The Effect of Temporal Aggregation on Battery Sizing for Peak Shaving
| Autor: | Dominik Werle, Daniel Warzel, Klemens Böhm, Anne Koziolek, Simon Bischof, Holger Trittenbach |
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| Rok vydání: | 2019 |
| Předmět: |
Consumption (economics)
Battery (electricity) Battery system Computer science 020209 energy Industrial production Safety margin 02 engineering and technology 010501 environmental sciences 01 natural sciences Reliability engineering Battery sizing Peaking power plant 0202 electrical engineering electronic engineering information engineering 0105 earth and related environmental sciences Data compression |
| Zdroj: | e-Energy |
| Popis: | Battery systems can reduce the peak electrical consumption through proper charging and discharging strategies. To this end, consumers often rely on historic consumption data to select a cost-efficient battery system. However, historic data is an imperfect mapping of the real consumption, because of a coarse sampling rate or measurement inaccuracies. This can result in non-optimal decisions, e.g., by underestimating the battery capacity required. In this article, we analyze how aggregation affects a state-of-the-art battery sizing algorithm for an industrial production site. We then use machine learning on a short period of high-resolution data to correct this error from historic data. Our experiments indicate that machine learning models can correct this error in some cases. However, adding a safety margin obtained from historic data to the battery size is a more reliable way of reducing the error. |
| Databáze: | OpenAIRE |
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