Keywords :
Atmospheric modeling; Costs; Electricity and Liquified Natural Gas (LNG) markets; Electricity supply industry; Energy storage; Liquefied natural gas; Liquid Air Energy Storage (LAES); Liquids; LNG regasification; optimal dispatch; probabilistic payback period analysis; Upper bound; Electricity and liquified natural gas market; Liquid air energy storage; Liquified natural gas; Liquified natural gas regasification; Natural gas markets; Optimal dispatch; Payback periods; Probabilistic payback period analyse; Probabilistics; Re-gasification; Upper Bound; Energy Engineering and Power Technology; Electrical and Electronic Engineering
Abstract :
[en] Roadmaps toward a low-carbon renewable energy industry demand substantial bulk energy storages to account for non-dispatchability of renewables. Liquid Air Energy Storage (LAES) has gained recognition as one of few bulk-scale energy storage facilities not limited by geographical requirements, unlike pumped hydro and compressed air energy storage systems. However, the comparatively low efficiency of freestanding LAES facilities hinders their widespread stationing in power and energy systems. There has been a recent uptick of interest in the potential efficiency gains of coupling LAES with the Liquified Natural Gas (LNG) regasification process. Thus, for the first time, this paper presents a day-ahead dispatch model for a LAES coupled with an LNG regasification process (hereafter, LAES-LNG), interacting with electricity and LNG markets as involved energy carriers. Through opportune sequence-aware LNG and electricity procuring, the coupled facility can benefit not only from the released cold energy during the LNG regasification process but also from co-firing regasified air and natural gas to produce and sell electricity. Focusing on a realistic market environment, the proposed dispatch model is derived as a two-stage stochastic setup. The cost-effectiveness of the proposed LAES-LNG facility is validated through a short-term daily test and a probabilistic economic feasibility study.
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