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In this work the transient simulations of four hybrid solar tower power plant concepts with open-volumetric receiver technology for a location in Barstow-Daggett, USA, are presented. The open-volumetric receiver uses ambient air as heat transfer fluid and the hybridization is realized with a gas turbine. The Rankine cycle is heated by solar-heated air and/or by the gas turbine's flue gases. The plant can be operated in solar-only, hybrid parallel or combined cycle-only mode as well as in any intermediate load levels where the solar portion can vary between 0 to 100%.
The simulated plant is based on the configuration of a solar-hybrid power tower project, which is in planning for a site in Northern Algeria. The meteorological data for Barstow-Daggett was taken from the software meteonorm. The solar power tower simulation tool has been developed in the simulation environment MATLAB/Simulink and is validated.
Large scale central receiver systems typically deploy between thousands to more than a hundred thousand heliostats. During solar operation, each heliostat is aligned individually in such a way that the overall surface normal bisects the angle between the sun’s position and the aim point coordinate on the receiver. Due to various tracking error sources, achieving accurate alignment ≤1 mrad for all the heliostats with respect to the aim points on the receiver without a calibration system can be regarded as unrealistic. Therefore, a calibration system is necessary not only to improve the aiming accuracy for achieving desired flux distributions but also to reduce or eliminate spillage. An overview of current larger-scale central receiver systems (CRS), tracking error sources and the basic requirements of an ideal calibration system is presented. Leading up to the main topic, a description of general and specific terms on the topics heliostat calibration and tracking control clarifies the terminology used in this work. Various figures illustrate the signal flows along various typical components as well as the corresponding monitoring or measuring devices that indicate or measure along the signal (or effect) chain. The numerous calibration systems are described in detail and classified in groups. Two tables allow the juxtaposition of the calibration methods for a better comparison. In an assessment, the advantages and disadvantages of individual calibration methods are presented.
This paper describes the modeling of a high-temperature storage system for an existing solar tower power plant with open volumetric receiver technology, which uses air as heat transfer medium (HTF). The storage system model has been developed in the simulation environment Matlab/Simulink®. The storage type under investigation is a packed bed thermal energy storage system which has the characteristics of a regenerator. Thermal energy can be stored and discharged as required via the HTF air. The air mass flow distribution is controlled by valves, and the mass flow by two blowers. The thermal storage operation strategy has a direct and significant impact on the energetic and economic efficiency of the solar tower power plants.
This work presents a basic forecast tool for predicting direct normal irradiance (DNI) in hourly resolution, which the Solar-Institut Jülich (SIJ) is developing within a research project. The DNI forecast data shall be used for a parabolic trough collector (PTC) system with a concrete thermal energy storage (C-TES) located at the company KEAN Soft Drinks Ltd in Limassol, Cyprus. On a daily basis, 24-hour DNI prediction data in hourly resolution shall be automatically produced using free or very low-cost weather forecast data as input. The purpose of the DNI forecast tool is to automatically transfer the DNI forecast data on a daily basis to a main control unit (MCU). The MCU automatically makes a smart decision on the operation mode of the PTC system such as steam production mode and/or C-TES charging mode. The DNI forecast tool was evaluated using historical data of measured DNI from an on-site weather station, which was compared to the DNI forecast data. The DNI forecast tool was tested using data from 56 days between January and March 2022, which included days with a strong variation in DNI due to cloud passages. For the evaluation of the DNI forecast reliability, three categories were created and the forecast data was sorted accordingly. The result was that the DNI forecast tool has a reliability of 71.4 % based on the tested days. The result fulfils SIJ’s aim to achieve a reliability of around 70 %, but SIJ aims to still improve the DNI forecast quality.