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978-3-8439-3976-8, Reihe Informatik
Energy Management of a Hybrid Energy Storage System in a single-family House including E-Car Mobility
132 Seiten, Dissertation Technische Universität Clausthal (2018), Softcover, A5
The transition into the new era in transport sector after the invasive introduction of electromobility and the already established tactic of the installation of small-scale renewable energy units in the domestic field force to the exploration of an undiscovered aspect of energy allocation. The existence of a unique battery system is already a widespread policy in the new renewable installations in a household, whereas it still remains partly explored the coupling of two storage systems, which from structural point of view are complementary.
In the frame of this research it is attempted to delimitate the capability of a hybrid energy storage system (HESS) so as to support the domestic load demand of a single family house in a central north area in Germany, which is accumulated from the load demand of an E-vehicle, that is used for commute reasons of the family and always charges at home.
The designed HESS is composed of a PV installation, two storage devices, namely a lead acid battery system (LAB) and a vanadium redox flow battery (VRB), as well as the respective load demand of the dwelling.
So as to succeed an optimized utilization of the dual storage system a novel algorithm is developed based on the Markov Decision Process, according to which the priority for charging and discharging process is assigned to the storage facility which depicts a favorable performance under the given conditions. The main target is to reduce the great fluctuations between the building and the grid and succeed a higher rate of self-energy consumption.
The designed method tackles the problem of controlling a dual storage installation in the domestic sector, when considering the electric vehicle charging requirements, and contributes to the management of the energy flow into a novel system topology with the introduction of reinforcement learning tools.
In the evaluation part it is proved that the designed approach is not only more efficient from a benchmark method but also lower rates of grid interaction are succeeded and higher self-energy consumption is achieved. It is thereby proved that a control algorithm is essential in order to fully leverage all the key resources.