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

The impact of available data history on the performance of photovoltaïc generation forecasting models

International audienceThe continuous growth of solar power capacity raises challenges to distribution system operators regarding power quality and security of supply. Network management systems must be enhanced with short-term forecasting functionalities able to predict the solar plants production in the next hours or days. The provision of individual forecasts for each solar plant on the network is often required. To that purpose, historical measurements are needed for tuning the forecasting models. The situation is challenging for new plants for which long history of measurements is not yet available. In that case, models able to provide accurate production forecasts based on few historical production data, are required. In this paper, we investigate the performance of state-of-the-art short-term PV forecasting models as a function of the historical data available for tuning. We compare the results with those obtained by a reference model whose utilization does not require more than one day of past production data. Our analysis relies on production data from a 200 kWc solar plant located in the south-east of France. It shows that satisfactory performances can be expected from state-of-the-art models, when calibrated with no more than one or two weeks of training data

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

Caractérisation et prédiction probabiliste des variations brusques et importantes de la production éolienne

Today, wind energy is the fastest growing renewable energy source. The variable and partially controllable nature of wind power production causes difficulties in the management of power systems. Forecasts of wind power production 2-3 days ahead can facilitate its integration. Though, particular situations result in unsatisfactory prediction accuracy. Errors in forecasting the timing of large and sharp variations of wind power can result in large energy imbalances, with a negative impact on the management of a power system. The objective of this thesis is to propose approaches to characterize such variations, to forecast their timing, and to estimate the associated uncertainty. First, we study different alternatives in the characterization of wind power variations. We propose an edge model to represent the random nature of edge occurrence, along with representing appropriately the bounded and non-stationary aspects of the wind power production process. From simulations, we make a parametric study to evaluate and compare the performances of different filters and multi-scale edge detection approaches. Then, we propose a probabilistic forecasting approach of edge occurrence and timing, based on numerical weather prediction ensembles. Their conversion into power provides an ensemble of wind power scenarios from which the different forecast timings of an edge are combined. The associated uncertainty is represented through temporal confidence intervals with conditionally estimated probabilities of occurrence. We evaluate the reliability and resolution of those estimations based on power measurements from various real world case studies.L'énergie éolienne est aujourd'hui la source d'énergie renouvelable en plus forte expansion. Le caractère variable et partiellement contrôlable de sa production complexifie la gestion du système électrique. L'utilisation dans divers processus de décision, de prédictions du niveau de production à des horizons de 2-3 jours, permet une meilleure intégration de cette ressource. Certaines situations donnent néanmoins lieu à des performances de prédiction insatisfaisantes. Des erreurs dans la prédiction de l'instant d'apparition de variations brusques et importantes de la production, peuvent être responsables d'importants déséquilibres énergétiques, et avoir un impact négatif sur la gestion du système électrique. L'objectif de cette thèse est de proposer des approches permettant d'une part de caractériser ces variations, et d'autre part de prédire et d'estimer l'incertitude dans l'instant de leur apparition. Dans un premier temps, nous étudions différentes formes de caractérisation de ces variations. Nous proposons un modèle de rupture permettant de représenter le caractère aléatoire dans la proximité des ruptures d'un signal, tout en tenant compte des aspects borné et non-stationnaire du processus de production. A partir de simulations issues de ce modèle, nous réalisons une étude paramétrique destinée à évaluer et comparer les performances de différents filtres et approches multi-échelles de détection. Dans un deuxième temps, nous proposons une approche de prédiction probabiliste de l'instant d'apparition d'une rupture, reposant sur l'utilisation de prévisions météorologiques ensemblistes. Leur conversion en puissance fournit différents scénarii de la production, à partir desquels sont agrégées les prédictions de l'instant d'apparition d'une rupture. L'incertitude associée est représentée à l'aide d'intervalles de confiance temporels et de probabilités estimées conditionnellement. Nous évaluons la fiabilité et la finesse de ces estimations sur la base de mesures de production provenant de différentes fermes éoliennes

