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Energietechnik
(2019)
Kurz und prägant werden die Energieumwandlungsprozesse dargestellt. Die Schwerpunkte reichen von der kompletten Beschreibung der nachhaltigen, erneuerbaren Energietechniken, über Gas- und Dampfturbinen-Kraftwerke sowie Kraft-Wärme-Kälte-Kopplungsanlagen bis hin zur Energieverteilung und zum Kyoto-Protokoll. Zu allen Kapiteln gibt es Aufgaben mit vollständigen Lösungen im Anhang. In der aktuellen Auflage sind die Grundlagen der Energiewandlung deutlich um verfügbare Energiequellen, Energieträger und den globalen Energiebedarf erweitert. Das Kapitel 19 wurde von seinem neuen Autor grundlegend neu gefasst und heißt nun „Marktliberalisierung und Energiewende“. Alle Kapitel wurden aktualisiert und die Inhalte didaktisch noch verständlicher dargestellt.
This paper presents the use of model predictive control (MPC) based approach for peak shaving application of a battery in a Photovoltaic (PV) battery system connected to a rural low voltage gird. The goals of the MPC are to shave the peaks in the PV feed-in and the grid power consumption and at the same time maximize the use of the battery. The benefit to the prosumer is from the maximum use of the self-produced electricity. The benefit to the grid is from the reduced peaks in the PV feed-in and the grid power consumption. This would allow an increase in the PV hosting and the load hosting capacity of the grid.
The paper presents the mathematical formulation of the optimal control problem
along with the cost benefit analysis. The MPC implementation scheme in the
laboratory and experiment results have also been presented. The results show
that the MPC is able to track the deviation in the weather forecast and operate
the battery by solving the optimal control problem to handle this deviation.
In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.