Thermo-economic optimization of an adiabatic compressed air energy storage system including system dynamics
[EN]Adiabatic compressed air energy storage is a promising, in-development technology for storing renewable energy, for instance, from wind parks or photovoltaic installations. This work presents a multi-objective thermoeconomic optimization analysis. It is based on a dynamic model of the plant’s th...
| Authors: | , , , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2026 |
| Country: | España |
| Institution: | Universidad de Salamanca (USAL) |
| Repository: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/169838 |
| Online Access: | http://hdl.handle.net/10366/169838 |
| Access Level: | Open access |
| Keyword: | Thermo-mechanical energy storage Adiabatic compressed air energy storage Radial packed-bed systems Dynamical integrated model Multi-objective optimization Thermo-economic objective functions |
| Summary: | [EN]Adiabatic compressed air energy storage is a promising, in-development technology for storing renewable energy, for instance, from wind parks or photovoltaic installations. This work presents a multi-objective thermoeconomic optimization analysis. It is based on a dynamic model of the plant’s thermodynamic performance, in which the dynamics of the thermal energy storage (packed-bed type) and the charge and discharge processes of the air reservoir are solved in detail. A plant configuration, as determined from previous work in our group, with a priori good round-trip efficiencies (around 0.76–0.78), is considered the starting point. It encompasses two-stage compression and expansion trains, along with two radial packedbeds (utilizing either sensible or phase-change materials) to capitalize on the cooling between compression steps. In the developed optimization procedure, the levelized cost of storage (LCoS) and the total capital expenditure (CAPEX) are taken as key performance indicators. The decision variables include, among others, mass flows, thermal energy storage dimensions, maximum and minimum cavern pressures, and the symmetry of the pressure ratios between compressors and turbines. The optimization procedure uses an NSGA-II genetic algorithm. One of the main novelties of the work is that accurate dynamic simulations have been used to obtain Pareto fronts. They are analyzed from different perspectives: the size, geometry, and materials of the packedbeds; the type of compressor (axial or centrifugal); energetic factors such as input and output energy and power; the maximum pressures in the cavern; and the mass flows in the charge and discharge processes. Values of LCoS are calculated with precision using realistic input data, resulting in approximately 80 e/MWh for a plant capable of storing 600 MWh (reference power of 200 MW for charge periods of 3 h) and electricity prices during charge of 50 e/MWh. The specific parameters and configurations that lead to those LCoS levels are made explicit. Furthermore, the influence of cavern costs, charging electricity prices, and idle time is analyzed in detail. |
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