Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems

Ana Carolina do Amaral Burghi, Tobias Hirsch and Robert Pitz-Paal
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Ana Carolina do Amaral Burghi: German Aerospace Center (DLR), Institute of Solar Research, Wankelstrasse 5, 70563 Stuttgart, Germany
Tobias Hirsch: German Aerospace Center (DLR), Institute of Solar Research, Wankelstrasse 5, 70563 Stuttgart, Germany
Robert Pitz-Paal: German Aerospace Center (DLR), Institute of Solar Research, Linder Höhe, 51147 Cologne, Germany

Energies, 2020, vol. 13, issue 6, 1-21

Abstract: Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

Keywords: renewable systems; storage; dispatch; optimization; energy markets; machine learning (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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