The necessary path towards sustainable development makes increasingly crucial the role of energy storage systems because the most affordable renewable energy sources (RES) are typically intermittent In this perspective compressed air energy storage (CAES) is one of the main alternatives to batteries and pumped hydro energy storage. This study shows how an integrated system coupling RES and CAES may significantly reduce greenhouse gas emissions with acceptable payback times. The electrical load is provided by a representative building sample (RBS) located in Naples (Southern Italy), generated using the simulation-based large-scale uncertainty/sensitivity analysis of building energy performance (SLABE) methodology. A Pareto multi-objective optimization is performed via a brute-force search to minimize CO2-eq emissions and payback time. The design variables refer to a photovoltaic system design and to size and operation of an associated diabatic CAES. Two different application scenarios are envisaged: 1000 buildings and 2000 buildings. The avoided greenhouse gas emissions are about 55% and 51% with simple payback times of 14.3 years and 12.4 years, respectively. The results show that the proposed integrated framework, with the right mix of renewables, may lead to the complete satisfaction of the electrical load at reasonable costs.

Optimization of a diabatic compressed air energy storage coupled with photovoltaics for buildings: CO2-eq emissions vs payback time

Aruta G.;Ascione F.;Bianco N.;
2022

Abstract

The necessary path towards sustainable development makes increasingly crucial the role of energy storage systems because the most affordable renewable energy sources (RES) are typically intermittent In this perspective compressed air energy storage (CAES) is one of the main alternatives to batteries and pumped hydro energy storage. This study shows how an integrated system coupling RES and CAES may significantly reduce greenhouse gas emissions with acceptable payback times. The electrical load is provided by a representative building sample (RBS) located in Naples (Southern Italy), generated using the simulation-based large-scale uncertainty/sensitivity analysis of building energy performance (SLABE) methodology. A Pareto multi-objective optimization is performed via a brute-force search to minimize CO2-eq emissions and payback time. The design variables refer to a photovoltaic system design and to size and operation of an associated diabatic CAES. Two different application scenarios are envisaged: 1000 buildings and 2000 buildings. The avoided greenhouse gas emissions are about 55% and 51% with simple payback times of 14.3 years and 12.4 years, respectively. The results show that the proposed integrated framework, with the right mix of renewables, may lead to the complete satisfaction of the electrical load at reasonable costs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/896860
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