Day-Ahead Forecasting of the Theoretical and Actual Wind Power Generation in Energy-Constrained Island Systems

Konstantinos Moustris () and Dimitrios Zafirakis
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Konstantinos Moustris: Air Pollution Lab., Department of Mechanical Engineering, University of West Attica, 250 Thivon & P. Ralli Street, 12241 Athens, Greece
Dimitrios Zafirakis: Soft Energy Applications & Environmental Protection Lab., Mechanical Engineering Department, University of West Attica, 250 Thivon & P. Ralli Street, 12241 Athens, Greece

Energies, 2023, vol. 16, issue 12, 1-18

Abstract: Grid operators of islands with limited system tolerance are often challenged by the need to curtail wind energy in order to maintain system stability and security of supply. At the same time, and in the absence of storage facilities and/or other means of flexibility such as demand-side management, wind park owners face the problem of rejected wind energy production that varies considerably within the year. In the prospect of a more dynamic market operation in island grids, estimation of the anticipated wind energy curtailments may allow the evaluation of different options for wind park owners, such as short-term leasing of energy storage and/or direct, bilateral power purchase agreements with flexible demand entities. To enable such options, effective wind energy forecasting is necessary not only in terms of theoretical production, but also in terms of actual production being absorbed by the system. In this direction, the current research works on the prediction of day-ahead wind energy production in island grids, aiming to generate both theoretical (expected) and actual wind power forecasts. To that end, we use artificial neural networks for the development of different day-ahead forecasting models of hourly granularity, and we then test their performance in a large-scale non-interconnected island system, where annual wind energy curtailments for local wind parks may exceed 25% of the respective theoretical yield. Our results indicate that models developed provide a fair accuracy of day-ahead wind energy predictions, which is further elaborated by initiating a discussion on the emergence of alternative actor schemes in similar systems.

Keywords: artificial neural networks; day-ahead forecasting; wind power dispatching; wind energy curtailments; island systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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