Session: 05-02: Concentrating Solar Power I -- Heliostats and Trough Receivers

Paper Number: 142277

142277 - Techno-Economic Analysis of a Two-Stage Heliostat 

Abstract: 

This project analyzed the two-stage heliostat concept in which the first stage, the tracking stage, faces towards the equator (south in the northern hemisphere) and consists of mirrors mounted on a common drive that move to track the sun. The concept involves a single set of drives controlling multiple small area mirrors that are segmented into facets by the unit's structure. Rays from the sun reflect off the tracking mirror and onto the second stage, the concentrating stage, which faces away from the equator (north in the northern hemisphere). The concentrating stage consists of stationary mirrors that each have a unique angle to direct rays towards a small-area, high-flux, point-focused receiver. The two stages form mirror pairs and a single heliostat unit consists of multiple pairs that run along a horizontal axis, east to west. By splitting the collection and concentrating process into two stages, multiple small, inexpensive mirrors can share a structure and be controlled by a single drive in the tracking stage. Modeling techniques specifically relevant to this two-stage heliostat concept were developed.  A field-level modeling approach was taken in which an entire field composed of many heliostat units can be simulated in a computationally efficient manner.  A unit refers to a set of heliostats that are coupled with a single set of drives.  This field-level model does not explicitly consider unit-level models such as blocking and shading; these types of losses are not typically important in conventional, single-stage heliostat technology but become more important and also more difficult to resolve in a multi-stage technology.  The unit-level losses are integrated into the field-level model through a correlation referred to as an efficiency modifier that is based on detailed unit-level modeling.  This approach is referred to as the two-model approach; the development and demonstration of this two-model approach for a multi-stage heliostat technology is a key outcome of this work. 

 

The field-level model is set up so that initially an oversized field is simulated and then heliostat units are removed based on their annual energy production in order to generate the highest performing field for a given set of geometric parameters that provides a specified design day power.  Because many units must be simulated at several days during the year in order estimate the annual energy production it is necessary to use many millions of rays to obtain estimates of annual energy production for each unit in the very large field.  In order to overcome this issue, the field reduction procedure fits a smooth second order curve fit to annual energy production as a function of position in the field.  This has the effect of smoothing out the noise that is otherwise caused by the Monte Carlo ray tracing technique which is large for small ray numbers.  The annual energy fit approach enables the selection of a properly sized, high-performing field using orders of magnitude fewer rays than would otherwise be possible and the development of this approach is a second key outcome of this work.  

 

The field level model using the two-model approach and the annual energy fit is sufficiently computationally fast that it can be coupled directly to a genetic optimization algorithm in order to optimize the geometric parameters in order to achieve the “best” design according to the lowest cost per unit of collected design day power.  The cost modeling that underlies the optimization is a simple, scaling type analysis.  Two optimal designs (250 kW and 500 kW) were selected for a much more detailed Design for Manufacture and Assembly (DFMA) analysis.  The DFMA work is used to adjust the scaling analysis so that the two cost models match at these two design points.

Presenting Author: Ty Neises NREL

Presenting Author Biography: Ty is a senior research engineer at NREL with a focus on thermal energy systems.

Authors:

Michael Wagner University of Wisconsin - Madison
Ty Neises NREL
Gregory Nellis University of Wisconsin - Madison
Ty Glisczinski University of Wisconsin - Madison
Sammie Lundin University of Wisconsin - Madison