Next Article in Journal
Carbon Footprint Estimation in Fiber Optics Industry: A Case Study of OFS Fitel, LLC
Next Article in Special Issue
A Joint Evaluation of the Wind and Wave Energy Resources Close to the Greek Islands
Previous Article in Journal
Low Carbon Economy Performance Analysis with the Intertemporal Effect of Capital in China
Previous Article in Special Issue
Assessing the Potential Benefits and Limits of Electric Storage Heaters for Wind Curtailment Mitigation: A Finnish Case Study
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(5), 864; doi:10.3390/su9050864

A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

1
Department of Electric and Energy, Elbistan Vocational School, University of K.Maraş Sütçü İmam, K.Maraş 46340, Turkey
2
Department of Electrical and Electronics, Faculty of Engineering, University of K.Maraş Sütçü İmam, K.Maraş 46040, Turkey
3
Department of Electrical Science, Kilis 7 Aralik University, Kilis 79000, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: João P.S. Catalão
Received: 7 March 2017 / Revised: 14 May 2017 / Accepted: 16 May 2017 / Published: 20 May 2017
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
View Full-Text   |   Download PDF [3285 KB, uploaded 22 May 2017]   |  

Abstract

During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality. View Full-Text
Keywords: wind power scenarios; day-ahead wind power; wind forecast wind power scenarios; day-ahead wind power; wind forecast
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yıldız, C.; Tekin, M.; Gani, A.; Keçecioğlu, Ö.F.; Açıkgöz, H.; Şekkeli, M. A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool. Sustainability 2017, 9, 864.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top