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首页> 外文期刊>Energy Conversion & Management >Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-Ⅱ and CRITIC-TOPSIS
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Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-Ⅱ and CRITIC-TOPSIS

机译：基于改进的NSGA-Ⅱ和批评 - TOPSIS的离网风/ PV /氢气系统的数据驱动配置优化

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摘要

This paper proposes a data-driven two-stage multi-criteria decision-making (MCDM) framework to investigate the optimal configuration of a stand-alone wind/PV/hydrogen system. In the first stage, a modified non-dominated sorting genetic algorithm (NSGA)-II based on reinforcement learning is utilized to determine a set of Pareto solutions. The objectives considered are to minimize the levelized cost of energy (LCOE), the loss of power supply possibility (LPSP) and the power abandonment rate (PAR), simultaneously. In the second stage, the Criteria Importance Though Intercrieria Correlation (CRITIC) method is utilized to determine the weight of the three objectives, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is employed to select the unique best solution from Pareto solutions. To verify the effectiveness, the framework is applied to the wind/PV/hydrogen system located in Aksay Kazak Autonomous County, Gansu Province, China to meet an off-grid industrial park's load demand of 1603 kWh/day and peak load of 117.17 kW. The result states that the optimal system, which consists of 83.2 kW PV panels, 160 kW wind turbines, 20 kW fuel cells, 54 kW electrolyzers and 450 m(3) hydrogen storage tanks, owns the LCOE of 0.226 $/kWh, the LPSP of 4.01% and the PAR of 2.15%.
机译：本文提出了一种数据驱动的两级多标准决策（MCDM）框架，以研究独立风/光伏/氢系统的最佳配置。在第一阶段，利用基于增强学习的修改的非主导分类遗传算法（NSGA）-II来确定一组Pareto解决方案。所考虑的目标是最大限度地减少能量（LCoE）的稳定性成本，电源可能性（LPSP）和电力遗弃率（PAR）同时。在第二阶段，利用中间相关性（批评者）方法的标准重要性来确定三个目标的重量，而采用相似性与理想解决方案（Topsis）方法的顺序偏好技术来选择独特的最佳解决方案帕累托解决方案。为了验证有效性，该框架适用于位于中国甘肃省Aksay哈萨克自治县的风/光伏/氢系统，以满足1203千瓦时/天的载荷需求，达到117.17千瓦的峰值负荷。结果表明，由83.2千瓦光电板，160 kW风力涡轮机，20 kW燃料电池，54 kW电解槽和450米（3）储氢罐的最优系统，拥有0.226 $ / kWh，LPSP的LCOE 4.01％，平价为2.15％。

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  • 来源
    《Energy Conversion & Management》 |2020年第7期|112892.1-112892.18|共18页
  • 作者

    Xu Chuanbo; Ke Yiming; Li Yanbin; Chu Han; Wu Yunna;

  • 作者单位

    North China Elect Power Univ Sch Econ & Management PCR Beijing Peoples R China|North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China;

    North China Elect Power Univ Sch Econ & Management PCR Beijing Peoples R China|North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China;

    North China Elect Power Univ Sch Econ & Management PCR Beijing Peoples R China|North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China;

    North China Elect Power Univ Sch Econ & Management PCR Beijing Peoples R China|North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China;

    North China Elect Power Univ Sch Econ & Management PCR Beijing Peoples R China|North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Wind/PV/hydrogen system; Configuration optimization; Reinforcement learning; NSGA-II; CRITIC; TOPSIS;

    机译：风/光伏/氢气系统;配置优化;加固学习;NSGA-II;评论家;TOPSIS;

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