Predictive storage strategy for optimal design of hybrid CSP-PV plants with immersion heater.

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Date: Apr. 2021
From: Solar Energy(Vol. 218)
Publisher: Elsevier Science Publishers
Document Type: Report; Brief article
Length: 331 words

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Abstract :

Keywords Storage strategy; Model predictive control; Linear programming; Hybrid CSP-PV power plant; Immersion heater Highlights * Modeling a hybrid CSP-PV power plant with Immersion heater. * Model predictive control for storage strategy using linear programming. * Temperature-dependent power block efficiency. * Demonstration with realistic weather data. * Optimal design for CSP, PV and storage size leads to an annual gain of 14%. Abstract A hybrid solar power plant effectively combines the two main advantages of solar power plants: concentrated solar power (CSP) with a cheap thermal storage system and photovoltaic (PV) with cheap electricity production. In a hybrid plant, both systems are coupled with the thermal storage, where an immersion heater can transfer the PV energy into thermal energy. A real-time storage strategy is developed using model predictive control considering the future energy tariff and future weather conditions. The efficiency of the power block is considered as quadratic function in dependency of the bulb temperature. As strategy the optimization problem is formulated as linear program. The methods are tested in a realistic scenario for a hybrid CSP-PV power plant with real weather data and different tariffs. Furthermore, on the basis of the best strategy, the optimal design for CSP, PV and storage size is investigated. In comparison to the state of the art (heuristic) optimization we gain 14 % by using a predictive control strategy in combination with an optimal power plant configuration. We show that the storage strategy not only impacts the achievable plant output but also very strongly the subsystem sizing. It can be seen that the plant configuration is massively influenced by the storage control scheme. Author Affiliation: (a) Karlsruhe Institute of Technology (KIT), Steinbuch Centre for Computing, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany (b) RWTH Aachen University, Research Group for Continuous Optimization, Department of Mathematics, Templergraben 55, 52064 Aachen, Germany * Corresponding author. Article History: Received 18 August 2020; Revised 19 October 2020; Accepted 1 November 2020 Byline: P. Richter [pascal.richter@kit.edu] (a,*), T. Trimborn (b), L. Aldenhoff (b)

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Gale Document Number: GALE|A657002920