1
1 INTRODUCTION
Many countries and regions are introducing policies aimed at reducing the environmental
footprint from the energy sector and increasing the use of renewable energy. In the United States,
a number of initiatives have been taken at the state level, from renewable portfolio standards
(RPSs) and renewable energy certificates (RECs) [1], to regional greenhouse gas emission
control schemes [2]. Within the U.S. Federal government, new energy and environmental
policies and goals are also being crafted, and these are likely to increase the use of renewable
energy substantially. The European Union is pursuing implementation of its ambitious 20/20/20
targets, which aim (by 2020) to reduce greenhouse gas emissions by 20% (as compared to 1990),
increase the amount of renewable energy to 20% of the energy supply, and reduce the overall
energy consumption by 20% through energy efficiency [3].
With the current focus on energy and the environment, efficient integration of renewable
energy into the electric power system is becoming increasingly important. In a recent report, the
U.S. Department of Energy (DOE) describes a model-based scenario, in which wind energy
provides 20% of the U.S. electricity demand in 2030 [4]. The report discusses a set of technical
and economic challenges that have to be overcome for this scenario to unfold. In Europe, several
countries already have a high penetration of wind power (i.e., in the range of 7 to 20% of
electricity consumption in countries such as Germany, Spain, Portugal, and Denmark). The rapid
growth in installed wind power capacity is expected to continue in the United States as well as in
Europe [4],[5],[6],[7],[8].
A large-scale introduction of wind power causes a number of challenges for electricity
market and power system operators who will have to deal with the variability and uncertainty in
wind power generation when making their scheduling and dispatch decisions. Wind power
forecasting (WPF) is frequently identified as an important tool to address the variability and
uncertainty in wind power and to more efficiently operate power systems with large wind power
penetrations [4],[5],[9],[10]. Moreover, in a market environment, the wind power contribution to
the generation portofolio becomes important in determining the daily and hourly prices, as
variations in the estimated wind power will influence the clearing prices for both energy and
operating reserves.
With the increasing penetration of wind power, WPF is quickly becoming an important
topic for the electric power industry. System operators (SOs), generating companies (GENCOs),
and regulators all support efforts to develop better, more reliable and accurate forecasting
models. Wind farm owners and operators also benefit from better wind power prediction to
support competitive participation in electricity markets against more stable and dispatchable
energy sources [11]. In general, WPF can be used for a number of purposes, such as: generation
and transmission maintenance planning, determination of operating reserve requirements, unit
commitment, economic dispatch, energy storage optimization (e.g., pumped hydro storage), and
energy trading.
The objective of this report is to review and analyze state-of-the-art WPF models and
their application to power systems operations. We first give a detailed description of the
我的内容管理
展开
