Integrating hourly life-cycle energy and carbon emissions of energy supply in buildings

Huge amount of energy resources greenhouse gas (GHG) emissions is devoted to the built environment. Therefore, an accurate assessment method of these indicators is compulsory. To take advantage of the temporal variation in the primary energy use and associated GHG emissions of the energy supply, we propose two ways of integrating hourly life-cycle conversion factors in building energy systems. First, to appraise the energy system design, we developed a versatile modelling and performance assessment framework using a multi-criteria approach. The simulated performances of possible energy systems are compared, and help designers systematically choose appropriate energy production and storage systems. Second, for the operation of the building, we propose an energy management procedure (EMP) that always feeds in the energy source with the least global warming potential (GWP). These two developments are tested on a case study consisting in an architectural project that should respect the 2000 W society targets. The running conditions of several energy systems are simulated and compared. The most promising scenario is identified. Compared to traditional methods, the assessment framework has a promising future but the GWP-based energy management procedure offers, in the context of the case study, limited GHG emissions mitigation at very high primary energy cost.

Neutral global warming potential target of electricity storage as threshold for greenhouse gas emission mitigation in buildings

Thomas Bernard Paul Jusselme, Didier Albert Simon Vuarnoz

Coupling photovoltaic (PV) systems with energy storage (ES) in buildings, enables to increase the building’s energy autonomy and the self-consumption of onsite renewables. ES increases nonetheless the life cycle environmental impact of the stored energy. As such, there exists a threshold where the GHG emission benefits of using ES start to compensate its own embedded and operational impact. In this study, a methodology to assess this neutral global warming potential target of ES is proposed, and is extended to the primary energy and its non-renewable part. The methodology is tested on a case study consisting of a feasibility project of a building located in Switzerland. When the surplus of renewable energy that cannot be used or stored directly is exportable to the grid, the operational benefits of the ES cannot balance anymore its embedded impact. In regards to market products, these neutral targets are tighter for GHG emissions than energy. Neutral targets are greatly affected by the characteristics of the grid mix supplying the building so the use of energy storage for the mitigation of GHG emissions in buildings may be efficient in Germany, but might be technologically more challenging with low-carbon French electricity.

2018

Environmental impact assessment of Swiss residential archetypes: a comparison of construction and mobility scenarios

Sergi Aguacil Moreno, Judith Elisa Drouilles, Endrit Hoxha, Thomas Bernard Paul Jusselme, Sophie Lufkin

Environmental performance assessment of the built environment tends to focus mostly on operational final energy consumption of buildings located within a specific context. Such a limited scope prevents broader usability of findings in practice. In Switzerland, the ‘2000-W society’ vision provides a theoretical framework towards energy transition. Intermediate targets for 2050 relate to an extensive assessment incorporating environmental impacts of construction materials and use of a building, and of induced mobility of its occupants. Accordingly, it becomes crucial to gather information about the current building stock performance and its transition potential. The paper aims at contributing to the sustainability transition debate by providing a comparative assessment of retrofitted and new residential buildings representative of the Swiss building stock. A direct output could constitute in establishing a reliable reference dataset to support practitioners’ or lawmakers’ future decisions. The novelty of the study relies on two aspects: (1) on adopting an interdisciplinary approach to propose an overview of the current status and transition potential of the built environment, and (2) on building a methodology able to extrapolate results for large-scale studies of neighbourhoods or larger built areas. Based on the definition of four building archetypes, this study assesses four scenarios decomposed into four to six variants. The scenarios consist in varying the building energy-performance, while the variants implement different locations—among urban, peripheral and rural areas—and different passive or active strategies. Results are expressed in terms of non-renewable primary energy consumption and global warming potential. They highlight in particular the performances of renovation projects that can decrease the impacts of current building stock by 75 to 85%, the effect of high-energy performance on embodied impacts, the high-level of performance of multi-family houses with 37% lower impacts compared to those of single-family houses and the significant impact of mobility (around 50%).

2019

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Alina Ursula Rosa Walch

To achieve the goals of the Paris Agreement on climate change, governments around the world must shift to renewable energy (RE) to drastically reduce their CO2 emissions within the next 10-30 years. The development of effective strategies for RE integration into national energy systems requires assessing the spatial and temporal availability of these resources at country scale. This spatio-temporal availability is described by the technical RE potential, defined as the maximum electrical or thermal energy that can be obtained from a given technology. While most previous technical potential assessments have used empirical models and geospatial tools to estimate RE potential, the increasing opportunities provided by Big Data and Machine Learning approaches, which extract additional information from large building and environmental datasets, are largely under-exploited. Integrating such data-driven methods with existing models enables national-scale RE resource assessments with higher precision and at higher spatial and temporal resolution than ever before.This work proposes Big Data approaches to estimate renewable energy potentials at national scale, with application to Switzerland. Focusing on the built environment, the technical potentials of Rooftop Solar Photovoltaics (RPV) and shallow Ground-Source Heat Pumps (GSHPs) are assessed at the spatial resolution of individual buildings and at hourly (RPV) or monthly (GSHP) temporal resolution. The proposed approaches combine large-scale data processing and Machine Learning with novel computational methods and state-of-the-art models of RE technologies. Furthermore, the quantification and propagation of uncertainties is addressed for RPV. The results are publicly available datasets of RE potentials, which enable spatio-temporal assessments of energy systems with a high share of renewables. At national level, the estimated RPV potential for Switzerland is 25+/-9 TWh, which may increase up to 39 TWh for more optimistic scenarios for solar PV panel installation, or beyond 60 TWh if rooftop-integrated PV is used. The shallow GSHP potential is estimated at up to 97 TWh, of which 20% is located in urban areas. I show that this potential may increase by over 50% through seasonal regeneration from space cooling. Spatial mismatches between supply and demand may be reduced by district heating networks (DHN).These findings have several implications for the Swiss Energy Transition for 2050: (i) To achieve the national target of 34 TWh from PV systems, 50-55% of all nearly flat (< 20° tilt) or south-facing surfaces must be exploited, whereby roofs with a high annual yield should be prioritised; (ii) GSHPs may deliver 30-70% of the heat demand of the targeted 1.5 million heat pumps and contribute significantly to the expansion of DHN in urban areas; (iii) these fractions may be increased through seasonal regeneration, for example from space cooling or excess solar thermal generation. The proposed approaches are transferable to RE potential assessments in other regions or countries, in particular by adapting them to open and cross-country input data, as well as to related RE technologies. The proposed data-driven methods and the publicly available datasets can be used by researchers, practitioners and policy makers to assess future decarbonisation pathways. I hope that this work will contribute to achieving national CO2 emission targets, moving one step closer to the goals of the Paris Agreement.