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

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Talari, S (Author) Other; Shafie Khah, M (Author) Other; Osorio, GJ (Author) Other; Wang, F (Author) Other; Heidari, A (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: Sustainability

Vol. 9

Final page: 2065

ISSN: 2071-1050

Publisher: MDPI

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00N-7RK

DOI: 10.3390/su9112065

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13