Optimization of constant power control of wind turbines to provide power reserves

In several countries, the wind power penetration increased tremendously in the last years. As the current wind turbines do not participate in frequency control nor reserve provision, this may compromise the proper functioning of the primary control and the provision of power reserves. If no actions are taken, increasing levels of wind penetration may result in serious problems concerning the stable operation of the power system. This paper focuses on the provision of power reserves by wind turbines. For this service, the constant power control strategy is chosen as control strategy, as it gives a constant power output and has the ability to provide power reserves. In this way, the wind turbine behaves more like a conventional power plant. The choice of the power reference value is crucial as it determines whether or not a stable operation of the wind turbine is possible and power reserves can be provided. In this paper, an algorithm is proposed to obtain the range of possible reference values. By means of simulations, the optimal reference value to provide power reserves with a single wind turbine is obtained. Also, reserve provision in a wind farm is investigated. It is shown that the provision of power reserves with wind turbines using the constant power strategy is possible, especially in wind farms

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

Predictive control of wind turbines by considering wind speed forecasting techniques

A wind turbine system is operated such that the points of wind rotor curve and electrical generator curve coincide. In order to obtain maximum power output of a wind turbine generator system, it is necessary to drive the wind turbine at an optimal rotor speed for a particular wind speed. A Maximum Power Point Tracking (MPPT) controller is used for this purpose. In fixed-pitch variable-speed wind turbines, wind-rotor parameters are fixed and the restoring torque of the generator needs to be adjusted to maintain optimum rotor speed at a particular wind speed for optimum power output. In turbulent wind environment, control of variable-speed fixed-pitch wind turbine systems to continuously operate at the maximum power points becomes difficult due to fluctuation of wind speeds. In this paper, wind speed forecasting techniques will be considered for predictive optimum control system of wind turbines

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

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

Wind turbine condition assessment through power curve copula modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors

Combined hydro-wind generation bids in a pool-based electricity market

Present regulatory trends are promoting the irect participation of wind energy in electricity markets. The final result of these markets sets the production scheduling for the operation time, including a power commitment from the wind generators. However, wind resources are uncertain, and the final power delivered usually differs from the initial power committed. This imbalance produces an overcost in the system, which must be paid by those who produce it, e.g., wind generators among others. As a result, wind farm revenue decreases, but it could increase by allowing wind farms to submit their bids to the markets together with a hydro generating unit, which may easily modify its production according to the expected imbalance. This paper presents a stochastic optimization technique that maximizes the joint profit of hydro and wind generators in a pool-based electricity market, taking into account the uncertainty of wind power prediction.En prens