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Adaptive energy optimization strategy of island renewable power-to-hydrogen system with hybrid electrolyzers structure

Energy, ISSN: 0360-5442, Vol: 301, Page: 131508
2024
  • 11
    Citations
  • 0
    Usage
  • 5
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    11
    • Citation Indexes
      11
  • Captures
    5
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Researchers at Shandong University Report New Data on Renewable Energy (Adaptive Energy Optimization Strategy of Island Renewable Power-to-hydrogen System With Hybrid Electrolyzers Structure)

2024 AUG 15 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Current study results on Energy - Renewable Energy have

Article Description

Islands, rich in solar, wind, and wave energy, present an opportunity for sustainable electrolytic hydrogen production. The challenge lies in the variability of 100 % renewable energy, affecting hydrogen output and electrolyzers' lifespan. To address this, a flexible hybrid electrolyzers structure is designed. It cleverly utilizes the low cost and high efficiency of alkaline electrolyzer to absorb the stable renewable energy component, and the rapid response capability of proton exchange membrane electrolyzer to absorb the fluctuating renewable energy component. Thus, it achieves efficient and long-lasting hydrogen production. Then, an operational optimization strategy is proposed to achieve the optimal hydrogen production scheme for this structure. This strategy includes an electrolyzer scheduling model that considers the dynamic process of state transitions and life degradation under the impact of fluctuating power sources. Furthermore, it encompasses an optimization algorithm balanced for scheduling accuracy, solving efficiency, and reduced risk of local optima through a bi-level fuzzy controller, simplifying the search space. Case studies demonstrated the hybrid structure's effectiveness in minimizing lifespan degradation and maximizing profits, validating the optimization method's capacity to quickly find the optimal scheduling plan. Results showed improvements of 6.6 % in annual return, 13.4 % in device lifespan, and 30.1 % in optimization time.

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