Forecasting Uncertainty Related to Ramps of Wind Power Production

International audienceThe continuous improvement of the accuracy of wind power forecasts is motivated by the increasing wind power integration. Today forecasters are challenged in providing forecasts able to handle extreme situations. This paper presents two methods focusing on forecasting large and sharp variations in power output of a wind farm called ramps. The fi rst one provides probabilistic forecasts using large temporal scales information about ramps. The second method uses ensembles to generate con dence intervals allowing to better estimate the timing of ramps. The two methods are tested and results are given for a real case study

A Novel Methodology for comparison of different wind power ramp characterization approaches

International audienceWind power forecasting is recognized as a means to facilitate large scale wind power integration into power systems. Recently, focus has been given on developing dedicated short-term forecasting approaches for the case of large and sharp wind power variations, so-called ramps. Accurate forecasts of specific ramp characteristics (e.g. timing, probability of occurrence, etc) are important since the related forecast errors may lead to potentially large power imbalances, with high impact to the power system. Various works about ramps' periodicity or predictability have led to the development of new characterization approaches. The evaluation of these approaches has often been neglected, leading to potentially irrelevant conclusions on ramps characteristics, or ineffective forecasting approaches. In this work, we propose a comprehensive framework for evaluating and comparing different characterization approaches of wind power ramps

The value of schedule update frequency on distributed energy storage performance in renewable energy integration

International audienceThis paper describes preliminary findings of research on the use of Distributed Energy Storage devices for Renewable Energy integration. The primary objective is to describe the effect of different storage scheduling strategies, and namely the benefits from intraday intraday scheduling on the storage performance in renewable energy integration. Optimal schedules of Distributed Energy Storage devices are based on forecasts of Renewable Energy production, local consumption and prices, along with other criteria. These forecasts tend to have a higher uncertainty for higher time horizons, resulting in losses due to errors and to the underutilization of the assets. The use of frequent schedules updates can reduce part of these drawbacks and this paper aims at quantifying this reduction. The importance of the quantification of the benefits arising from different rescheduling frequencies lies in its influence on the ICT infrastructure necessary to implement it and its cost

Impact of PV forecasts uncertainty on batteries management in microgrids

International audienceThis paper is motivated by the question of the impact that uncertainty in PV forecasts has in forecast-based battery schedule optimisation in microgrids in presence of network constraints. We examine a specific case where forecast accuracy can be impacted by the lack of enough data history to finetune the forecasting models. This situation can be expected to be frequent with new PV installations. A probabilistic PV production forecast algorithm is used in combination with a battery schedule optimisation algorithm. The size of the learning dataset of the forecast algorithm is modified in order to simulate the application of the system to new plants and the impact on the performance in the management of the battery is analyse

Evaluation of the level of prediction errors and sub-hourly variability of PV and wind generation in a future with a large amount of renewables

International audienceIn this paper we propose a method for the simulation of errors in renewable energy sources generation forecasting (photovoltaic and wind) for use in power system planning studies. The proposed methodology relies on 5 elementary simulation steps. The first step is the simulation of photovoltaic plant and wind farm power production, with a sufficient spatial and temporal resolution (few km and hourly time step), the second is the simulation of the localisation of production sites, the third step is the generation of forecast errors using historic data of numerical weather predictions, and the last step is the simulation of intra-hourly variations of photovoltaic production. Finally, it is discussed how these simulation tools can assist the evaluation of the required tertiary reserves in a power system with a large share of renewable energies into the mix

Diffusion methods for wind power ramp detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38679-4_9Proceedings of 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Part IThe prediction and management of wind power ramps is currently receiving large attention as it is a crucial issue for both system operators and wind farm managers. However, this is still an issue far from being solved and in this work we will address it as a classification problem working with delay vectors of the wind power time series and applying local Mahalanobis K-NN search with metrics derived from Anisotropic Diffusion methods. The resulting procedures clearly outperform a random baseline method and yield good sensitivity but more work is needed to improve on specificity and, hence, precision.With partial support from Spain's grant TIN2010-21575- C02-01 and the UAM-ADIC Chair for Machine Learning. The rst author is also supported by an FPI-UAM grant and kindly thanks the Applied Mathematics Department of Yale University for receiving her during her visits. The second author is supported by the FPU-MEC grant AP2008-00167

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