Kickoff of offshore wind power in China: playoffs for China wind power development

Year 2010 is the significant year of offshore wind power development in China. The first national offshore wind power project is connected to the grid, and the first round of concession projects marks the strong support from central government. It is foreseeable that offshore wind power capacity in China will expand rapidly in the future, and the understanding pattern of it is crucial for analyzing the overall wind market in China and global offshore wind power development. This paper firstly provides an overview of global offshore wind power development, then in China, including historical installation, potential of resources, demonstration and concession projects, and target of development. Based on this, analysis on current policies related to offshore wind power and their implementation, current wind farm developers and turbine manufacturers of China's offshore wind industry is done. All the previous analysis generates complete evaluation of current status and some issues and trends of China offshore wind power development, based on which some policy recommendations for sustainable development of offshore wind power are made

What to expect from a greater geographic dispersion of wind farms? - A risk portfolio approach

The UK, like many other industrialised countries, is committed to reducing greenhouse gas emissions under the Kyoto Protocol. To achieve this goal the UK is increasingly turning towards wind power as a source of emissions free energy. However, the variable nature of wind power generation makes it an unreliable energy source, especially at higher rates of penetration. Likewise the aim of this paper is to measure the potential reduction in wind power variability that could be realised as a result of geographically dispersing the location of wind farm sites. To achieve this aim wind speed data will be used to simulate two scenarios. The first scenario involves locating a total of 2.7 gigawatts (GW) of wind power capacity in a single location within the UK while the second scenario consists of sharing the same amount of capacity amongst four different locations. A risk portfolio approach as used in financial appraisals is then applied in the second scenario to decide upon the allocation of wind power capacity, amongst the four wind farm sites, that succeeds in minimising overall variability for a given level of wind power generation. The findings of this paper indicate that reductions in the order of 36% in wind power variability are possible as a result of distributing wind power capacity

Using conditional kernel density estimation for wind power density forecasting

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms

Wind Energy: Harnessing the Wind to Generate Electricity

Key facts: - The spinning blades of wind turbines convert energy from the wind's motion into electricity. - Installed US wind power plants had 6,740 megawatts (MW) of electrical capacity in 2004, enough to serve 1.6 million households. - The cost of wind power is competitive with other energy sources. With the Production Tax Credit of 1.9 cents per kilowatt-hour (kWh), wind power costs between $0.03 and$0.06 per kWh, a huge decline from $0.80 per kWh in 1980. - Wind power is one of the fastest growing energy sources in the United States: its capacity increased on average 25 percent per year from 1990 to 2003. Although capacity increased by only 6 percent in 2004, due to the expiration of the Production Tax Credit, the American Wind Energy Association anticipates that over 2000 MW of wind power capacity will be added in 2005, more than in any previous year

Benefits of spatio-temporal modelling for short term wind power forecasting at both individual and aggregated levels

The share of wind energy in total installed power capacity has grown rapidly in recent years around the world. Producing accurate and reliable forecasts of wind power production, together with a quantification of the uncertainty, is essential to optimally integrate wind energy into power systems. We build spatio-temporal models for wind power generation and obtain full probabilistic forecasts from 15 minutes to 5 hours ahead. Detailed analysis of the forecast performances on the individual wind farms and aggregated wind power are provided. We show that it is possible to improve the results of forecasting aggregated wind power by utilizing spatio-temporal correlations among individual wind farms. Furthermore, spatio-temporal models have the advantage of being able to produce spatially out-of-sample forecasts. We evaluate the predictions on a data set from wind farms in western Denmark and compare the spatio-temporal model with an autoregressive model containing a common autoregressive parameter for all wind farms, identifying the specific cases when it is important to have a spatio-temporal model instead of a temporal one. This case study demonstrates that it is possible to obtain fast and accurate forecasts of wind power generation at wind farms where data is available, but also at a larger portfolio including wind farms at new locations. The results and the methodologies are relevant for wind power forecasts across the globe as well as for spatial-temporal modelling in general

Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events