Negnevitsky, M, Wind Power Forecasting Techniques, Alternative Energy and Shale Gas Encyclopedia, John Wiley & Sons, Inc, JH Lehr and J Keeley (ed), United States of America, pp. 10-19. ISBN 978-0-470-89441-5 (2016) [Research Book Chapter]
Copyright 2016 John Wiley & Sons, Inc.
Wind power is currently the fastest growing power generation sector in the world-worldwide growth in wind power generation has been at 40% a year for the last 10 years. With this rapid growth of generation, new challenges are being introduced to power markets. Wind power is an intermittent source. For example, at a wind site in Tasmania, Australia, the average change in mean wind speed vectors over a 2.5- minute period is about 44.4% and the average change in maximum wind speeds over the same period is 55.0%. This demonstrates that the wind speed and its direction can change rapidly by a large amount over a very short period of time. Great variability in wind farm generation has both technical and commercial implications for the efficient planning and operation of power systems.
Large wind farms tend to use large wind turbines. Large turbines have a significant rotational inertia when considering a short time frame. As a result, wind patterns shorter than 10 seconds usually have a negligible effect on the turbine output, and it is a standard practice in wind generation to neglect short-term gusts. However, trends over several minutes are of vital importance.
In order to accommodate the increase in wind power generation, we need to change the way that wind generation is scheduled. Both utilities and electricity market operators will be required to forecast the wind generation to improve system performance. Wind generation will have to be bid into the market like any other generation. In order to handle these requirements, power system operators will need to have better wind forecasting models and tools.
Forecasting is a vital part of business planning in todayís competitive environment, regardless of the field in which you work. To be ready for a situation before it occurs gives a distinct advantage over the competition. Wind prediction is complex due to the windís high degree of volatility and deviation. This means that standard time length definitions for power engineering do not strictly apply. The phrase "shortterm forecasting," in the case of wind forecasting, normally means periods from minutes out to half an hour while longterm forecasting means periods from several hours out to a few days.
This difference between the two forecasting time periods is important when it comes to creating a prediction system. Three main classes of different techniques have been used for wind power forecasting. These are numerical weather prediction (NWP) methods, statistical methods, and methods based on applications of artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFISs). TheNWP methods are dominant for forecasts over 10 hours ahead. However, statistical and ANN-based methods appear to be more accurate over shorter periods (minutes to a few hours). They are also much simpler than the NWP methods.
|Item Type:||Research Book Chapter|
|Keywords:||wind power, forecasting techniques|
|Research Group:||Electrical and Electronic Engineering|
|Research Field:||Power and Energy Systems Engineering (excl. Renewable Power)|
|Objective Group:||Energy Storage, Distribution and Supply|
|Objective Field:||Energy Services and Utilities|
|Author:||Negnevitsky, M (Professor Michael Negnevitsky)|
|Downloads:||1 View Download Statistics|
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