City energy modelling - Optimising local low carbon transitions with household budget constraints

Research output: Contribution to journalArticle

Abstract

Urban areas constitute over three-fourths of the current global economy, house more than half of the global population and consume more than two thirds of final global energy consumption with the consequential greenhouse gas emissions. New dynamics are occurring with cities becoming vectors of sustainable development, through initiatives like the Covenant of Mayors. Innovative approaches and tools are required to support city development planning in compliance with climate change mitigation goals. City planning and management are typically addressed through a fragmented and silo approach, failing to capture the dynamics and complexity of a city energy system. Integrated energy system modelling tools are progressively incorporating increasing realism to enhance results quality and confidence. This paper demonstrates the effects of including household budget constraints in integrated energy system optimisation modelling on low carbon city pathways. The TIMES_EVORA model was used to characterize current and future city energy system, covering all its chain, from energy supply sectors (e.g. electricity production) to end use sectors (e.g. residential and transport). Optimal solutions for meeting Évora municipality (Portugal) 2030 greenhouse gases emissions targets were assessed, combining household budget constraints for the acquisition of more efficient technologies – from appliances to private vehicles. The results showed a decrease of hybrid vehicles ownership in 2030 and a consequent increase of diesel vehicles. The optimal model solution shows a persistence in the acquisition of highly efficient households' appliances and the deployment of additional PV systems to facilitate a reduction of the electricity generation CO2 footprint. This allows to offset the transport CO2 emission increase and provide benefits across all energy system. Improving the integration of behavioural or real-life aspects in energy system optimisation models allows further understanding of city energy systems dynamics and provide robust city decarbonization strategies design. Cities sustainable energy system and urban planning should include different agent's investment constraints in order to avoid technology deployment locking that can compromise cities sustainable pathways.

Original languageEnglish
Article number100387
JournalEnergy Strategy Reviews
Volume26
DOIs
Publication statusPublished - 1 Nov 2019

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Urban planning
Gas emissions
Greenhouse gases
Carbon
Electricity
Decarbonization
Domestic appliances
Hybrid vehicles
Climate change
Sustainable development
Dynamical systems
Energy utilization
Planning
Compliance

Keywords

  • City energy system
  • Household budget constraints
  • Integrated energy model
  • Portugal
  • Scenarios analysis

Cite this

@article{b94ae41cb27743fa81abc82f196c781f,
title = "City energy modelling - Optimising local low carbon transitions with household budget constraints",
abstract = "Urban areas constitute over three-fourths of the current global economy, house more than half of the global population and consume more than two thirds of final global energy consumption with the consequential greenhouse gas emissions. New dynamics are occurring with cities becoming vectors of sustainable development, through initiatives like the Covenant of Mayors. Innovative approaches and tools are required to support city development planning in compliance with climate change mitigation goals. City planning and management are typically addressed through a fragmented and silo approach, failing to capture the dynamics and complexity of a city energy system. Integrated energy system modelling tools are progressively incorporating increasing realism to enhance results quality and confidence. This paper demonstrates the effects of including household budget constraints in integrated energy system optimisation modelling on low carbon city pathways. The TIMES_EVORA model was used to characterize current and future city energy system, covering all its chain, from energy supply sectors (e.g. electricity production) to end use sectors (e.g. residential and transport). Optimal solutions for meeting {\'E}vora municipality (Portugal) 2030 greenhouse gases emissions targets were assessed, combining household budget constraints for the acquisition of more efficient technologies – from appliances to private vehicles. The results showed a decrease of hybrid vehicles ownership in 2030 and a consequent increase of diesel vehicles. The optimal model solution shows a persistence in the acquisition of highly efficient households' appliances and the deployment of additional PV systems to facilitate a reduction of the electricity generation CO2 footprint. This allows to offset the transport CO2 emission increase and provide benefits across all energy system. Improving the integration of behavioural or real-life aspects in energy system optimisation models allows further understanding of city energy systems dynamics and provide robust city decarbonization strategies design. Cities sustainable energy system and urban planning should include different agent's investment constraints in order to avoid technology deployment locking that can compromise cities sustainable pathways.",
keywords = "City energy system, Household budget constraints, Integrated energy model, Portugal, Scenarios analysis",
author = "Dias, {L. P.} and S. Sim{\~o}es and Gouveia, {J. P.} and J. Seixas",
note = "The work supporting this paper was funded by the EU project INSMART , Integrated Smart City Planning, under grant agreement no.: 314164 and by Portuguese Science and Technology Foundation (FCT) through the scholarship PD/BD/128452/2017 . The authors want to thank all INSMART project members, and particular e4SMA, S.A. team: Rocco De Miglio, Maurizio Gargiulo and Alessandro Chiodi for the essential TIMES_city modelling contributions. Finally, the authors acknowledge and thank the support given to CENSE by the Portuguese Foundation for Science and Technology Funda{\cc}{\~a}o para a Ci{\^e}ncia e Tecnologia, I.P., Portugal ( UID/AMB/04085/2019 ).",
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N1 - The work supporting this paper was funded by the EU project INSMART , Integrated Smart City Planning, under grant agreement no.: 314164 and by Portuguese Science and Technology Foundation (FCT) through the scholarship PD/BD/128452/2017 . The authors want to thank all INSMART project members, and particular e4SMA, S.A. team: Rocco De Miglio, Maurizio Gargiulo and Alessandro Chiodi for the essential TIMES_city modelling contributions. Finally, the authors acknowledge and thank the support given to CENSE by the Portuguese Foundation for Science and Technology Fundação para a Ciência e Tecnologia, I.P., Portugal ( UID/AMB/04085/2019 ).

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