Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach

Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach

Voulis, N. (TU Delft System Engineering)
Warnier, Martijn (TU Delft System Engineering)
Brazier, F.M. (TU Delft System Engineering)

2018

Cities and communities worldwide are seeking to become more sustainable by transitioning to renewable energy resources, and by introducing electric transportation and heating. The impact and suitability of such technologies for a given area heavily depend on local conditions, such as characteristics of local demand. In particular, the shape of a local demand profile is an important determinant for how much renewable energy can be used directly, and how charging of electric vehicles and use of electric heating affect a local grid. Unfortunately, a systematic understanding of local demand characteristics on different urban scales (neighbourhoods, districts and municipalities) is currently lacking in literature. Most energy transition studies simplify local demand to household demand only. This paper addresses this knowledge gap by providing a novel data-driven classification and analysis of demand profiles and energy user compositions in nearly 15000 neighbourhoods, districts and municipalities, based on data from the Netherlands. The results show that on all urban scales, three types of areas can be distinguished. In this paper, these area types are termed “residential”, “business” and “mixed”, based on the most prevalent energy users in each. Statistic analysis of the results shows that area types are pairwise significantly different, both in terms of their profiles and in terms of their energy user composition. Moreover, residential-type demand profiles are found only in a small number of areas. These results emphasise the importance of using local detailed spatio-temporal demand profiles to support the transition of urban areas to sustainable energy generation, transportation and heating. To facilitate the implementation of the obtained insights in other models, a spreadsheet modelling tool is provided in an addendum to this paper.

Urban energy
Electricity demand profile
Spatio-temporal demand
Energy transition
Clustering

http://resolver.tudelft.nl/uuid:6f72e29c-b1da-4b5a-b2a0-9599e24fe48c

https://doi.org/10.1016/j.apenergy.2018.08.121

0306-2619

Applied Energy, 230, 1157-1171

Institutional Repository

journal article

© 2018 N. Voulis, Martijn Warnier, F.M. Brazier