AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling

Hoofar Hemmatabady, Bastian Welsch, Julian Formhals and Ingo Sass

Applied Energy, 2022, vol. 311, issue C, No S0306261922001209

Abstract: Borehole heat exchanger (BHE) arrays represent a key technology for the future provision of sustainable building heating and cooling energy. They are either used as pure geothermal systems only extracting heating energy from the subsurface or they are also used to store excess heat from solar thermal collectors or waste heat from cooling applications in summer. The diversity of the systems makes it difficult to identify the optimal system in terms of emission reduction and economic efficiency. In this study, we assess the most relevant BHE system layouts for heating-only as well as combined heating and cooling purposes using dynamic simulations of the overall heating system in combination with an enviro-economic analysis method. The assessment routine is used in a multi-objective optimization approach to minimize the different system layouts' emission factor (EF) and their levelized cost of energy (LCOE). In order to cope with the high computational cost of the required long-term considerations, an artificial neural network (ANN) has been used to generate a proxy model in an intermediate step of the multi-objective optimization procedure. This approach delivers reliable optimization results, which reveal, that the lowest emissions for heating and cooling systems are realized by solar-assisted layouts. Comparison with a fossil-based reference layout shows that the most economical BHE layout accomplishes a 60% reduction in the EF with a moderate increase in the LCOE of only 13%. If, however, emission penalty costs are taken into account, the evaluated layouts also become economically advantageous compared to fossil-based systems.

Keywords: District heating and cooling; Borehole thermal energy storage; Environ-economic method; Artificial neural network; TRNSYS; Multi-objective optimization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2022.118652

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