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考虑海上制氢的岛屿多目标优化调度

Multi-objective optimal scheduling of islands considering offshore hydrogen production.

作者信息

Wang Yirui, Tian Ruize, Zheng Siguang, Lu Changyue, Zhou Shuang

机构信息

International Education Institute, North China Electric Power University, Beijing, China.

School of Electrical Engineering, Southwest Jiaotong University, Chengdu, Sichuan, China.

出版信息

Sci Rep. 2025 Jul 28;15(1):27371. doi: 10.1038/s41598-025-05313-5.

DOI:10.1038/s41598-025-05313-5
PMID:40717136
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12301489/
Abstract

Ocean islands possess abundant renewable energy resources, providing favorable conditions for developing offshore clean energy microgrids. However, geographical isolation poses significant challenges for direct energy transfer between islands. Recent electrolysis and hydrogen storage technology advancements have created new opportunities for distributed energy utilization in these remote areas. This paper presents a low-carbon economic dispatch strategy designed explicitly for distant oceanic islands, incorporating energy self-sufficiency rates and seasonal hydrogen storage (SHS). We propose a power supply model for offshore islands considering hydrogen production from offshore wind power. The proposed model minimizes operational and carbon emission costs while maximizing energy self-sufficiency. It considers the operational constraints of the island's energy system, the offshore transportation network, the hydrogen storage infrastructure, and the electricity-hydrogen-transportation coupling of hydrogen storage (HS) and seasonal hydrogen storage (SHS) services. To optimize the dispatch process, this study employs an improved Grey Wolf Optimizer (IGWO) combined with the Differential Evolution method to enhance population diversity and refine the position updating mechanism. Simulation results demonstrate that integrating HS and SHS effectively enhances energy self-sufficiency and reduces carbon emissions. For instance, hydrogenation costs decreased by 21.4% after optimization, and the peak-valley difference was reduced by 16%. These findings validate the feasibility and effectiveness of the proposed approach.

摘要

海洋岛屿拥有丰富的可再生能源资源,为发展海上清洁能源微电网提供了有利条件。然而,地理隔离给岛屿之间的直接能源传输带来了重大挑战。近期电解和氢存储技术的进步为这些偏远地区的分布式能源利用创造了新机遇。本文提出了一种专门为偏远海洋岛屿设计的低碳经济调度策略,纳入了能源自给率和季节性氢存储(SHS)。我们提出了一种考虑海上风电制氢的离岛供电模型。该模型在将运营成本和碳排放成本降至最低的同时,使能源自给率最大化。它考虑了岛屿能源系统、海上运输网络、氢存储基础设施以及氢存储(HS)和季节性氢存储(SHS)服务的电 - 氢 - 运输耦合的运行约束。为了优化调度过程,本研究采用了改进的灰狼优化器(IGWO)与差分进化方法相结合,以增强种群多样性并优化位置更新机制。仿真结果表明,整合HS和SHS有效地提高了能源自给率并减少了碳排放。例如,优化后加氢成本降低了21.4%,峰谷差降低了16%。这些结果验证了所提方法的可行性和有效性。

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3
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