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Research Papers

FRED: The Flexible Renewable Energy System Dispatch Optimizer

[+] Author and Article Information
Ana Carolina do Amaral Burghi

German Aerospace Center (DLR),
Institute of Solar Research,
Wankelstrasse 5, 70563 Stuttgart, Germany
e-mail: Ana.doAmaralBurghi@dlr.de

Tobias Hirsch

German Aerospace Center (DLR),
Institute of Solar Research,
Wankelstrasse 5, 70563 Stuttgart, Germany
e-mail: Tobias.Hirsch@dlr.de

Robert Pitz-Paal

German Aerospace Center (DLR),
Institute of Solar Research,
Linder Höhe, 51147 Cologne, Germany
e-mail: Robert.Pitz-Paal@dlr.de

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the Journal of Solar Energy Engineering: Including Wind Energy and Building Energy Conservation. Manuscript received January 31, 2019; final manuscript received March 27, 2019; published online May 2, 2019. Assoc. Editor: Isaac Mahderekal.

J. Sol. Energy Eng 141(5), 051009 (May 02, 2019) (17 pages) Paper No: SOL-19-1042; doi: 10.1115/1.4043518 History: Received January 31, 2019; Accepted April 11, 2019

Due to their thermal storage capability, concentrated solar power (CSP) plants have flexibility on electricity dispatch, being able to participate in balancing power markets. The development of an optimum electricity delivery schedule should be fast to react to updated forecast and the dynamic electricity markets, apart from considering best operational practices, as it brings significant cost reductions and improvement in plant performance. Therefore, dispatch optimization tools should combine financial and operational optimization with acceptable computational time. In this context, an innovative dispatch optimization algorithm used to derive a CSP plant operation schedule is presented. FRED is a heuristic rule-based algorithm used to optimize financial income while considering plant best operational practices. Simulations performed with a CSP plant tower model following FRED optimization strategy show the possibility of improved financial results with fast dispatch planning, ensuring the importance of this technology in the pathway to a highly renewable energy mix.

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Figures

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Fig. 1

Scheme of FRED algorithm

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Fig. 2

Input and output scheme of solar field model

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Fig. 3

Input and output scheme of power block model

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Fig. 4

Scheme of optimization algorithm rules sequence

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Fig. 5

Financial input data used in simulation presented in Sec. 3.1, with labels of hourly prioritization inside the plot area

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Fig. 6

PB input schedule, SF output thermal flow, and TES level along the optimization sequence (part I)

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Fig. 7

PB input schedule, SF output thermal flow, and TES level along the optimization sequence (part II)

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Fig. 8

Economical contribution of each rule to final schedule (case 1)

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Fig. 9

Financial input data used in simulation presented in Sec. 3.2, with labels of hourly prioritization inside the plot area

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Fig. 10

PB input schedule, SF output thermal flow, and TES level of some specific rules

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Fig. 11

Economical contribution of each rule to final schedule (case 2)

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Fig. 12

Mean hourly Spanish market price with standard deviation for 20162

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Fig. 13

Annual results of FRED optimization and solar-driven strategy with perfect forecast as weather input: comparison between delivered electricity and thermal dumped energy (a) and financial income (b)

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Fig. 14

Annual results of FRED optimization and solar-driven strategy with persistence forecast as weather input: comparison between delivered electricity and thermal dumped energy (a) and financial income (b)

Tables

Errata

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