Short-Range Forecasting for Energy

Haupt, Sue Ellen

doi:10.1007/978-3-319-68418-5_7

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…In order to obtain an optimization of RR resources activation, it has been investigated the availability of innovative predictive techniques able to reduce the forecasting error, since a correct forecast would lead to an increase in the quality of the power supply and to a reduction of the procurements cost. A literature review [32][33][34][35][36][37][38][39][40] highlighted that so called nowcasting techniques could improve forecast accuracy in the time horizon from 1 to few hours in advance. These methodologies combine real-time measurements on the current state of the system (e.g., measured irradiance, wind speed, satellite imagines, radar data, etc.)…”

Section: Identification Of Alternative Techniques To Be Investigated For Error Reductionmentioning

confidence: 99%

Replacement Reserve for the Italian Power System and Electricity Market

et al. 2020

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Over the last years, power systems around the globe experienced deep changes in their operation, mainly induced by the widespread of Intermittent Renewable Energy Sources (IRES). These changes involved a review of market and operational rules, in the direction of a stronger integration. At European level, this integration is in progress, driven by the new European guidelines and network codes, which deal with multiple issues, from market design to operational security. In this framework, the project TERRE (Trans European Replacement Reserve Exchange) is aimed at the realization of a European central platform, called LIBRA, for the exchange of balancing resources and, in particular, for the activation of the procured Replacement Reserve (RR) resources. The Italian Transmission System Operator (TSO), TERNA, is a participant of the project and it is testing new methodologies for the sizing of RR and its required activation throughout the TERRE process. The aim of the new methodologies is to find areas of potential improvement in the sizing of RR requirements and activation, which open up the possibility for a reduction of the procurement cost, without endangering the security of the power system. This paper describes a new RR sizing methodology, proposed by TERNA, which is based on a persistence method, showing its results on real data and highlighting key advantages and potential limitations of this approach. In order to overcome these limitations, a literature review on alternative approaches has been carried out, identifying nowcasting techniques as a relevant alternative for the very short term forecast horizon. These one could be further investigated and tested in the future, using the proposed persistence method as a benchmark.

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Section: Identification Of Alternative Techniques To Be Investigated For Error Reductionmentioning

confidence: 99%

Replacement Reserve for the Italian Power System and Electricity Market

et al. 2020

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“…Short term (daily, hourly or less) forecasts of wind or solar power, based on weather forecasts, need to be highly accurate to allow the output of individual sites to be carefully managed (e.g. Giebel et al, 2011;De Felice et al, 2015;Haupt, 2018). Similarly, climatological risk studies, for example to allow financing for individual site development, or for planning future transmission/distribution grid requirements, can also require accurate transformations across timescales (e.g.…”

Section: Forecasting Wind and Solar Power Generationmentioning

confidence: 99%

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Bett¹,

Thornton²,

Troccoli³

et al. 2019

Preprint

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We demonstrate the current levels of skill for seasonal forecasts of wind and irradiance in Europe, using forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that a simple linear regression-based method, using the hindcast and forecast ensemble means, provides a straightforward approach to produce reliable probabilistic seasonal forecasts in the cases where there is skill. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, providing opportunities to improve the skill in some cases.We further demonstrate that taking a seasonal average and a regional (e.g. national) average means that wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance): the detailed non-linear transformations from meteorological variables to energy variables, which can be essential for precision on weather forecasting timescales and for climatological studies, are usually not necessary when producing seasonal forecasts of these average quantities.Together, our results demonstrate that, in the cases where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, it can be very straightforward to forecast seasonal mean wind and solar power generation based on those climate variables, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

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“…Statistical methods work best for intra-hourly forecasts and up to three-hour ahead forecasts. Physical methods are used primarily for forecasting output beyond three to six hours, with some exceptions in solar, such as the application of total sky imagers for short-term forecasting for cloud prediction (Haupt 2018). In general, statistical models perform better for wind energy than for solar energy over short time horizons and physical models show better results for both wind and solar over long time horizons (Widén et al 2015), because statistical models do not do a good job of predicting cloud coverage.…”

Section: Box 21 Examples Of Reducing System Costs Through Forecastingmentioning

confidence: 99%

“…A classical persistence model is typically used for intra-hour forecasts, particularly for wind, where accuracy can reach acceptable levels. Over the first 15-45 minutes, it is often difficult to surpass the accuracy of the persistence forecast (Haupt 2018). These models are rarely used for longer-term forecasts, as they rapidly lose predictive power when time horizons increase.…”

Section: Typical Usementioning

confidence: 99%

Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy

2019

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Short-Range Forecasting for Energy

Cited by 9 publications

References 33 publications

Replacement Reserve for the Italian Power System and Electricity Market

Replacement Reserve for the Italian Power System and Electricity Market

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Using Forecasting Systems to Reduce Cost and Improve Dispatch of Variable Renewable Energy

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