Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems
Resilience-oriented operation of microgrids in the presence of power-to-hydrogen systems
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DOI：
10.1016/j.apenergy.2023.121429
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发表时间：
2023-10
期刊：
影响因子：
11.2
通讯作者：
Vahid Shahbazbegian;M. Shafie‐khah;H. Laaksonen;G. Strbac;H. Ameli
中科院分区：
文献类型：
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作者：
Vahid Shahbazbegian;M. Shafie‐khah;H. Laaksonen;G. Strbac;H. Ameli
这项研究提出了一种新的框架，用于提高微电网的弹性，该框架基于电力制氢概念和微电网独立运行的能力（即，孤岛模式）。为此，正在开发一种用于微电网弹性运行的模型，其中由电力制氢系统产生的压缩氢可以用于通过燃料电池发电或出售给其他行业。该模型是一个双目标优化问题，它通过（i）减少与主电网的有功功率交换，（ii）减少欧姆功率损耗，以及（iii）增加罐中储存的氢气量来最大限度地降低运行成本和弹性。由于优化问题的混合整数非线性性质，还开发了一种解决方案来处理双目标模型的复杂性，结合目标规划方法和广义Benders分解。结果表明，弹性方法虽然增加了运营成本，但在主网故障时不会导致甩负荷。该研究得出结论，整合分布式发电制氢系统可带来显著的效益，包括减排高达20%，成本节省高达30%。此外，与使用GAMS软件中的商业求解器相比，分解方法的集成将计算性能提高了54%。
This study presents a novel framework for improving the resilience of microgrids based on the power-to-hydrogen concept and the ability of microgrids to operate independently (i.e., islanded mode). For this purpose, a model is being developed for the resilient operation of microgrids in which the compressed hydrogen produced by power-to-hydrogen systems can either be used to generate electricity through fuel cells or sold to other industries. The model is a bi-objective optimization problem, which minimizes the cost of operation and resilience by (i) reducing the active power exchange with the main grid, (ii) reducing the ohmic power losses, and (iii) increasing the amount of hydrogen stored in the tanks. A solution approach is also developed to deal with the complexity of the bi-objective model, combining a goal programming approach and Generalized Benders Decomposition, due to the mixed-integer nonlinear nature of the optimization problem. The results indicate that the resilience approach, although increasing the operation cost, does not lead to load shedding in the event of main grid failures. The study concludes that integrating distributed power-to-hydrogen systems results in significant benefits, including emission reductions of up to 20 % and cost savings of up to 30 %. Additionally, the integration of the decomposition method improves computational performance by 54 % compared to using commercial solvers within the GAMS software.