Towards optimal solar tracking: a dynamic programming approach
File(s)pcj2014.pdf (288.53 KB)
Accepted version
Author(s)
Panagopoulos, Athanasios Aris
Chalkiadakis, Georgios
Jennings, R Nicholas
Type
Conference Paper
Abstract
The power output of photovoltaic systems (PVS) increases with the use of effective and efficient solar tracking techniques. However, current techniques suffer from several drawbacks in their tracking policy: (i) they usually do not consider the forecasted or prevailing weather conditions; even when they do, they (ii) rely on complex closed-loop controllers and sophisticated instruments; and (iii) typically, they do not take the energy consumption of the trackers into account. In this paper, we propose a policy iteration method (along with specialized variants), which is able to calculate near-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from online providers. To account for the energy needs of the tracking system, the technique employs a novel and generic consumption model. Our simulations show that the proposed methods can increase the power output of a PVS considerably, when compared to standard solar tracking techniques.
Date Issued
2015-01
Date Acceptance
2015-01-25
Citation
2015, pp.695-701
Publisher
AAAI Press
Start Page
695
End Page
701
Journal / Book Title
Proceedings of the 29th AAAI Conference on Artificial Intelligence
Copyright Statement
© 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Source
AAAI-2015: 29th AAAI Conference on Artificial Intelligence
Publication Status
Unpublished
Start Date
2015-01-25
Finish Date
2015-01-30
Coverage Spatial
Austin, Texas