Torben Skov Nielsen

Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models

Short-term wind power prediction is a primary requirement for efficient large-scale integration of wind generation in power systems and electricity markets. The choice of an appropriate prediction model among the numerous available models is not trivial, and has to be based on an objective evaluation of model performance. This paper proposes a standardized protocol for the evaluation of short-term windpower prediction systems. A number of reference prediction models are also described, and their use for performance comparison is analysed. The use of the protocol is demonstrated, using results from both on-shore and offshore wind farms. The work was developed in the frame of the Anemos project (EU R&D project) where the protocol has been used to evaluate more than 10 prediction systems.

Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time-varying regression model. In a second step, time-series models, i.e., ARMA and Holt-Winters, are applied to account for residual autocorrelation and seasonal dynamics. Empirical results are presented for out-of-sample forecasts of day-ahead prices in the Western Danish price area of Nord Pools Elspot, during a two year period covering 2010-2011. These results clearly demonstrate the practical benefits of accounting for the complex influence of these explanatory variables.

From wind ensembles to probabilistic information about future wind power production -- results from an actual application

Meteorological ensemble forecasts aim at quantifying the uncertainty of the future development of the weather by supplying several possible scenarios of this development. Here we address the use of such scenarios in probabilistic forecasting of wind power production. Specifically, for each forecast horizon we aim at supplying quantiles of the wind power production conditional on the information available at the time at which the forecast is generated. This involves: (i) transformation of meteorological ensemble forecasts into wind power ensemble forecasts and (ii) calculation of quantiles based on the wind power ensemble forecasts. Given measurements of power production, representing a region or a single wind farm, we have developed methods applicable for these two steps. While (ii) should in principle be a simple task we found that the probabilistic information contained in the wind power ensembles from (i) cannot be used directly and therefore both (i) and (ii) requires statistical modelling. Based on these findings an demo-application, supplying quantile forecasts for operational horizons of up to approximately 6 days, was developed for two utilities participating in a common project. The application use ECMWF-ensembles. One setup corresponds to an offshore wind farm (Nysted, Denmark) and one corresponds to regional forecasting (Western Denmark). In the paper we analyze the results obtained from 8 months of actual operation of this system. It is concluded that the demo-application produce reliable forecasts. The average difference between the 75% and 25% quantile forecasts exceeds 50% of the installed capacity for horizons longer than approximately 4 days for the wind farm setup. For the regional forecasts the corresponding horizon is not reached within 7 days, which is the maximum horizon available. The ability of the demo-application to differentiate between situations with low and high uncertainty is analysed. Also, the relation between the forecasted uncertainty and the actual skill of a point forecast is analysed. A satisfactory agreement is observed

Local linear regression with adaptive orthogonal fitting for the wind power application

Abstract Short-term forecasting of wind generation requires a model of the function for the conversion of meteorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assumption is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive estimation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covariance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomial regression with orthogonal fit to accurately approximate the target regression, even though it may hardly be visible when calculating error criteria against corrupted data.

Temperature prediction at critical points in district heating systems

Current methodologies for the optimal operation of district heating systems use model predictive control. Accurate forecasting of the water temperature at critical points is crucial for meeting constraints related to consumers while minimizing the production costs for the heat supplier. A new forecasting methodology based on conditional finite impulse response (cFIR) models is introduced, for which model coefficients are replaced by coefficient functions of the water flux at the supply point and of the time of day, allowing for nonlinear variations of the time delays. Appropriate estimation methods for both are described. Results are given for the test case of the Roskilde district heating system over a period of more than 6 years. The advantages of the proposed forecasting methodology in terms of a higher forecast accuracy, its use for simulation purposes, or alternatively for better understanding transfer functions of district heating systems, are clearly shown.

On On-line Systems for Short-term Forecasting for Energy Systems

The paper describes experiences with developing on-line computer systems for short-term forecasting of wind power production and heat consumption in district heating networks. The computer systems are briefly described and some general aspects regarding system modeling with the purpose of forecasting are discussed. One consequence of the approach used is that the stochastic properties of the forecast errors can not be inferred from the models generating the forecasts. With the purpose of using the stochastic properties as input to formal OR-models we discuss how these can be modeled.

ARX-Models with Parameter Variations Estimated by Local Fitting

Abstract The estimation in ARX-models in which the parameters are replaced by smooth functions are studied by simulation. The estimation method is based on the ideas of locally weighted regression. Kernel estimates (local constants) are in general inferior to local quadratic estimates. In the case of correlated input sequences the kernel estimates are rather unreliable. Local quadratic estimates are in general quite reliable. This also holds when the input sequence are correlated, at least to the extend typical for the application, which is the modelling of temperatures in a district heating system.

Evaluation of energy storage system to support Danish island of Bornholm power grid

This paper presents a real-time evaluation and simulation approach of energy storage system (ESS) based on large renewable-based electricity generation, which can be used for grid support. The ESS is designed to maintain power quality as a primary regulation, while the conventional generation units handle the secondary frequency regulation to mitigate ramping issues. The real time models of Bornholm distribution grid, which is the combination of an aggregated wind power generation and the energy storage system (ESS) has been used to test the system and control approach in a real time grid simulator to identify the improvement of the grid support capability. The interactive simulation platform with real-time energy forecasting data running online with a link to the Bornholm power system data are being used to measure and validate the system performance with and without storage after a disturbance.