Due to the fluctuating nature of wind resource, a power producer participating in liberalized electricity market is subject penalties related regulation costs. Accurate forecasts generation are therefore paramount for reducing such and thus maximizing revenue. Despite fact that increasing accuracy spot may reduce penalties, this paper shows that, if accompanied with information on their uncertainty, i.e., form predictive distributions, then can be basis defining advanced strategies...

In this paper, an advanced model, based on recurrent high order neural networks, is developed for the prediction of power output profile a wind park. This model outperforms simple methods like persistence, as well classical in literature. The architecture forecasting optimised automatically by new algorithm, that substitutes usually applied trial-and-error method. Finally, online implementation into control system optimal operation and management real autonomous wind-diesel system, presented.

Short-term wind power prediction is a primary requirement for efficient large-scale integration of generation in systems and electricity markets. The choice an appropriate model among the numerous available models not trivial, has to be based on objective evaluation performance. This paper proposes standardized protocol short-term windpower systems. A number reference are also described, their use performance comparison analysed. demonstrated, using results from both on-shore offshore farms....

A generic method for the providing of prediction intervals wind power generation is described. Prediction complement more common point forecasts, by giving a range potential outcomes given probability, their so-called nominal coverage rate. Ideally they inform situation-specific uncertainty forecasts. In order to avoid restrictive assumption on shape forecast error distributions, focus an empirical and nonparametric approach named adapted resampling. This employs fuzzy inference model that...

In recent years, the penetration of photovoltaic (PV) generation in energy mix several countries has significantly increased thanks to policies favoring development renewables and also significant cost reduction this specific technology. The PV power production process is characterized by variability, as it depends on meteorological conditions, which brings new challenges system operators. To address these challenges, important be able observe anticipate levels. Accurate forecasting output...

Abstract Predictions of wind power production for horizons up to 48–72 h ahead comprise a highly valuable input the methods daily management or trading generation. Today, users predictions are not only provided with point predictions, which estimates conditional expectation generation each look‐ahead time, but also uncertainty given by probabilistic forecasts. In order avoid assumptions on shape predictive distributions, these produced from non‐parametric methods, and then take form single...

In the first part of this two-part paper, detailed dynamic equations for power system and wind energy conversion (WECS) components their synthesis to a unified model are presented. This is basis creating simulation software able perform transient stability analysis isolated diesel-wind turbine systems accurate assessment interaction. Approximations in various component models, when necessary, remain between limits that do not affect accuracy performed. A new general multimachine also...

Wind power forecasting tools have been developed for some time. The majority of such usually provides single-valued (spot) predictions. Such predictions limits the use decision-making under uncertainty. In this paper we propose a method producing complete predictive probability density function (PDF). is based on kernel estimation techniques. preliminary results show that levels with state art one while being fast and PDF. were obtained through real data from three French wind farms.

ABSTRACT Today, 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 the large and sharp variation production farm can experience within few hours called ramp event . Developing forecast specially dedicated ramps primary because both difficulties that usual models have predict potential risk they represent management system. First, we propose methodology...

This paper presents an optimization model for Home Energy Management Systems from aggregator's standpoint. The aggregator manages a set of resources such as PV, electrochemical batteries and thermal energy storage by means electric water heaters. Resources are managed in order to participate the day-ahead local flexibility markets, also considering grid constraint support at Point Common Coupling. resulting is Mixed-Integer Linear Programming problem which objective minimize operation costs...

Around the world wind energy is starting to become a major provider in electricity markets, as well participating ancillary services markets help maintain grid stability. The reliability of system operations and smooth integration into has been strongly supported by years improvement weather power forecasting systems. Deterministic forecasts are still predominant utility practice although truly optimal decisions risk hedging only possible with adoption uncertainty forecasts. One main...

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose probabilistic spatio-temporal model for production that exploits information from neighboring plants. The provides complete future probability density function very short-term horizons (0-6 h). method based on quantile regression L <sub...

Deriving decisions from data typically involves a sequential process with two components, forecasting and optimization. Forecasting models learn by minimizing loss function that stands as proxy for task-specific costs (e.g., trading, scheduling) without considering the downstream optimization, which in practice creates performance bottleneck obscures impact of on decisions. This work proposes single data-driven module leverages structure optimization component directly learns policy...