Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization Based Hybrid Method

Wen-Yeau Chang

doi:10.3390/en6094879

Abstract

High penetration of wind power in the electricity system provides many challenges to power system operators, mainly due to the unpredictability and variability of wind power generation. Although wind energy may not be dispatched, an accurate forecasting method of wind speed and power generation can help power system operators reduce the risk of an unreliable electricity supply. This paper proposes an enhanced particle swarm optimization (EPSO) based hybrid forecasting method for short-term wind power forecasting. The hybrid forecasting method combines the persistence method, the back propagation neural network, and the radial basis function (RBF) neural network. The EPSO algorithm is employed to optimize the weight coefficients in the hybrid forecasting method. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a wind energy conversion system (WECS) installed on the Taichung coast of Taiwan. Comparisons of forecasting performance are made with the individual forecasting methods. Good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Highlights

The gradual shortage of oil and worldwide awareness of environmental issues highlights the exploitation of renewable energy technologies
Wind power forecasting was computed using the historical wind power and wind speed data every 10 min for a 2400 kW wind energy conversion system (WECS) installed in Taichung, on the coast of Taiwan
The proposed wind power forecast method is compared with the radial basis function (RBF) neural network method, the persistence method, and the back propagation neural network method

Summary

Introduction

The gradual shortage of oil and worldwide awareness of environmental issues highlights the exploitation of renewable energy technologies. Wind power is one of the most attractive renewable energy applications because of its high efficiency and low pollution [1]. Wind power is the fastest growing source of renewable energy. In mid-2012, the capacity of the wind energy conversion system (WECS) reached 254 GW, with 273 GW expected for a full year. Global wind capacity has grown by 7% in the past six months and by 16.4% annually [2]. Large-scale wind penetration requires answers to a lot of problems such as low voltage ride through [3], spinning reserve capacity scheduling, utility grid control and operation, and ancillary service scheduling. An accurate wind forecasting method is known as an efficient tool to overcome these problems [4,5]

Methods

Results

Conclusion