Sustainability insights on emerging solar district heating technologies to boost the nearly zero energy building concept

Arising the Nearly Zero Energy Buildings (NZEB) concept in Europe, the solar district heating systems (SDHS) present a potential solution to meet the buildings sector's European energy performance directive. Nevertheless, current practices face several technological and economical barriers to ensure service quality. In this context, our work presents a sustainability analysis (technical, economic, environmental, and social) for SDHS integration in the residential sector to meet the NZEB and positive energy building goals. This paper proposes an application of a machine learning model incorporating multi-objective optimization and multi-criteria decision making to facilitate a sustainability index for the decision-making stakeholders and policymakers. The proposed analysis application is illustrated through retrofitted residential communities with building energy rating (D) at different sizes (10, 25, 50,100, and 500 houses) located in Emmen (Netherlands) and compared to a standard decentralized heat pump. The optimization results show the ability of SDHS to provide a solar fraction up to 95% in the community of 500 houses. Furthermore, achieving a NZEB status is only approved economically from a community size of 100 houses with a life cycle cost of 41 V/m2 and a payback period of 25 years. These results align with a substantial environmental and social improvement of 78.2% and 29.7%, respectively, compared to the decentralized heat pump. Overall, this study provides policy decision making with an evaluation for positive energy communities and suggests the SDHS integration to meet the global sustainability goals. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study demonstrates the potential of solar district heating systems in the residential sector to meet the Nearly Zero Energy Buildings and positive energy building goals, through sustainability analysis, machine learning model, and multi-objective optimization.