Many countries worldwide have adopted policies to support the expansion of renewable energy sources aimed at reducing greenhouse gas emissions, combating climate change, and, more generally, establishing a globally sustainable energy system. As a result, energy systems around the world are undergoing a process of fundamental change and transformation that goes far beyond the technological dimension. While energy system models have been developed and used for several decades to support decision makers in governments and companies, these models usually focus on the techno-economic dimension, whereas they fall short in addressing and considering behavioural and societal aspects of decisions related to technology acceptance, adoption, and use. In fact, it is often the societal dimension that comes with the greatest challenges and barriers when it comes to making such a socio-technical transformation happen in reality. This paper therefore provides an overview of state-of-the-art energy system models on the one hand and research studying behavioural aspects in the energy sector on the other hand. We find that these are two well-developed fields of research but that they have not yet been integrated sufficiently well to provide answers to the many questions arising in the context of complex socio-technical transformation processes of energy systems. While some promising approaches integrating these two fields can be identified, the total number is very limited. Based on our findings, research gaps and potentials for improvement of both energy system models and behavioural studies are derived. We conclude that a stronger collaboration across disciplines is required.

To mitigate climate change, energy systems must be decarbonised. Human behaviour affects energy systems on residential scales through technology adoption and use, but is often neglected in models for analysing energy systems. We therefore study the optimal planning and operation of a sector-coupled residential energy system driven by economic and environmental interests and user behaviour in terms of desired thermal comfort and clothing. Methodologically, we combine a highly flexible energy system optimisation framework for investment and operational planning, a thermal building representation, a continuous and empirically founded objective for thermal comfort as the sole driver of heating demand and an analytical multi-objective optimisation method in one sector-coupled model. We find that optimal investment in and operation of technology are highly dependent on users’ clothing and the desired comfort level. Changing from unadapted to warm clothing in transition and winter season can reduce costs by 25%, carbon emissions by 48%, gas consumption by 84%, heat demand by 20% or necessary PV installations by 28% without lowering thermal comfort. Similar reduction potentials are offered by lowering thermal comfort without changing clothing. We find that heat pumps, rooftop solar PV, batteries and generously sized water tanks are essential technologies that should be adopted regardless of user behaviour, while hydrogen is not. Full decarbonisation would require additional measures like refurbishments or further carbon-free energy sources. We conclude that in striving for decarbonisation and independency of gas, appropriate clothing and sector coupling should be promoted by policy makers and utilised by end-users as very efficient ways of reducing costs, carbon emissions, energy use and gas dependency.

Energy system models (ESMs) inform the transition from fossil fuels to renewable energy. Optimal system design is influenced by the shape and magnitude of electricity demand. Demands will change as decarbonisation efforts across sectors aim at electrification. However, many ESMs oversimplify the complexity of demand changes driven by individual adoption decisions. Agent-based models (ABMs) allow for incorporating behavioural theories capturing this complexity, but rely on assumptions about factors like energy prices and emissions affecting adoption behaviour. This work introduces a novel framework, CanAdopt, that integrates an ABM with an ESM, alleviating assumption requirements in both models. The capabilities of the novel framework are demonstrated for scenario analysis of policy impacts in the energy and residential heating sectors. The ABM models heating technology adoption and residential electricity demands for the ESM, which optimises capacity expansion and yields electricity prices and embedded emissions for the ABM. Both models cover 2020–2050 and are executed sequentially eight times. Applied to Ontario, Canada, the most progressive scenario achieves net-zero by 2035 and 2040 in the residential sector and power sector, respectively. The total transition costs are 327 billion CAD in the residential sector and 395 billion CAD in the power sector. Cumulative heating related emissions increase by 43% through a five-year delay in achieving net-zero, underscoring the urgency of the transition. The governmental carbon abatement costs in the residential sector range from 72 to 110 CAD/(t CO₂), well below the federal carbon tax of 170 CAD/(t CO₂). The coupling of both models showed, that increased residential heat pump adoption may reduce total transition cost by 8 to 10 billion CAD in the power system, but the increases in demand may be challenging to meet if additionally electric furnaces are widely adopted.