Optimal decarbonization conditions for buildings & districts: A model-based techno-economic perspective

The built environment is undergoing a fundamental transformation to mitigate the effects of climate change. Energy use and subsequent greenhouse gas emissions in the building sector currently contribute about one-third for both values globally. Meeting buildings’ electrical and thermal energy demands in a cost-effective and low-CO2 manner increasingly requires more complex technological configurations. Technology solutions in the realms of energy efficiency (e.g. building envelope retrofits), renewable energy (e.g. solar photovoltaic), and complementary technologies (e.g. heat pumps and energy storage) are promising options. Decision-makers such as investors, developers, and policymakers are challenged to implement the required technologies due to the rapid pace of technological change and the increased complexities in technological interactions. The temporal dimension of technological change plays a key role due to the long lifespan of built environment infrastructure, particularly for case for decentralized energy systems with multi-technology characteristics. In order to make investment decisions, decision-makers must determine: (i) what can technologically be done to achieve a low-CO2 built environment, along with (ii) when and (iii) where it can be done, and overall (iv) how those decisions affect technical, economic, and environmental performance. Answers to these questions are required to develop strategies to justify long-term investment decisions due to the multi-decade lifespans of many technologies. Understanding the various strategic options would present the general conditions for decarbonization of the built environment. Despite the clear end target of 2050 Net-Zero emissions, these conditions are still unclear. In this context, the guiding research question of this dissertation is: How can quantitative methods support economic decision-making in complex technological systems over the long-term? A mix of qualitative and quantitative methodologies are applied to answer this overarching question for two main research cases in the built environment: decentralized multi-energy systems and thermal energy efficiency retrofits. Qualitative approaches are used to understand managerial investment decision-making mechanisms. Subsequently, quantitative energy modeling approaches are utilized to study technological adoption through the design and operation of energy technologies. The techno-economic models are all formulated as deterministic optimization problems with Mixed-Integer Linear Programming techniques to capture major cost vs. CO2 emissions trade-offs. Models account for temporal developments in both technological and contextual factors along with considerations of different spatial scales – from the building level scaled up to an urban district. The spatial contexts in the dissertation are both European and Swiss. This dissertation is composed of six individual articles each with specific research objectives. Article I, the sole qualitative article, uncovers the present day decision-making mechanisms, along with their future needs, in which real estate investors consider energy-relevant investment decisions. Articles II and III consider complex multi-technology interactions to evaluate the potential of optimal district-level decentralized multi-energy systems. Particular focus is given to the considerations of short- and long-term storage technologies such as batteries and Power-to-Hydrogen, respectively. Articles IV–VI present novel optimization approaches to consider the two cases over long term temporal scopes – the MANGO (Multi-stAge eNerGy Optimization) suite of models. The articles vary in their considerations of technological complexity and spatial contexts from buildings to districts: Article V considers individual buildings while Articles IV and VI take district-level perspectives with network interconnections between buildings. Increasing technological scope is largely achieved through expanding traditional energy supply-side models to include the energy demand-side with retrofits. Based on the individual articles, the dissertation makes the following three contributions to the literature. First, it provides the necessary considerations of both qualitative and quantitative decision-making processes relevant for achieving Net-Zero emissions pathways with increasingly complex technological adoption. Second, the techno-economic optimization models demonstrate the technological configurations which are optimal for cost-effective decarbonization from a life-cycle perspective considering embodied emissions. Third, the novel optimization model formulations advance the existing energy science literature by considering complex technology interactions in efficient modeling environments. Multiple optimal strategies can be evaluated at various spatial scopes over 30-year time horizons. Finally, the applied research provides three valuable insights for practitioner and policy decision-makers towards a low-CO2 built environment. First, the vast majority of cost-effective emission reductions can be achieved with commercially available technologies. Second, the adoption of technologies relevant for the decarbonization has two important elements: (i) the actual technologies the user adopts to meet a certain requirement and (ii) on a meta-level, the models they use to make those decisions. Current decision-making processes should be improved to consider optimal investments considering the entire technological, spatial, and temporal decision-space. Third, new business models are required to accelerate the adoption of both the technologies and decision-making models. Traditional approaches to project development and financing in the built environment are challenged by renewable energy integration with storage, the interdependence of energy demand- and supply-side measures, and sector coupling.