Scholarlink

<p>Cold Ironing (CI) is a proven strategy for reducing ship emissions at berth; however, its large, stochastic electricity demand creates significant technical and economic stress on port energy systems when supplied exclusively by the utility grid. This study proposes a smart sizing framework for a grid-connected Hybrid Renewable Energy System integrating photovoltaic and wind generation to sustainably supply CI operations. Using real operational data from a Mediterranean port, a high-resolution energy model is coupled with a Genetic Algorithm–based capacity optimization to determine the optimal renewable mix that minimizes both the levelized cost of energy and the carbon footprint of shore-side electrification. The resulting hybrid system is designed to maximize on-site renewable penetration while using the grid only as a balancing resource under load and resource uncertainty. Results show that the optimized configuration substantially reduces grid dependency and delivers major emission abatement compared with both grid-only CI and auxiliary-engine operation. The study demonstrates that smart capacity sizing, rather than real-time dispatch control, is the critical enabler of techno-economic and environmental viability for renewable-powered CI, and it provides a scalable, process-oriented decision-support framework for the design of sustainable port energy infrastructures.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Genetic-algorithm-based smart sizing of hybrid renewable energy systems for stochastic high-power cold ironing loads

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月26日

查看全文
  • Dimitrios Cholidis,
  • Nikolaos Sifakis,
  • Alexandros Chachalis,
  • George Arampatzis

摘要

Cold Ironing (CI) is a proven strategy for reducing ship emissions at berth; however, its large, stochastic electricity demand creates significant technical and economic stress on port energy systems when supplied exclusively by the utility grid. This study proposes a smart sizing framework for a grid-connected Hybrid Renewable Energy System integrating photovoltaic and wind generation to sustainably supply CI operations. Using real operational data from a Mediterranean port, a high-resolution energy model is coupled with a Genetic Algorithm–based capacity optimization to determine the optimal renewable mix that minimizes both the levelized cost of energy and the carbon footprint of shore-side electrification. The resulting hybrid system is designed to maximize on-site renewable penetration while using the grid only as a balancing resource under load and resource uncertainty. Results show that the optimized configuration substantially reduces grid dependency and delivers major emission abatement compared with both grid-only CI and auxiliary-engine operation. The study demonstrates that smart capacity sizing, rather than real-time dispatch control, is the critical enabler of techno-economic and environmental viability for renewable-powered CI, and it provides a scalable, process-oriented decision-support framework for the design of sustainable port energy infrastructures.

Graphical Abstract