Flexible and efficient renewable-power-to-methane concept enabled by liquid CO2 energy storage: Optimization with power allocation and storage sizing

Meng Qi, Jaewon Lee, Seokyoung Hong, Jeongdong Kim, Yi Liu, Jinwoo Park and Il Moon

Energy, 2022, vol. 256, issue C

Abstract: Power-to-methane (PtM) coupled with renewables requires an energy buffer to ensure a steady and flexible operation. Liquid CO2 energy storage (LCES) is an emerging energy storage concept with considerable round-trip efficiency (53.5%) and energy density (47.6 kWh/m3) and can be used as both an energy and material (i.e., CO2) buffer in the PtM process. Integration of LCES with the PtM process realizes co-production of methane and electricity, supports peak shaving of the power grid, and enhances profitability via the sale of electricity at peak hours. This study aims to design, optimize, and comprehensively evaluate the techno-economic performance of the PtM-LCES process using a renewable power mix of solar and wind. A process scheduling model is formulated to understand the impacts of storage sizing and power allocation on the process performance. Then, artificial neural network-based surrogate optimization was performed to establish a cost-optimal design. Investigation at Kramer Junction, California, found that the production cost of methane was 161.6 €/MWh with an efficiency of 76.2% and a renewables penetration of 78.7%. This cost could be reduced by improving LCES's performance and considering electricity price arbitrage in areas with high electricity prices. The electricity price arbitrage and improvement of LCES's performance could lower the cost to 83.8 and ∼60 €/MWh, respectively. Moreover, zero or negative carbon emissions can be achieved if the renewables penetration can be increased to 91.5%. Results indicate that the PtM-LCES process is both energy efficient and economically viable, through which renewable methane can be cost-competitive with fossil natural gas.

Keywords: Power-to-methane (PtM); Liquid CO2 energy storage (LCES); Flexible operation; Hybrid energy storage; Artificial neural network; Surrogate modeling (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:256:y:2022:i:c:s0360544222014864

DOI: 10.1016/j.energy.2022.124583

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