Characterization and probabilistic forecasting of wind power production ramps

L'énergie éolienne est aujourd'hui la source d'énergie renouvelable en plus forte expansion. Le caractère variable et partiellement contrôlable de sa production complexifie la gestion du système électrique. L'utilisation dans divers processus de décision, de prédictions du niveau de production à des horizons de 2-3 jours, permet une meilleure intégration de cette ressource. Certaines situations donnent néanmoins lieu à des performances de prédiction insatisfaisantes. Des erreurs dans la prédiction de l'instant d'apparition de variations brusques et importantes de la production, peuvent être responsables d'importants déséquilibres énergétiques, et avoir un impact négatif sur la gestion du système électrique. L'objectif de cette thèse est de proposer des approches permettant d'une part de caractériser ces variations, et d'autre part de prédire et d'estimer l'incertitude dans l'instant de leur apparition. Dans un premier temps, nous étudions différentes formes de caractérisation de ces variations. Nous proposons un modèle de rupture permettant de représenter le caractère aléatoire dans la proximité des ruptures d'un signal, tout en tenant compte des aspects borné et non-stationnaire du processus de production. A partir de simulations issues de ce modèle, nous réalisons une étude paramétrique destinée à évaluer et comparer les performances de différents filtres et approches multi-échelles de détection. Dans un deuxième temps, nous proposons une approche de prédiction probabiliste de l'instant d'apparition d'une rupture, reposant sur l'utilisation de prévisions météorologiques ensemblistes. Leur conversion en puissance fournit différents scénarii de la production, à partir desquels sont agrégées les prédictions de l'instant d'apparition d'une rupture. L'incertitude associée est représentée à l'aide d'intervalles de confiance temporels et de probabilités estimées conditionnellement. Nous évaluons la fiabilité et la finesse de ces estimations sur la base de mesures de production provenant de différentes fermes éoliennes.Today, wind energy is the fastest growing renewable energy source. The variable and partially controllable nature of wind power production causes difficulties in the management of power systems. Forecasts of wind power production 2-3 days ahead can facilitate its integration. Though, particular situations result in unsatisfactory prediction accuracy. Errors in forecasting the timing of large and sharp variations of wind power can result in large energy imbalances, with a negative impact on the management of a power system. The objective of this thesis is to propose approaches to characterize such variations, to forecast their timing, and to estimate the associated uncertainty. First, we study different alternatives in the characterization of wind power variations. We propose an edge model to represent the random nature of edge occurrence, along with representing appropriately the bounded and non-stationary aspects of the wind power production process. From simulations, we make a parametric study to evaluate and compare the performances of different filters and multi-scale edge detection approaches. Then, we propose a probabilistic forecasting approach of edge occurrence and timing, based on numerical weather prediction ensembles. Their conversion into power provides an ensemble of wind power scenarios from which the different forecast timings of an edge are combined. The associated uncertainty is represented through temporal confidence intervals with conditionally estimated probabilities of occurrence. We evaluate the reliability and resolution of those estimations based on power measurements from various real world case studies

Forecasting ramps of wind power production with numerical weather prediction ensembles

International audienceToday, there is a growing interest in developing short-term wind power forecasting tools able to provide reliable information about particular, so-called 'extreme' situations. One of them is the large and sharp variation of the production a wind farm can experience within a few hours called ramp event. Developing forecast information specially dedicated to ramps is of primary interest because of both the difficulties that usual models have to predict and the potential risk they represent in the management of a power system. First, we propose a methodology to characterize ramps of wind power production with a derivative filtering approach derived from the edge detection literature. Then we investigate the skill of numerical weather prediction ensembles to make probabilistic forecasts of ramp occurrence. Through conditioning probability forecasts of ramp occurrence to the number of ensemble members forecasting a ramp in time intervals, we show how ensembles can be used to provide reliable forecasts of ramps with sharpness. Our study relies on 18months of wind power measures from an 8MW wind farm located in France and forecasts ensemble of 51 members from the Ensemble Prediction System of the European Center for Medium-Range Weather Forecasts

Sensitivity analysis in the technical potential assessment of onshore wind and ground solar photovoltaic power resources at regional scale

International audiencePotential assessment has served various objectives in the development of renewable energies. However, the prospective nature of this type of assessment sometimes makes it difficult to evaluate and compare estimation results based on different data and modeling. To facilitate this comparison, uncertainty estimates need to be systematically provided. Since potential assessment sometimes relies on numerous parameters, this may first require determining the most important inputs to focus on. In this paper, we propose a sensitivity analysis methodology based on Sobol indices so as to identify the main inputs from a nonlinear assessment model. We illustrate the proposed methodology through analyzing sensitivity in an onshore wind and ground solar photovoltaic (PV) potential assessment covering two French regions. As a result, we show that, when estimating the potential of these renewable energy sources, parameters defining surface availability are more prevalent than those related to technology