Sectorial reflectance-based cleaning policy of heliostats for Solar Tower power plants

RENEWABLE ENERGY

卷:166

Article

Truong-Ba, Huy^1,3  Cholette, Michael E.¹  Picotti, Giovanni^1,2  Steinberg, Theodore A.¹  Manzolini, Giampaolo² 

[1] Queensland Univ Technol, 2 George St, Brisbane, Qld 4001, Australia

[2] Politecn Milan, Dipartimento Energia, Via Lambruschini 4, I-20156 Milan, Italy

[3] Int Univ VNUHCM, Ho Chi Minh City, Vietnam

关键词: Heliostat cleaning optimization;    Approximate dynamic programming;    Reflectance;    Cleaning;    Heliostats;    Solar tower;    Concentrating solar power;   

DOI  :  10.1016/j.renene.2020.11.129

来源: Elsevier

【 摘 要 】

For concentrating Solar Tower (ST) power plants, heliostats must be cleaned to maintain high productivity, but this comes at the cost of cleaning expenditures. Striking the correct balance remains challenging, due in part to the fact that soiling losses are location-dependent, stochastic, seasonal, and spatially inhomogeneous across the field. In this paper, novel reflectance-based cleaning policies are developed that trigger and prioritize cleaning of different solar field sectors based on reflectance measurements. In contrast to existing approaches, these policies have the potential to mitigate the effect of stochastic soiling losses and allocate finite cleaning resources by considering the spatial inhomogeneity of soiling. The optimization of the policy is conducted using the approximate Markov Decision Process (MDP) paradigm that utilizes a simulation model based on a recently developed physical soiling model. The proposed approach is applied to a case study on a hypothetical ST plant located in South Australia. The proposed policies are benchmarked with other traditional time-based cleaning policies and a previously developed reflectance-based policy. The results indicate a considerable benefit of sectorial reflectance-based cleaning strategies to other benchmarked policies (i.e. similar to 2% savings on total cleaning costs). Moreover, in case where the per-cleaning costs (e.g. water, fuel) are significant compared to the fixed costs (e.g. truck depreciation), the savings of proposed sectorial cleaning policies are greater (similar to 10% savings). (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free