  Isolated renewable power to ammonia (IRePtA) has been recognized as a
promising way to decarbonize the chemical industry. Optimal sizing of the
renewable power system is significant to improve the techno-economic of IRePtA
since the investment of power sources exceeds 80\% of the total investment.
However, multi-timescale electricity, hydrogen, and ammonia storages, minimum
power supply for system safety, and the multi-year uncertainty of renewable
generation lead to difficulties in planning. To address the issues above, an
IGDT-MILFP model is proposed. First, the levelized cost of ammonia (LCOA) is
directly formulated as the objective, rendering a mixed integer linear
fractional programming (MILFP) problem. Information gap decision theory (IGDT)
is utilized to handle the multi-year uncertainty of renewable generation.
Second, a combined Charnes-Cooper (C&C) transformation and Branch-and-Bound
(B&B) method is proposed to efficiently solve the large-scale IGDT-MILFP model,
giving robust and opportunistic planning results. Then, Markov Chain Monte
Carlo (MCMC) sampling-based posteriori analysis is leveraged to quantify the
long-run performance. Finally, a real-life system in Inner Mongolia, China, is
studied. The results indicate that the proposed methods could reduce the
computational burden by orders of magnitude for solving a large-scale MILFP
problem. Moreover, the proposed IGDT-MILFP model is necessary and accurate to
obtain an optimal capacity allocation with the lowest expected LCOA (3610
RMB/t) in long-run simulations.
