Abstract
Battery energy storage systems are a key to the utilization of renewable energies, allowing for short-term storage of electricity and balancing of energy generation and consumption. However, the optimal operation of these systems is still an area of research. This paper presents operating strategies and their optimization with respect to total operational energy costs in buildings that are equipped with automated building energy management systems. The presented approach uses an evolutionary algorithm to set the parameters of the battery system controller for a rolling horizon. The combination of scheduling and control is chosen to aim at robustness against deviations of local loads from predictions. Scenarios comprising different electricity tariffs and the optimization of three operating strategies are simulated and evaluated. The results show that the operating strategies and their optimization lead to significantly different results, reflecting their ability to cope with uncertainty of future consumption and generation.
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Müller, J., Ahrens, M., Mauser, I., Schmeck, H. (2018). Achieving Optimized Decisions on Battery Operating Strategies in Smart Buildings. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_15
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DOI: https://doi.org/10.1007/978-3-319-77538-8_15
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