Aiming clusters of heliostats over solar receivers for distributing heat flux using one variable per group
Graphical abstract
Introduction
The last few decades have seen an advancement of research on solar thermal processes for heat and power generation. It has made it possible for these technologies to move forward, implementing systems predictable and dispatchable. Nevertheless, the consensus among solar energy researchers is that these technologies require additional improvement to gain a competitive advantage over competitors in the non-renewable energy industry [1]. In this regard, over the past decade, the main challenges in advancing solar tower technology lie in lowering its operating costs by over 75% through different pathways [2]. As such, an adequate aiming strategy has the potential to reduce up to 50% heat flux peaks with limited drawbacks on the optical efficiency. It is achieved by repositioning the aiming points of those heliostats that are closer to the central receiver [3]. In consequence, it promotes a more homogeneous distribution of the heat flux that reduces the fatigue cycling induced in the structure of the receiver. Among other improvement techniques, optimizing the aiming and tracking strategies for heliostats is under development. For solar central receivers, peak thermal stresses caused by localized short-term high heat fluxes represent a significant limitation on the operational performance [4,5]. It is a characteristic that is inherently related to the aiming strategy, but this also depends on the environmental conditions around the process. The shadowing effect caused by the clouds over the heliostat field can impede the operating conditions of the solar receiver tower, as this effect imposes the highest variations of heat flux over the receiver [6]. The main goal of the heliostat aiming strategy is to maintain the intercepting solar radiation within the allowable limits of thermal stresses [7] and to avoid reaching undesired operation temperatures. Inappropriate heliostat aiming management can either degrade the chemical composition of the heat transfer fluid (e.g., molten salts) [8] or obstruct the pipelines of the central receiver. Studies in the existing literature infer that optimization techniques are commonly used among all other methods used to determine aiming point locations. For instance, Refs. [9] shows that an appropriate aiming strategy could maximize the thermal output of the receiver while considering the heat flux density constraints, which is given by the temperature and the mass flow rate of the molten salt through the central receiver. They analyzed two cases for aiming point movements, namely vertically constrained and free move, and the second case was shown to exhibit no improvement. Authors in Ref. [10] branched the optimization method to work with smaller subproblems. It was concluded that the best way to address the aiming point calculation is to consider the overlapping effect among the sectors that each subproblem involves. In Ref. [11] it was implemented an integer programming solver to optimize the collected energy efficiency of a central receiver near real-time scales. This methodology can cope with cloud shading effects for different heliostat distributions. The same authors have proposed an improved method through a bi-objective optimization that is not constrained by the preset aiming points [12]. Other approaches are based on the location of the aiming points by taking into account a theoretical approximation of the reflected beam radius given by each heliostat over the central receiver [13]. For example, in Ref. [14] it was developed a heliostat movement strategy based on the reflected beam radius by using the k-values for each mirror. Subsequently, the solar field was divided into three zones, and the same k-value was given to the two regions located close to the central receiver thereby, allowing the heliostat within these regions to aim closer to the edges of the receiver. Conversely, a larger k-value as assigned to heliostats located in the third region, which imposes a constraint that avoids its aiming point to go far from the receiver’s equator.
Previous aiming point methodologies developed by the authors of this research have demonstrated that the process control strategies can be adapted to manage aiming strategies for solar central receivers [[15], [16], [17]]. This work goes a step further in this approach by developing a new group behavior for aiming points using a single manipulated variable per group. Moreover, it has been conceived to reduce the number of groups to be analyzed in the solar field; thus, it makes the process more manageable, thereby overcoming challenges such as multivariable tuning of control strategies and the dynamic identification of the entire process. The paper is organized as follows. First, the implemented solar field model is described in Section 2. Second, the details of the group behavior implementation are given in Section 3. Finally, the following sections belong to the heat flux analysis and the discussion on the proposed methodology.
Section snippets
Solar field model
The solar field implemented in this investigation follows the plant given by Ref. [9]. The main characteristics used in the simulation are shown in Table 1. The peak incident power on the image plane over the receiver is obtained using Equations (1), (2).
Fig. 1 shows atmospheric attenuation (), shading and blocking (), incident angle cosine (), and overall optical () efficiencies by solar noon of the day obtained from Solar Pilot [18]. The flux
Virtual control valve design for aiming heliostats over a central receiver
Control strategies for continuous processes are usually designed to execute corrective actions over devices such as control valves. There are three main kinds of control valves: linear, equal percentage, and quick opening. The first two options are only used to implement regulatory control. The selection of one alternative depends on the nature of the process. For instance, conventional nonlinear processes usually require equal percentage valves.
Heat flux variation over the receiver due to a step-change in
As discussed in the previous section, the overarching goal is to set a single variable per group, , to determine the position of each aiming point. However, this approach must have some flexibility and robustness for subsequent analysis based on the manipulated input from a control strategy. Thus, this section shows the effect that a step-change in for group 11 (See Fig. 5) has over its panel of the central receiver. In this case, decreases from 100% to 15%, which means that the
Discussion
The implementation of continuous control strategies adapted to aim heliostats on a central solar receiver is still at an early stage of development. One of the notable points that will allow its acceptance within real plants is a clear and precise formulation of a mathematical structure that allows a group of heliostats to work in a coordinated way through the manipulation of a single variable for such a group. Section 3 of this research paper explicitly proposes a methodology that allows an
Conclusions
This paper proposes a methodology for the implementation of the aiming behavior within a group of heliostats that allow them to work in a coordinated manner. It is achieved by considering the relative position between the heliostats and the central receiver tower. This development promotes the use of a single manipulated variable per group, which is in favor of the subsequent analysis of the dynamic identification methodologies for this type of process, and the tuning of continuous control
Funding
The Chilean Government funded this research through a postdoctoral project supported by the Comisión Nacional de Investigación Científica y Tecnológica (CONICYT), the Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT), and Universidad Técnica Federico Santa María, postdoctoral grant number 3190542. The authors also express their gratitude for the financial support from ANID/Fondap/15110019 “Solar Energy Research Center”-SERC-Chile.
CRediT authorship contribution statement
Jesús García: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition. Rodrigo Barraza: Conceptualization, Methodology, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Supervision, Project administration, Funding acquisition. Yen Chean Soo Too: Validation, Formal analysis, Writing - review & editing.
Declaration of competing interest
All the authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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2022, Renewable EnergyCitation Excerpt :Nevertheless, as shown by the controller output, it was not highly disturbed. It is noteworthy that the shape of the average HF at the middle of each panel is due to the configuration of the aiming control valve [33]. Heliostats that were far from the receiver moved more slowly than did the closer ones.
Coupled optical-thermal-stress characteristics of a multi-tube external molten salt receiver for the next generation concentrating solar power
2021, EnergyCitation Excerpt :The current studies on the aiming strategy can be classified into two types. One is to homogenize heat flux distributions and reduce the peak heat flux, which aims at mitigating receiver's thermal stress [4,17,31]. Another is to limit the peak heat flux within the allowable heat flux calculated based on allowable stress [22,24,32].
Flux-feedback as a fast alternative to control groups of aiming points in molten salt power towers
2021, Solar EnergyCitation Excerpt :This is just one possible configuration of groups of heliostats that can be formed, in which four distinct areas of the panel were established to project flux onto them in a differentiated manner. Nonetheless, other strategies to create groups and paths for the groups can be explored, as shown in Garcia et al. (García et al., 2020). The control strategy in this work is thus composed of 192 PI-Controllers, which in this work use flux-feedback information from the model in STRAL to determine each heliostat’s aiming point.