Operating Reserve Quantification Using Prediction Intervals of Wind Power: An Integrated Probabilistic Forecasting and Decision Methodology

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next-day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a 2-week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climates can be valuable when planning next-day energy production, in the usage of de-icing systems and for site safety.

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Probabilistic forecasting of wind power production losses in cold climates: A case study

10.5194/wes-2017-28 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jennie P. Söderman ◽

Heiner Körnich ◽

Esbjörn Olsson ◽

Hans Bergström ◽

Anna Sjöblom

Keyword(s):

Wind Power ◽

Energy Production ◽

Cold Climate ◽

Forecast Skill ◽

Cold Climates ◽

Probabilistic Forecasting ◽

Probabilistic Forecasts ◽

Forecasting System ◽

Production Losses

Abstract. The problem of icing on wind turbines in cold climates is addressed using probabilistic forecasting to improve next- day forecasts of icing and related production losses. A case study of probabilistic forecasts was generated for a two- week period. Uncertainties in initial and boundary conditions are represented with an ensemble forecasting system, while uncertainties in the spatial representation are included with a neighbourhood method. Using probabilistic forecasting instead of one single forecast was shown to improve the forecast skill of the ice-related production loss forecasts and hence the icing forecasts. The spread of the multiple forecasts can be used as an estimate of the forecast uncertainty and of the likelihood for icing and severe production losses. Best results, both in terms of forecast skill and forecasted uncertainty, were achieved using both the ensemble forecast and the neighbourhood method combined. This demonstrates that the application of probabilistic forecasting for wind power in cold climate can be valuable when planning next-day energy production, in the usage of de-icing systems, and for site safety.

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Evaluating the impact of wind power probabilistic forecasting on very-short-term generation scheduling for Wind-Storage Combined Generation System

2018 IEEE/IAS 54th Industrial and Commercial Power Systems Technical Conference (I&CPS) ◽

10.1109/icps.2018.8369972 ◽

2018 ◽

Author(s):

Jie Shi ◽

Guoyu Zhang ◽

Wei-Jen Lee

Keyword(s):

Wind Power ◽

Probabilistic Forecasting ◽

Generation System ◽

Short Term ◽

Generation Scheduling ◽

The Impact

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Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method

Energies ◽

10.3390/en13184643 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4643 ◽

Cited By ~ 1

Author(s):

Vasileios Evangelopoulos ◽

Panagiotis Karafotis ◽

Pavlos Georgilakis

Keyword(s):

Power Distribution ◽

Mean Squared Error ◽

Distribution Network ◽

Load Forecasting ◽

Distribution Networks ◽

Prediction Intervals ◽

Probabilistic Forecasting ◽

Power Distribution Networks ◽

Spatial Error ◽

Electric Loads

The efficient spatial load forecasting (SLF) is of high interest for the planning of power distribution networks, mainly in areas with high rates of urbanization. The ever-present spatial error of SLF arises the need for probabilistic assessment of the long-term point forecasts. This paper introduces a probabilistic SLF framework with prediction intervals, which is based on a hierarchical trending method. More specifically, the proposed hierarchical trending method predicts the magnitude of future electric loads, while the planners’ knowledge is used to improve the allocation of future electric loads, as well as to define the year of introduction of new loads. Subsequently, the spatial error is calculated by means of root-mean-squared error along the service territory, based on which the construction of the prediction intervals of the probabilistic forecasting part takes place. The proposed probabilistic SLF is introduced to serve as a decision-making tool for regional planners and distribution network operators. The proposed method is tested on a real-world distribution network located in the region of Attica, Athens, Greece. The findings prove that the proposed method shows high spatial accuracy and reduces the spatial error compared to a business-as-usual approach.

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