Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study

Keirstead, James; Calderón, Carlos

doi:10.1016/j.enpol.2012.03.058

Cited by 35 publications

(25 citation statements)

References 21 publications

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“…More generally, spatial mapping of energy demand has potential applications in several areas of energy study, including district heating (Finney et al, 2012), urban planning (Kierstead & Calderon, 2012), rural planning (Schlegel et al, 2012) and the potential for using waste heat from industry (McKenna & Norman, 2010). The importance and utility of heat mapping was demonstrated by the recent release of a UK CHP Development Map by DECC (2013c).…”

Section: Introductionmentioning

confidence: 99%

Spatial mapping of building energy demand in Great Britain

et al. 2014

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Maps of energy demand from buildings in Great Britain have been created at 1 km square resolution. They reveal the spatial variation of demand for heat and electricity, of importance for energy distribution studies and particularly for bioenergy research given the significant distance-based restrictions on the viability of bioenergy crops. Maps representing the spatial variation of energy demand for the year 2009 were created using publicly available sub-national gas and electricity consumption data. A new statistical model based on census data was used to increase the spatial resolution. The energy demand was split into thermal energy (the heat energy required for space heating and hot water) and electricity used for purposes other than heating (nonheating electricity or NHE) and was determined separately for the domestic and nondomestic sectors. 'Scenario factors,' representing the fractional change at national level in the demand for heat and NHE, were derived from scenarios constructed by UKERC. These scenarios represent a range of pathways from the present day to 2050. The present work focused on the two cases of greatest relevance, the 'low carbon' and 'additional policies' scenarios, and factors for both were derived, for the demand types described, for every 5 years between 2000 and 2050. Approximately, future spatial energy demands can be obtained by applying the scenario factors to the base mapping data for 2009.

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Section: Introductionmentioning

confidence: 99%

Spatial mapping of building energy demand in Great Britain

et al. 2014

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“…Appropriate decision support tools need to provide guidance on cost-effective local policies and technologies, and the 'optimal' sequential deployment of various measures in a long-time perspective [17].…”

Section: Needs Of Local Governmentsmentioning

confidence: 99%

Supporting Cities’ Emission Mitigation Strategies: Modelling Urban Transport in a Times Energy System Modelling Framework

Forsberg¹,

Krook‐Riekkola²

2017

WIT Transactions on the Built Environment

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The transport sector is a significant emitter of greenhouse gases (GHGs) and air pollutants in urban areas. How the transport sector evolve during the coming decades will have significant impact on the possibilities to meet tough climate and environmental targets. This makes transportation an important part of cities' Sustainable Energy and Climate Action Plans. Still, transportation is somewhat overlooked in many city-level analyses. Energy system optimisation models, like the TIMES modelling framework, are useful tools in identifying energy pathways to reach ambitious energy savings and emission mitigation targets. Based on the identification of urban transport-energy system characteristics, the needs of local governments, and insights from traditional transport models, we propose a partly new representation of the transport sector within a TIMES-City modelling framework, adapting it to the urban transport-energy setting to improve model realism and power of insight. TIMES-City supports analysis of intracity and long-distance passenger and freight transportation, including only the city organisation or the entire administrative city area. Detailed techno-economic-environmental representation of all major existing and emerging modes, technologies and fuels provides basis for consistent long-term analyses. Keywords: energy system optimisation model, urban energy system, urban transports, transportenergy setting, TIMES-City, sustainable energy and climate action plan, sustainable urban mobility plans, local energy policy. INTRODUCTIONThe European Commission (EC) have highlighted cities as the drivers of the European economy, while also emphasizing the importance of well-functioning urban transportation to continue to attract investments and create jobs [1]. However, transportation also bring significant external effects. From a global perspective, emissions of greenhouse gases (GHGs) is top priority, and transportation accounts for more than 20% of energy-related carbon emissions [2], but locally, air pollutants, congestion, noise, and safety are as important [3]. These emissions are attributed to the currently dominant technologies and fossil fuels. Mitigating emissions calls for comprehensive city-level strategies, assessing feasible and effective measures within the entire spectrum of the avoid-shift-improve framework [4].An operations research approach can be useful to help structure the problem, and identify the system environment, its resources, and decision-makers [5]. When approaching problems of systemic character, mathematical models can be powerful tools for 'mental experiments' aimed at exploring system dynamics in an uncertain future [6]. Hence, comprehensive energy system optimisation models (ESOM) can be useful for cities when identifying energy, climate, and clean air strategies.In this study, a TIMES-based ESOM is adapted and applied to the urban scale. Focus is on the urban transport-energy setting, which is somewhat overlooked or neglected in many urban energy system studies. We attempt...

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“…As well as a fully dynamic formulation, there are multiperiod and steady-state versions. The latter has been used in a number of studies [42,43,44]. One limitation of the model is that, even with the efficient time representation, it can be difficult to solve problems involving high resolutions in 210 space and time: the case study presented in [30] was very difficult to solve even with only 9 cells and 18 time intervals (6 hourly intervals for three seasons).…”

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confidence: 99%

A general spatio-temporal model of energy systems with a detailed account of transport and storage

Samsatli

2015

Computers & Chemical Engineering

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Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study

Cited by 35 publications

References 21 publications

Spatial mapping of building energy demand in Great Britain

Spatial mapping of building energy demand in Great Britain

Supporting Cities’ Emission Mitigation Strategies: Modelling Urban Transport in a Times Energy System Modelling Framework

A general spatio-temporal model of energy systems with a detailed account of transport and storage

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