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Configurations of renewable power generation in cities using open source approaches: With Philadelphia case study

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  • Ranalli, Joseph
  • Alhamwi, Alaa

Abstract

In this paper, an open source tool is introduced to represent urban energy infrastructure in the City of Philadelphia, and different renewable energy scenarios are compared with respect to minimization of the standard deviation of the residual load. Renewable energy sources play a critical role in the world’s ongoing energy transition in response to climate change. Urban Energy Systems may be particularly sensitive to this transition due to the high energy demand density associated with urban environments. Open energy analysis and modeling tools can provide important information that can be used by urban energy planners, policy makers, and other stakeholders during this transition. In the present study, we apply FlexiGIS, an open energy modeling tool developed in a European context, to a case study in the City of Philadelphia. Due to the importance of open access to energy data, we pay particular attention to open energy data sources. Notably, OpenStreetMap was incomplete in its spatial coverage, but alternate open data resources were identified. This work conducts an optimization of the renewable energy mix to minimize the amount of balancing energy required for the residual load. We observe that Philadelphia has an optimal mix of renewables that favors a roughly even share of wind and solar, but that, compared to a previous case study in Oldenburg, Germany, requires more balancing energy at comparable levels of renewable penetration.

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  • Ranalli, Joseph & Alhamwi, Alaa, 2020. "Configurations of renewable power generation in cities using open source approaches: With Philadelphia case study," Applied Energy, Elsevier, vol. 269(C).
  • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920305390
    DOI: 10.1016/j.apenergy.2020.115027
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    2. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).

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