finally, a corporate microgrid is tested. The results show that the capacity of wind power accommodation with 15% flexible load is increased 11.52% which is 5.23% more than that without re-scheduling process. And the energy expected cost with re-scheduling process is 715823$ which is 4.69% less than that without re-scheduling process.

Keywords: Pre-scheduling; Virtual re-scheduling; Adjustable scheduling; Robust constraints (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148117304421
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:113:y:2017:i:c:p:1-10

DOI: 10.1016/j.renene.2017.05.056

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-03-19
Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:1-10