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Optimization of Operation and Control Strategies for Battery Energy Storage Systems by Evolutionary Algorithms




Veröffentlicht: 2016
Herausgeber: Giovanni Squillero and Paolo Burelli
Buchtitel: Applications of Evolutionary Computation
Ausgabe: 9597
Seiten: 507-522
Reihe: Lecture Notes in Computer Science
Verlag: Springer International Publishing

BibTeX




Kurzfassung
To support the utilization of renewable energies, an optimized operation of energy systems is important. Often, the use of battery energy storage systems is stated as one of the most important measures to support the integration of intermittent renewable energy sources into the energy system. Additionally, the complexity of the energy system with its many interdependent entities as well as the economic efficiency call for an elaborate dimensioning and control of these storage systems. In this paper, we present an approach that combines the forward-looking nature of optimization and prediction with the feedback control of closed-loop controllers. An evolutionary algorithm is used to determine the parameters for a closed-loop controller that controls the charging and discharging control strategy of a battery in a smart building. The simulation and evaluation of a smart residential building scenario demonstrates the ability to improve the operation and control of a battery energy storage system. The optimization of the control strategy allows for the optimization with respect to variable tariffs while being conducive for the integration of renewable energy sources into the energy system.

ISBN: 978-3-319-31204-0
Weitere Informationen unter: Link
DOI Link: 10.1007/978-3-319-31204-0_33

Projekt

Energie-Allianz


Verknüpfte Tools

Energy Smart Home Lab, Organic Smart Home


Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet
Energieinformatik, Evolutionäre Algorithmen