Next Article in Journal
A Study on a Control Method with a Ventilation Requirement of a VAV System in Multi-Zone
Next Article in Special Issue
Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine
Previous Article in Journal
The Spillover Effects on Employees’ Life of Construction Enterprises’ Safety Climate
Previous Article in Special Issue
Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(11), 2065; doi:10.3390/su9112065

Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

1
C-MAST, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilhã, Portugal
2
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
3
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
4
Australian Energy Research Institute (AERI), School of Electrical Engineering and Telecommunications, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia
5
INESC TEC, Faculty of Engineering, University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal
6
INESC-ID, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais, 1, 1049-001 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 27 September 2017 / Revised: 27 October 2017 / Accepted: 6 November 2017 / Published: 10 November 2017
(This article belongs to the Special Issue Wind Energy, Load and Price Forecasting towards Sustainability)
View Full-Text   |   Download PDF [2045 KB, uploaded 10 November 2017]   |  

Abstract

Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA) method and Radial Basis Function Neural Network (RBFN). To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO) is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators. View Full-Text
Keywords: bivariate ARIMA; hybrid method; price forecasting; wind generator; wavelet transform bivariate ARIMA; hybrid method; price forecasting; wind generator; wavelet transform
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

Talari, S.; Shafie-khah, M.; Osório, G.J.; Wang, F.; Heidari, A.; Catalão, J.P.S. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators. Sustainability 2017, 9, 2065.

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