Modelling urban energy requirements using open source data and models. (1st December 2018) - Alhamwi, Alaa; Medjroubi, Wided; Vogt, Thomas; Agert, Carsten
Highlights: Systematic approach for simulating spatial-temporal urban electricity requirements. A validated open source GIS-based tool for modelling local demand and supply. The model successfully reproduced the electricity profiles compared to real datasets. High shares of renewables can reduce the required balancing energy from storage. High autarky level can be achieved at a specific mix of renewables for the case study. Abstract: The transition towards sustainable and self-sufficient cities requires extensive knowledge of the electricity requirements for diverse consumers that interact in a temporal and geospatial platform. Energy models contribute to the strategic planning of future urban energy systems by providing relevant insights and scientific recommendations. In this context, energy policy advice have to entail transparency of the tools and models used as well as their respective input datasets which are generally neither open nor publicly available. The aim of this contribution is, to model urban energy requirements, namely local electricity consumption and on-site renewable power generation, using solely open source data and models. A systematic approach for a bottom-up simulation of urban electricity supply and demand down to the building unit level is developed here. The methodology combines spatial parameters of urban geometries and settlements and links them to real-world data using Geographic Information Systems. The developed model is showcased and validated for the city of Oldenburg (Germany). Quarter-hourly time series of electricity demand and supply are extracted for each existing urban unit such as buildings and streets. A regression analysis has been performed to validate the model outputs against measured data. Accordingly, up to 94% of the variance of the simulated urban electricity demand and supply are predictable from the measured datasets. The resulting demand and supply profiles have been used to investigate storage needs in urban areas. Furthermore, the model presented here offers a detailed load representation of different urban consumers' segmentation. It determines also the configurations of on-site urban renewable energy mixes at which the share of electricity from grid and/or storage are at its minimum. Preliminary results show that, high levels of urban energy autarky could be achieved for a specific combination of local renewable energy sources.