With the pressing need to address climate change and reduce greenhouse gas emissions, governments around the world have set ambitious climate goals, necessitating a transi- tion to renewable energy sources. In the Netherlands specifically, the government has recognized the potential of district heating networks as a vital strategy for decarboniza- tion, given that 81.5% of domestic energy consumption is dedicated to thermal loads. The development of new energy assets, the integration of storage solutions, the use of in- termittent renewable energy sources, and the inclusion of multiple energy carriers such as fuels, power, and heat, collectively referred to as hybrid renewable energy systems, have made the energy infrastructure more complex than ever before. The challenge lies in understanding the implications of integrating diverse renewable assets and optimiz- ing the system for both reliability and economic feasibility, as optimal sizing in hybrid renewable energy systems remains insufficiently understood. This study aims to answer the following research question: What is an effective approach to capacity optimisation in renewable energy systems that integrate thermal and power sources with hybrid energy storage? This study adopts both qualitative and quantitative research approaches, employing multi-actor analysis, system design, optimization techniques, and data analysis to de- termine the optimal sizing of the identified system components. The study integrates real-world data from Eneco’s district heating network in Utrecht, employing optimisa- tion models to minimise costs while ensuring a reliable supply of heat for the connected households. The research addresses sub-questions related to optimisation techniques, hybrid system design, and operational performance. The study results show that a systems engineering approach to capacity optimization in hybrid renewable energy systems can provide robust solutions to the challenges of balancing reliability, economic feasibility, and sustainability in energy infrastructure. By taking an integrated approach that spans multi-actor analysis, system design, opti- mization design, model development, and result analysis, relevant system components can not only be identified but also capacity-optimized. Throughout the study we have shown that single-layer optimisation using mixed integer linear programming provides the most accurate results in diverse hybrid systems with complex asset dispatch. Fur- thermore, we identified masked time resolution adjustment as the highest-performing simplification technique, achieving a 91.58% reduction in solution time while showing minimal differences in results compared to full optimization. For Utrecht’s district heat- ing network specifically, we showed that a renewable hybrid system, relying on thermal energy storage, power-to-heat, and CO2 compensated fuel-to-heat, is economically and technically feasible up until an operational power-to-heat fraction of 85%.