Dynamic Modelling of Large Autonomous power systems with high penetration from renewable (wind & hydro) power sources

International audienceThis paper presents a model for the transient stability analysis of isolated power systems including various conventional (thermal, gas, diesel) and renewable (hydro, wind) power units. The objective is to assess the impact of a high integration from renewables and to focus on the interaction between hydro units - which have a special dynamic behaviour, and wind turbines. Detailed models for the power system components are developed. Emphasis is given to the representation of different hydro power plant structures. An algorithm is proposed for the identification of the unknown power system parameters. The examined case study is the one of the power system of New Caledonia, where various types of hydro plants and a wind farm are installed. The developed model permits to define penetration limits for the renewable sources, to define rules for the safe operation of the system and finally it contributes to the dimensioning of new power plant installations

Short-Term load forecasting using a neuro-fuzzy model based on entropy maximisation

International audienceThe paper presents a new short-term load forecasting approach based on dynamic fuzzy logic modelling. The developed model produces forecasts for the next 48 hours, which are updated every hour. Such a sliding window scheme is different than conventional models that operate usually once a day. The paper emphasizes on developing appropriate learning and on-line adaptation schemes based on the maximal entropy principle. In contrast to the traditional approach, such schemes permit to avoid overfitting of the model to the data. Thus, the ability of the model to predict new data (generalisation) is maximized. The architecture of the model is selected using non-linear optimisation techniques such the non-linear Simplex. The model has been developed in the frame of the EU research project More-Care and implemented for on-line use at the islands of Crete and Madeira. Results from the case studies are presented showing the efficiency of the approach

Forecasting of wind parks production by dynamic fuzzy models with optimal generalisation capacity

International audienceOn-line forecasting of the power output of wind farms is of major importance for a reliable and secure large-scale integration of wind power, especially under liberalized energy market environment. This paper presents such a prediction tool that receives on-line SCADA measurements, as well as numerical weather predictions as input, to predict the power production of wind parks 48 hours ahead. The prediction tool integrates models based on adaptive fuzzy-neural networks configured either for short-term or long-term forecasting. In each case, the model architecture is selected through non-linear optimization techniques. By this way the accuracy of the model on out of sample data (generalization) is optimized. The forecasting models are integrated in the MORE-CARE Energy Management Software (EMS) software developed in the frame of a European research project. In this EMS platform, wind forecasts and confidence intervals are used by economic dispatch and unit commitment functions. The paper presents detailed results on the performance of the developed models on a real wind farm using HIRLAM numerical weather predictions as input

Wind into power, from ANEMOS to SafeWind

Nowadays, European countries like Germany, Spain and Denmark already have a significant share of wind generation in their electricity generation mix. In parallel, the large-scale integration of wind power is also taking place at a rapid pace in other European countries. The European Wind Energy Association (EWEA) foresees 230,000MW of wind capacity installed by 2020 (of which 40,000MW will be off-shore) able to produce 600TWh per year and to cover 14-18% of EU electricity demand

Wind Power forecasting using fuzzy neural networks enhanced with on-line prediction risk assessment

International audienceThe paper presents an advanced wind forecasting system that uses on-line SCAnA measurements, as well as numerical weather predictions (NWP) as input, to predict the power production of wind park8 48 hours ahead. The prediction system integrates models based on adaptive fuzzy-neural networks configured either for short-term (1-10 hours) or longterm (1-48 hours) forecasting. The paper presents detailed oneyear evaluation results ofthe models on the case study oflreland, where the output of several wind farms is predicted using HIRLAM meteorological forecasts as input A method for the online estimation of confidence intervals of the forecasts is developed together with an appropriate index for assessing online the risk due to the inaccuracy of the numerical weather predictions

Best practice in the use of short-term forecasting. Results from 2 workshops organised by the Pow'Wow project

