Optimal Sizing of Isolated Renewable Power Systems With Ammonia Synthesis: Model and Solution Approach

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. Ho...

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Bibliographic Details
Published in:IEEE transactions on power systems Vol. 39; no. 5; pp. 6372 - 6385
Main Authors: Yu, Zhipeng, Lin, Jin, Liu, Feng, Li, Jiarong, Zhao, Yuxuan, Song, Yonghua
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ammonia
Chain branching
Chemical synthesis
Combined C&C and B&B algorithm
Costs
Decision theory
Hydrogen
information gap decision theory (IGDT)
Investment
isolated renewable power to ammonia (IRePtA)
Markov chains
Mathematical programming
Mixed integer
mixed-integer linear fractional programming (MILFP)
Planning
Power sources
Renewable energy sources
Sizing
Uncertainty
ISSN:0885-8950, 1558-0679
Online Access:Get full text
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Summary: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 (3645 RMB/t) in long-run simulations.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2024.3360315