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Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset
Open AccessArticle

Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems

1
German Aerospace Center (DLR), Institute of Solar Research, Wankelstrasse 5, 70563 Stuttgart, Germany
2
German Aerospace Center (DLR), Institute of Solar Research, Linder Höhe, 51147 Cologne, Germany
*
Author to whom correspondence should be addressed.
Energies 2020, 13(6), 1517; https://doi.org/10.3390/en13061517
Received: 18 December 2019 / Revised: 23 February 2020 / Accepted: 5 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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. View Full-Text
Keywords: renewable systems; storage; dispatch; optimization; energy markets; machine learning renewable systems; storage; dispatch; optimization; energy markets; machine learning
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do Amaral Burghi, A.C.; Hirsch, T.; Pitz-Paal, R. Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems. Energies 2020, 13, 1517.

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