Article
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Artificial Learning Dispatch Planning for Flexible Renewable Energy Systems
Version 1
: Received: 20 January 2020 / Approved: 21 January 2020 / Online: 21 January 2020 (10:20:43 CET)
How to cite: do Amaral Burghi, A.C.; Hirsch, T.; Pitz-Paal, R. Artificial Learning Dispatch Planning for Flexible Renewable Energy Systems. Preprints 2020, 2020010237 do Amaral Burghi, A.C.; Hirsch, T.; Pitz-Paal, R. Artificial Learning Dispatch Planning for Flexible Renewable Energy Systems. Preprints 2020, 2020010237
Abstract
Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in market conditions prediction 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 whole-sale 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 optimum 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, emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.
Keywords
renewable systems; storage; dispatch; optimization; energy markets; machine learning
Subject
Engineering, Energy and Fuel Technology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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