Ambition meets reality – Modeling renovations of the stock of apartments in Gothenburg by 2050

Mata, Érika; Wanemark, Joel; Österbring, Magnus; Shadram, Farshid

doi:10.1016/j.enbuild.2020.110098

Cited by 12 publications

(5 citation statements)

References 31 publications

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“…First, we use the ECCABS model [49,50], developed to investigate energy use reductions in Swedish residences [51], and used to map opportunities and costs for the transformation of residential and nonresidential buildings in Sweden [52,53] and several European countries [54]. Only some basic features of the model are used in this work (figure 2), i.e.…”

Section: Eskilstunamentioning

confidence: 99%

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

Mata,

Garcia,

Suna

et al. 2024

Environ. Res.: Energy

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This study develops a reproducible method for estimating the cost-efficient flexibility potential of a local or regional energy system. Future scenarios that achieve ambitious climate targets and estimate the cost-efficient flexibility potential of demonstration sites were defined. Flexible potentials for energy system assessment are upscaled from the demonstration sites in Eskilstuna (Sweden) and Lower Austria (Austria). As heat pumps (HPs) and district heating (DH) are critical for future heat demand, these sites are representative types of DH networks in terms of size and integration with the electricity grid. In both regions a TIMES model is used for energy system optimization, while for upscaling, Eskilstuna uses the building-stock model ECCABS, whereas Lower Austria uses a Mixed Integer Linear Programming optimization model, and the BALMOREL power system model. According to the modeling, HPs will dominate Eskilstuna’s heating sector by 2040. In Lower Austria, DH becomes more prevalent, in combination with wood biomass and HPs. These findings are explained by the postulated technological-economic parameters, energy prices, and CO2 prices. We conclude that future electricity prices will determine future heating systems: either a high share of centralized HPs (if electricity prices are low) or a high share of combined heat-and-power (if electricity prices are high). Large-scale energy storage and biomass can be essential solutions as may deliver increased cost-effectiveness, if available and under certain conditions.

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Section: Eskilstunamentioning

confidence: 99%

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

Mata,

Garcia,

Suna

et al. 2024

Environ. Res.: Energy

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“…Some models for assessing alternative retrofit solutions from socio-economic aspects were developed [77][78][79]. Some models for urban building energy retrofit plans were proposed considering workmanship capacity and population dynamics [80,81].…”

Section: Optimization Modeling-based Approachmentioning

confidence: 99%

Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review

Shu

Zhao

2023

Buildings

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This research presents a comprehensive review of the research on smart urban energy retrofit decision-making. Based on the analysis of 91 journal articles over the past decade, the study identifies and discusses five key categories of approaches to retrofit decision-making, including simulation, optimization, assessment, system integration, and empirical study. While substantial advancements have been made in this field, opportunities for further growth remain. Findings suggest directions for future research and underscore the importance of interdisciplinary collaboration, data-driven evaluation methodologies, stakeholder engagement, system integration, and robust and adaptable retrofit solutions in the field of urban energy retrofitting. This review provides valuable insights for researchers, policymakers, and practitioners interested in advancing the state of the art in this critical area of research to facilitate more effective, sustainable, and efficient solutions for urban energy retrofits.

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“…When covering economic inputs, bottom-up statistical models differ from the macro-econometric models of Q1 in that they enable micro-economic studies with a higher level of detail and often cover the interactions between households and individuals (e.g. building owners) and organizations [86] (e.g., in studies of the UK and Germany [14], China [79], and Denmark [73]). Bottom-up statistical models are found across national, regional, and urban scale studies of building stock energy use.…”

Section: Q3: Bottom-up/black-boxmentioning

confidence: 99%

“…[87]. Recent advances in computing and data have allowed improvement of the traditional, single-building archetype approach to include modeling of hundreds or thousands of representative buildings (e.g., ResStock), sometimes even modeling every individual building in a given geographic area (e.g., ECCABS [86]). 4 As such, the methodologies used to generate the building archetypes may be diverse, including artificial reference buildings [88,91], statistically sampled reference buildings [87], synthetic buildings [97,98], or data-driven approaches [6,139].Geospatial modeling, which uses building energy simulation in combination with spatial representation and modeling in geographic information systems (GIS), is a rapidly developing physics-modeling approach that holds promise for generating information required for energy and emissions-related policy making and planning by actors such as municipalities and utilities already using GIS-based decision support.…”

Section: Physics-simulationmentioning

confidence: 99%

Developing a common approach for classifying building stock energy models

Langevin

Reyna

Ebrahimigharehbaghi

et al. 2020

Renewable and Sustainable Energy Reviews

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Buildings contribute 40% of global greenhouse gas emissions; therefore, strategies that can substantially reduce emissions from the building stock are key components of broader efforts to mitigate climate change and achieve sustainable development goals. Models that represent the energy use of the building stock at scale under various scenarios of technology deployment have become essential tools for the development and assessment of such strategies. Within the past decade, the capabilities of building stock energy models have improved considerably, while model transferability and sharing has increased. Given these advancements, a new scheme for classifying building stock energy models is needed to facilitate communication of modeling approaches and the handling of important model dimensions. In this article, we present a new building stock energy model classification framework that leverages international modeling expertise from the participants of the International Energy Agency's Annex 70 on Building Energy Epidemiology. Drawing from existing classification studies, we propose a multi-layer quadrant scheme that classifies modeling techniques by their design (top-down or bottom-up) and degree of transparency (black-box or white-box); hybrid techniques are also addressed. The quadrant scheme is unique from previous classification approaches in its non-hierarchical organization, coverage of and ability to incorporate emerging modeling techniques, and treatment of additional modeling dimensions. The new classification framework will be complemented by a reporting protocol and online registry of existing models as part of ongoing work in Annex 70 to increase the interpretability and utility of building stock energy models for energy policy making.

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Ambition meets reality – Modeling renovations of the stock of apartments in Gothenburg by 2050

Cited by 12 publications

References 31 publications

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

Comparative modeling of cost-optimal energy system flexibility for Swedish and Austrian regions

Decision-Making Approach to Urban Energy Retrofit—A Comprehensive Review

Developing a common approach for classifying building stock energy models

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