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Moving windows value iteration for effective solar tracking

Romaidis Giorgis-Orfeas

Πλήρης Εγγραφή

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Περίληψη

Solar trackers have recently become an important asset in energy production industry.The most efficient type is dual axis trackers that follow sun’s movement in horizontal and vertical level based on an astronomical equation. In this thesis we present several variants of a dynamic programming method to calculate the optimal solar tracking strategy of a given tracker. To this end, we first make sure that we appropriately incorporate the physical characteristics of a commercially available PV system in our model. Then we choose a combination of specific dates and weather data, both historic and artificial, to use as datasets for experimentation over typical weather patterns. Our proposed algorithm addresses the problem of system’s positioning as a sequential decision making problem for optimal control.We use a Markov Decision Process (MDP) representation and apply value iteration (VI) with a k-step look ahead functionality over reduced state-space windows, to approximate an optimal solution. The results show that the performance of our approach clearly outperforms that of a typical dual axis tracker.

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