AAAI Publications, Twenty-Ninth AAAI Conference on Artificial Intelligence

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Towards Optimal Solar Tracking: A Dynamic Programming Approach
Athanasios Aris Panagopoulos, Georgios Chalkiadakis, Nicholas Robert Jennings

Last modified: 2015-02-10

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 on-line 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.

Keywords


Solar tracking; Energy Efficiency; Smart Grid; MDP; Dynamic programming; Policy Iteration; Alternating

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