While the building sector has a significant thermodynamic improvement potential, exergy analysis has been shown to provide new insight for the optimisation of building energy systems. This paper presents an exergy-based multi-objective optimisation tool that aims to assess the impact of a diverse range of retrofit measures with a focus on non-domestic buildings. EnergyPlus was used as a dynamic calculation engine for first law analysis, while a Python add-on was developed to link dynamic exergy analysis and a Genetic Algorithm optimisation process with the aforementioned software. Two UK archetype case studies (an office and a primary school) were used to test the feasibility of the proposed framework. Different measures combinations based on retrofitting the envelope insulation levels and the application of different HVAC configurations were assessed. The objective functions in this study are annual energy use, occupants' thermal comfort, and total building exergy destructions. A large range of optimal solutions was achieved highlighting the framework capabilities. The model achieved improvements of 53% in annual energy use, 51% of exergy destructions and 66% of thermal comfort for the school building, and 50%, 33%, and 80% for the office building. This approach can be extended by using exergoeconomic optimisation

Energy simulation tools have a major role in the assessment of building energy retrofit (BER) measures. Exergoeconomic analysis and optimisation is a common practice in sectors such as the power generation and chemical processes, aiding engineers to obtain more energy-efficient and cost-effective energy systems designs. ExRET-Opt, a retrofit-oriented modular-based dynamic simulation framework has been developed by embedding a comprehensive exergy/exergoeconomic calculation method into a typical open-source building energy simulation tool (EnergyPlus). The aim of this paper is to show the decomposition of ExRET-Opt by presenting modules, submodules and subroutines used for the framework’s development as well as verify the outputs with existing research data. In addition, the possibility to perform multi-objective optimisation analysis based on genetic-algorithms combined with multi-criteria decision making methods was included within the simulation framework. This addition could potentiate BER design teams to perform quick exergy/exergoeconomic optimisation, in order to find opportunities for thermodynamic improvements along the building’s active and passive energy systems. The enhanced simulation framework is tested using a primary school building as a case study. Results demonstrate that the proposed simulation framework provide users with thermodynamic efficient and cost-effective designs, even under tight thermodynamic and economic constraints, suggesting its use in everyday BER practice