Daniel JohnSteuerung solar gespeister häuslicher Energieversorgungssysteme auf der Basis des Reinforcement Learning Ansatzes

About this book englishenglish

To reduce energy-related greenhouse gas emissions, households are increasingly supplied with energy by photovoltaic (PV) or photovoltaic-thermal (PVT) systems. Due to the volatile character of these solar systems, an efficient control system is needed for their increased integration into households.

The aim of this work is therefore to develop a control concept for solar-powered domestic energy supply systems and to evaluate it with the aid of simulation. Conceptually, it is examined whether a control based on reinforcement learning is suitable to positively influence operational parameters and whether an improvement of these parameters compared to a reference control can be achieved by implementing this control. For this purpose, a supply-side control of the flow rate through PVT systems, a demand-side control of the loading of hot water storage tanks or the control of both aspects is used.

The control concept trains control units on the basis of a training data set and then validates them on an independent validation data set. This approach allows quantification of both training and validation success.

The simulation-based evaluation shows that the control concept can successfully train and validate control units and improve the parameters of domestic energy supply systems through their simulation-based implementation. The quantifiable effects can be detected consistently over a large number of the examined cases and can also be demonstrated over the examined single-family houses per case in similar orders of magnitude.

The developed control concept can thus be evaluated as suitable with regard to a simulation-based analysis. If these results are successfully tested, it will be possible to integrate volatile renewable energies into households in order to improve holistic parameters.

Ihr Werk im Verlag Dr. Kovač

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