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Selected Publication:

Zeyringer, M; Andrews, D; Schmid, E; Schmidt, J; Worrell, E.
(2015): Simulation of disaggregated load profiles and development of a proxy microgrid for modelling purposes
INT J ENERG RES. 2015; 39(2): 244-255. FullText FullText_BOKU

Abstract:
The deployment of small-scale renewable energy technologies affects the electricity grid depending on the local resource potential as well as on the regional composition of consumers. Spatially explicit renewable energy supply data and spatially disaggregated load profiles of consumers are usually not available to modellers. These data are, however, necessary to better account for the particularities of electricity systems with high levels of distributed renewable production. We present a methodology to estimate the load profile for the distribution grid of household and commercial consumers at 1kmpixel resolution for Austria. Consequently, we combine statistical data on the distribution of electricity consumers with standardized load profiles. Additionally, we present a model that allows allocating theoretical transformers to pixels allowing constructing proxy microgrids. We validate the load generation methodology for three different days and seasons. Hence, we use recently measured load profiles from the federal state of Vorarlberg. The proxy microgrids are validated using data on transformer locations from the federal state of Upper Austria. The modelling approach allows reproducing the historically measured load profiles and the number and location of transformers in the distribution grid with reasonable accuracy. The validation results show that about 80-91% of the variance of the modelled demand data can be explained by the variance of the measured data. In addition, about 78% of the transformer locations can be replicated. Copyright (c) 2014 John Wiley *** Sons, Ltd.
Authors BOKU Wien:
Schmid Erwin
Schmidt Johannes
Schmidt Julia

Find related publications in this database (Keywords)
load modelling
transformer
distribution grid
geographic information systems
clustering
optimization


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