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Robust Optimal Aiming Strategies in Concentrated Solar Tower Power Plants

Sascha Kuhnke(kuhnke***at***math2.rwth-aachen.de)
Pascal Richter(pascal.richter***at***kit.edu)
Fynn Kepp(fynn.kepp***at***rwth-aachen.de)
Jeff Cumpston(jeff.cumpston***at***avt.rwth-aachen.de)
Christina Büsing(buesing***at***math2.rwth-aachen.de)

Abstract: A concentrated solar tower power plant consists of a receiver mounted atop of a central tower and a field of movable mirrors called heliostats. The heliostats concentrate solar radiation onto the receiver where a fluid is heated to produce electricity in a conventional thermodynamic cycle. Aiming strategies are used to assign each heliostat to an individual aim point on the receiver such that a given flux distribution on the receiver surface is reached. As uncertainties in the tracking of the heliostats exist, aiming strategies are applied that use large safety margins to avoid dangerously high flux concentrations on the receiver. This approach leads to an inefficient use of the power plant and thus economical losses. In this paper, we consider advanced methods to include these uncertainties into the design of efficient aiming strategies. To this end, we present a mixed-integer linear programming (MILP) formulation for the optimization of aiming strategies based on Gamma-robustness. In a case study, we show that the Gamma-robust optimization approach yields solutions with strong objective values and thus high economical benefits while maintaining a high degree of safety. Compared to non-robust solutions, the Gamma-robust solutions achieve better objective values while guaranteeing the same degree of safety.

Keywords: Solar thermal power, Aiming strategy, Robust optimization, Mixed-integer linear programming, Uncertainty quantification

Category 1: Robust Optimization

Citation: Robust Optimal Aiming Strategies in Concentrated Solar Tower Power Plants, Lehrstuhl II für Mathematik, RWTH Aachen University, Pontdriesch 10-12, 52062 Aachen, Germany, 09/2019

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Entry Submitted: 09/16/2019
Entry Accepted: 09/16/2019
Entry Last Modified: 09/16/2019

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