← 返回
最小化地中海海上风电场的平准化度电成本与视觉影响:一种多目标优化方法
Minimizing Levelized Cost of Energy and visual impact in Mediterranean offshore wind farms: A multi-objective optimization approach
| 作者 | V.F.Barnabe · M.Cont · T.C.M.Ancor · G.Delibr · A.Castorrin · F.Rispol · A.Corsin |
| 期刊 | Energy Conversion and Management |
| 出版日期 | 2025年1月 |
| 卷/期 | 第 344 卷 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | Multi-objective wind farm layout optimization using genetic algorithm. |
语言:
中文摘要
海上风电场正成为欧盟实现2050年净零排放转型的关键发电设施选项。随着在多吉瓦级风电场中安装大型风力涡轮机的趋势日益增长,从海岸线所感知到的视觉影响正受到越来越多的关注。本研究提出了一种优化方法,将视觉影响作为社会可接受性的指标,并结合平准化度电成本(Levelized Cost of Energy, LCOE),为海上风电项目提供全面的技术经济可持续性评估。该方法在一个指定的海洋区域内求解一个多目标、多约束的风电场布局优化问题。每种研究情景下的风电场中,风机数量均为独立变量之一,且从海岸线多个观测点共同参与视觉影响的评估。案例研究设定为位于地中海的一个虚拟风电场,采用15 MW的风力涡轮机。结果生成了一个帕累托前沿,其中权衡解对应一个由13台风机规则分布组成的风电场,其平准化度电成本为110.73 €/MWh。此外,还进行了四项对比分析,以评估(i)不同风机尺寸、(ii)不同尾流损失模型、(iii)不同风资源数据来源以及(iv)不同风电场区域的影响。
English Abstract
Abstract Offshore wind farms are emerging as a key power plant option for EU’s transition to net-zero emissions by 2050. With the growing trend of installing large turbines in multi-gigawatt farms, increasing attention is being given to the visual impact perceived from the coast. This study introduces an optimization method that incorporates visual impact as a social-acceptance indicator and the Levelized Cost of Energy to provide a comprehensive techno-economic sustainability assessment for offshore wind projects. The method resolves a multi-objective and multi-constrained wind farm layout optimization problem in a designated marine area. The number of turbines is one of the independent variables in each studied wind farm and multiple points of observation from the shoreline are contributing to the evaluation on visual impact. The case study is represented by a virtual wind farm located in the Mediterranean Sea, with 15 MW turbines. The results yield a Pareto front, with the trade-off solution represented by a farm with 13 turbines, distributed regularly, and a Levelized Cost of Energy of 110.73 €/MWh. Additionally, four comparative analyses are performed to evaluate the effect of (i) different turbine sizes, (ii) different wake loss models, (iii) different wind data source and (iv) different wind farm areas.
S
SunView 深度解读
该海上风电场多目标优化研究对阳光电源储能系统具有重要借鉴意义。研究中的度电成本(LCOE)优化方法可应用于PowerTitan储能系统与海上风电的配套方案设计,通过ST系列PCS的GFM控制技术平抑风电波动,降低综合度电成本。多约束优化思路可启发iSolarCloud平台开发风储协同优化算法,在满足电网接入、视觉影响等多维约束下,实现储能容量配置与成本的帕累托最优,提升海上新能源项目的技术经济性与社会接受度。