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GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas

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  • Alhamwi, Alaa
  • Medjroubi, Wided
  • Vogt, Thomas
  • Agert, Carsten

Abstract

Planning of sustainable Urban Energy Systems is a challenging task for the many involved stakeholders which requires optimal use of available knowledge for decision support. Therefore, systematic approaches integrating energy models and interactively linking them to real-world data are highly required. In this regard, Geographic Information Systems (called GIS) offer as a platform many advantages and can play a significant role in integrating renewable energy sources at the urban scale. The optimal integration and placement of different possible flexibilisation technologies such as storage can profit from the possibilities offered by GIS tools. In addition, GIS facilitate policy making, allowing for a realistic and multilayer representation of urban energy systems. This contribution first draws an overview of GIS-based models for urban energy systems by investigating the current state of modelling. It introduces in a second step, an outline of a transferable GIS-based platform for the optimisation of storage and other flexibilisation technologies in urban areas. The model is composed of three main components and deals with the optimal integration of flexibilisation technologies. The fields of applications of the model are, but not restricted to, analysis of the emergence of sustainable cities, self-consumption at the urban levels, autarky measures, capacity demand and economic efficiency, and the integration of flexibility options. The method developed by the authors and presented here deals with the first component of the proposed model which is setting up the spatial framework. The framework relies mainly on spatial features of urban objects extracted from the open source OpenStreetMap database. The different steps involved in the spatial framework set-up are extraction and filtering of the data sets and their algorithmic steps which will be introduced here in details. A central finding of this contribution illustrates the feasibility and effectiveness of using open source data and tools to replicate urban energy features.

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  • Alhamwi, Alaa & Medjroubi, Wided & Vogt, Thomas & Agert, Carsten, 2017. "GIS-based urban energy systems models and tools: Introducing a model for the optimisation of flexibilisation technologies in urban areas," Applied Energy, Elsevier, vol. 191(C), pages 1-9.
  • Handle: RePEc:eee:appene:v:191:y:2017:i:c:p:1-9
    DOI: 10.1016/j.apenergy.2017.01.048
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    1. Jaccard,Mark, 2006. "Sustainable Fossil Fuels," Cambridge Books, Cambridge University Press, number 9780521679794, October.
    2. De Gennaro, Michele & Paffumi, Elena & Scholz, Harald & Martini, Giorgio, 2014. "GIS-driven analysis of e-mobility in urban areas: An evaluation of the impact on the electric energy grid," Applied Energy, Elsevier, vol. 124(C), pages 94-116.
    3. Pfeiffer, Alexander & Millar, Richard & Hepburn, Cameron & Beinhocker, Eric, 2016. "The ‘2°C capital stock’ for electricity generation: Committed cumulative carbon emissions from the electricity generation sector and the transition to a green economy," Applied Energy, Elsevier, vol. 179(C), pages 1395-1408.
    4. Girardin, Luc & Marechal, François & Dubuis, Matthias & Calame-Darbellay, Nicole & Favrat, Daniel, 2010. "EnerGis: A geographical information based system for the evaluation of integrated energy conversion systems in urban areas," Energy, Elsevier, vol. 35(2), pages 830-840.
    5. Sahoo, K. & Hawkins, G.L. & Yao, X.A. & Samples, K. & Mani, S., 2016. "GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: A case study with cotton stalks in the Southeastern US," Applied Energy, Elsevier, vol. 182(C), pages 260-273.
    6. Saha, Mithun & Eckelman, Matthew J., 2015. "Geospatial assessment of potential bioenergy crop production on urban marginal land," Applied Energy, Elsevier, vol. 159(C), pages 540-547.
    7. Pfenninger, Stefan & DeCarolis, Joseph & Hirth, Lion & Quoilin, Sylvain & Staffell, Iain, 2017. "The importance of open data and software: Is energy research lagging behind?," Energy Policy, Elsevier, vol. 101(C), pages 211-215.
    8. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    9. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    10. Sánchez-Lozano, Juan M. & Henggeler Antunes, Carlos & García-Cascales, M. Socorro & Dias, Luis C., 2014. "GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain," Renewable Energy, Elsevier, vol. 66(C), pages 478-494.
    11. de Sisternes, Fernando J. & Jenkins, Jesse D. & Botterud, Audun, 2016. "The value of energy storage in decarbonizing the electricity sector," Applied Energy, Elsevier, vol. 175(C), pages 368-379.
    12. Ma, Jun & Cheng, Jack C.P., 2016. "Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology," Applied Energy, Elsevier, vol. 183(C), pages 182-192.
    13. Frauke Wiese & Gesine Bökenkamp & Clemens Wingenbach & Olav Hohmeyer, 2014. "An open source energy system simulation model as an instrument for public participation in the development of strategies for a sustainable future," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 3(5), pages 490-504, September.
    14. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    15. Arampatzis, G. & Kiranoudis, C. T. & Scaloubacas, P. & Assimacopoulos, D., 2004. "A GIS-based decision support system for planning urban transportation policies," European Journal of Operational Research, Elsevier, vol. 152(2), pages 465-475, January.
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