LAUSR

Abstract Distributed energy plants can be relevant to mitigate global energy consumption and carbon emissions. The energy and environmental advantages of these systems can be achieved through an optimal design by considering their life cycle energy consumption and environmental impacts. However, the high technical complexity and the scarcity of life cycle inventory data about distributed energy plants makes their design optimization very challenging. This paper proposes a design optimization methodology for distributed energy plants by accounting for life cycle impacts. The plant comprises renewable energy systems, fossil fuel energy systems and energy storage technologies. The life cycle inventory data are scaled at different sizes by using scaling laws which are obtained by gathering data of commercially available systems of various sizes and from different manufacturers. In this manner, impact scaling curves for the quantification of energy and environmental impacts like fossil cumulative energy demand, global warming potential and abiotic resource depletion are obtained. The validity of the proposed methodology is demonstrated by considering the campus of the University of Parma (Italy) as a case study. A distributed energy plant is optimally designed by using a mixed-integer genetic algorithm to minimize life cycle fossil cumulative energy demand. Moreover, an economic assessment of the optimal configurations is also performed. Compared to a conventional plant, the configuration with combined heat and power systems allows a primary energy saving of about 15% and a reduction of total costs of about 12%, while the configuration with reversible heat pumps is the most expensive.