This paper was intended to be the updated version of the similar paper presented at EWEC 2007 in Milan after the second workshop had been held. However, the planned second workshop was moved after the abstract deadline to after EWEC 2008, therefore we will not repeat last year's paper, but just summarise the main findings.International audienceShort-term forecasting of wind power for about 48 hours in advance is an established technique by now. Any utility getting over a few percent wind power penetration is buying a system or a service on the market. But which system? Also, once the system is installed and running day-to-day in the control room or on the trading floor, what is the best way to use the predictions? Which pitfalls are there to be aware of, and how can one maximise the value of the short-term forecasts

Conditional prediction intervals of wind power generation

International audienceA generic method for the providing of prediction intervals of wind power generation is described. Prediction intervals complement the more common wind power point forecasts, by giving a range of potential outcomes for a given probability, their so-called nominal coverage rate. Ideally they inform of the situation-specific uncertainty of point forecasts. In order to avoid a restrictive assumption on the shape of forecast error distributions, focus is given to an empirical and nonparametric approach named adapted resampling. This approach employs a fuzzy inference model that permits to integrate expertise on the characteristics of prediction errors for providing conditional interval forecasts. By simultaneously generating prediction intervals with various nominal coverage rates, one obtains full predictive distributions of wind generation. Adapted resampling is applied here to the case of an onshore Danish wind farm, for which three point forecasting methods are considered as input. The probabilistic forecasts generated are evaluated based on their reliability and sharpness, while compared to forecasts based on quantile regression and the climatology benchmark. The operational application of adapted resampling to the case of a large number of wind farms in Europe and Australia among others is finally discussed

Best Practice in short-term Forecasting. A users Guide

http://powwow.risoe.dk/publ/giebelkariniotakis-bestpracticeinstf_156_ewec2007fullpaper.pdfInternational audienceShort-term forecasting of wind power for about 48 hours in advance is an established technique by now. Any utility getting over a few percent wind power penetration is buying a system or a service on the market. However, once the system is installed and running day-to-day in the control room or on the trading floor, what is the best way to use the predictions? Which pitfalls are there to be aware of, and how can one maximise the value of the short-term forecasts? For this purpose, a workshop was organised in Delft in October 2006. The aim of the paper is to present the results of this study and analyse how practices are influenced by the initial choice of the prediction approach or prediction system, the level of penetration, the intended use of the forecasts, the acceptance operators may have for wind energy, the power system management tools or functions where the forecasts are used, and many more 1

Strategies for Wind Power Trading in Sequential Short-Term Electricity Markets

International audienceIn several countries, independent wind power producers have the possibility to participate in short-term electricity markets for trading their production. However, the limited predictability of the wind resource may lead to differences between produced and contracted energy, thus generating energy imbalances. This may result in the payment of imbalance penalties, which leads to a reduction of the competitiveness of wind power generation. This paper develops a methodology, suitable for independent wind power producers, that permits them to participate in an efficient way simultaneously in several sequential electricity markets, namely day-ahead and intraday markets. The considered intraday market takes place through a continuous trading mechanism. The imbalance cost reduction related to the adjustment participation in the intraday market is assessed using a real-world test case

An advanced On-line Wind Resource Prediction system for the optimal management of wind park

International audienceThe paper presents an advanced wind forecasting system that uses on-line SCADA measurements, as well as numerical weather predictions as input to predict the power production of wind parks 48 hours ahead. The prediction tool integrates models based on adaptive fuzzy-neural networks configured either for short-term or long-term forecasting. In each case, the model architecture is selected through non-linear optimization techniques. The forecasting system is integrated within the MORE-CARE EMS software developed in the frame of a European research project. Within this on-line platform, the forecasting module provides forecasts and confidence intervals for the wind farms in a power system, which can be directly used by economic dispatch and unit commitment functions. The platform can run also as a stand-alone application destined only for wind forecasting. Detailed results are presented on the performance of the developed models on a real wind farm using HIRLAM numerical weather predictions as input