EconPapers    
Economics at your fingertips  
 

Mean-variance model for power system economic dispatch with wind power integrated

Y.Z. Li, Q.H. Wu, M.S. Li and J.P. Zhan

Energy, 2014, vol. 72, issue C, 510-520

Abstract: This paper presents the mean-variance (MV) model to solve the power system economic dispatch with wind power integrated, based on the optimal power flow problem. The MV model considers the profit and risk simultaneously under the environment of uncertain wind power, which is formulated as a multi-objective optimization problem. The MGSOMP (multiple-group search optimizer with multiple producers) is proposed to solve the MV model to find Pareto solutions, based on GSOMP (group search optimizer with multiple producers). Then the preference ranking organization method is used for decision making to determine the final dispatch solution. The MV model and MGSOMP are tested on the modified IEEE 30-bus and 118-bus power systems, respectively. Simulation results show that the MV model is well applicable to solve power system dispatch considering wind power integrated, and MGSOMP can obtain more convergent and better diversified Pareto solutions, compared with GSOMP.

Keywords: Mean-variance model; Economic dispatch; Wind power; Multi-objective optimization algorithm; Decision making (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214006409
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:energy:v:72:y:2014:i:c:p:510-520

DOI: 10.1016/j.energy.2014.05.073

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-12-28
Handle: RePEc:eee:energy:v:72:y:2014:i:c:p:510-520