Abstract

Heating is the largest end-use of energy globally and a major source of emissions. In Sweden, district heating plays a key role and is largely based on renewable and recovered energy, but challenges remain, including peak-load fossil use, biomass competition, and aging infrastructure. This master’s thesis investigates the feasibility of integrating waste heat from data centers and small modular reactors (SMRs) into Swedish district heating systems, focusing on their combined potential. It analyzes how different sizing strategies affect technical performance, economic feasibility, and resource efficiency. This was complemented with a literature review and insights from interviews with experts which illustrated trends within technology, regulations, and energy market. The study is based on a generalized Swedish district heating system using Stockholm climate data. Several scenarios were developed where heat supply from data centers and SMRs were dimensioned according to summer demand, winter demand, and average annual demand.

The qualitative results show that the integration of waste heat is primarily influenced by technical constraints, economic incentives, and regulatory conditions. At the same time, factors such as seasonal demand variations, geographic proximity, and stakeholder collaboration are critical for practical feasibility. Furthermore, dimensioning plays a crucial role in overall performance. A system dimensioned for summer demand achieved a high utilization rate but a relatively low coverage rate, indicating insufficient capacity during winter peaks. In contrast, winter-based dimensioning achieved a high coverage rate but a significantly lower utilization rate, due to excess heat during large parts of the year, resulting in unfavorable economic outcomes. Dimensioning based on average annual demand provided a balanced alternative with more stable economic results.

Economic analysis indicates data center solutions are more sensitive to electricity prices and heat pump performance, while the SMR-based solution generally achieves lower heat production costs (LCOE). The study concludes that the optimal sizing strategy depends strongly on local conditions, including fuel alternatives, electricity prices, and system-specific demand patterns.