Electric Power ›› 2024, Vol. 57 ›› Issue (9): 113-123.DOI: 10.11930/j.issn.1004-9649.202403015
• Technical Economy, Planning and Operation, and Policy Mechanisms of Offshore Wind Power Hydrogen Production • Previous Articles Next Articles
Ningbo HUANG1(), Jianwei GAO1(
), Chuanbo XU1, Xuanhua XU2, Shutong ZHAO1, Xunjie GOU3, Xiaojing JIANG1
Received:
2024-03-06
Accepted:
2024-06-04
Online:
2024-09-23
Published:
2024-09-28
Supported by:
Ningbo HUANG, Jianwei GAO, Chuanbo XU, Xuanhua XU, Shutong ZHAO, Xunjie GOU, Xiaojing JIANG. Site Selection of Offshore Wind Power-Hydrogen Production and Refueling Ports Based on Empirical Mining and Hybrid Linguistic Approach[J]. Electric Power, 2024, 57(9): 113-123.
数值 | 语义 | 数值 | 语义 | |||
1 | 完全不自信 | 5 | 高的自信度 | |||
2 | 很低的自信度 | 6 | 很高的自信度 | |||
3 | 低的自信度 | 7 | 完全自信 | |||
4 | 中等的自信度 |
Table 1 Linguistic scales
数值 | 语义 | 数值 | 语义 | |||
1 | 完全不自信 | 5 | 高的自信度 | |||
2 | 很低的自信度 | 6 | 很高的自信度 | |||
3 | 低的自信度 | 7 | 完全自信 | |||
4 | 中等的自信度 |
一级指标 | 二级指标 | 关键词 | ||
自然因素 | 风速 | 风能资源;风速分布;风向变化;长期风速变化;电力生产潜力;能源评估;风力发电效率··· | ||
水深 | 海洋地形;水深测量;海床地质;建设成本;基础建设;水下结构;设计参数;安全评估··· | |||
浪高 | 气象条件;浪高频率;风暴影响;设备稳定性;维护成本;海洋动力学;设计安全;海浪数据··· | |||
用地面积 | 空间规划;建设限制;场址选择;环境影响;土地利用;开发潜力;规模可扩展性;地理条件··· | |||
经济因素 | LCOH | 初始投资成本;运维成本折现;税收折现;利息折现;回收残值;全寿命制氢量折现;现金折现率;折现期数··· | ||
技术因素 | 单位可利用小时数 | 风力发电机效率;运行稳定性;维护周期;故障率;性能监测;能源供应稳定性;可用性分析··· | ||
技术成 熟度 | 技术验证;成本效益;市场接受度;研发进度;技术壁垒;创新应用;技术标准;发展趋势··· | |||
社会因素 | 政府支持 | 政策激励;资金补贴;法规框架;项目批准;政策环境;发展规划;环境评估政策;投资优惠··· | ||
公众支持 | 社会接受度;公众意识;社区参与;意见征询;教育普及;公众关系;社会责任;媒体报道··· | |||
氢气需求 | 市场规模;需求增长;应用领域;消费趋势;价格敏感性;竞争分析;市场潜力;客户调研··· | |||
GDP增长 | 经济贡献;产业发展;就业创造;技术创新;产值增长;投资吸引;地方经济;产业链效应··· | |||
环境因素 | 节能效益 | 能源利用率;节能措施;能源管理;节能技术;效率提升;消耗降低;环保标准;绿色能源··· | ||
碳减排 | 减排目标;温室气体;碳足迹;环境保护;清洁能源;碳交易;碳税;环境影响评估;气候变化··· | |||
风险因素 | 极端天气 | 天气预报;风险评估;应急准备;灾害影响;安全措施;设备防护;风险管理;恢复计划··· | ||
配电可 靠性 | 电网稳定;供电中断;备用电源;电力质量;电网接入;电力供应;运营风险;维护策略··· | |||
气体泄漏 | 安全标准;泄露检测;防泄措施;应急响应;风险控制;维修协议;安全培训;监控系统··· | |||
储运风险 | 物流管理;储存条件;运输安全;风险识别;风险预防;安全规范;应急措施;储运设备··· |
Table 2 Site selection indicator system for OWP-HPRP project
一级指标 | 二级指标 | 关键词 | ||
自然因素 | 风速 | 风能资源;风速分布;风向变化;长期风速变化;电力生产潜力;能源评估;风力发电效率··· | ||
水深 | 海洋地形;水深测量;海床地质;建设成本;基础建设;水下结构;设计参数;安全评估··· | |||
浪高 | 气象条件;浪高频率;风暴影响;设备稳定性;维护成本;海洋动力学;设计安全;海浪数据··· | |||
用地面积 | 空间规划;建设限制;场址选择;环境影响;土地利用;开发潜力;规模可扩展性;地理条件··· | |||
经济因素 | LCOH | 初始投资成本;运维成本折现;税收折现;利息折现;回收残值;全寿命制氢量折现;现金折现率;折现期数··· | ||
技术因素 | 单位可利用小时数 | 风力发电机效率;运行稳定性;维护周期;故障率;性能监测;能源供应稳定性;可用性分析··· | ||
技术成 熟度 | 技术验证;成本效益;市场接受度;研发进度;技术壁垒;创新应用;技术标准;发展趋势··· | |||
社会因素 | 政府支持 | 政策激励;资金补贴;法规框架;项目批准;政策环境;发展规划;环境评估政策;投资优惠··· | ||
公众支持 | 社会接受度;公众意识;社区参与;意见征询;教育普及;公众关系;社会责任;媒体报道··· | |||
氢气需求 | 市场规模;需求增长;应用领域;消费趋势;价格敏感性;竞争分析;市场潜力;客户调研··· | |||
GDP增长 | 经济贡献;产业发展;就业创造;技术创新;产值增长;投资吸引;地方经济;产业链效应··· | |||
环境因素 | 节能效益 | 能源利用率;节能措施;能源管理;节能技术;效率提升;消耗降低;环保标准;绿色能源··· | ||
碳减排 | 减排目标;温室气体;碳足迹;环境保护;清洁能源;碳交易;碳税;环境影响评估;气候变化··· | |||
风险因素 | 极端天气 | 天气预报;风险评估;应急准备;灾害影响;安全措施;设备防护;风险管理;恢复计划··· | ||
配电可 靠性 | 电网稳定;供电中断;备用电源;电力质量;电网接入;电力供应;运营风险;维护策略··· | |||
气体泄漏 | 安全标准;泄露检测;防泄措施;应急响应;风险控制;维修协议;安全培训;监控系统··· | |||
储运风险 | 物流管理;储存条件;运输安全;风险识别;风险预防;安全规范;应急措施;储运设备··· |
方案 | 自然因素 | 经济因素 | ||||||
风速 | 水深/m | 用地面积/km2 | 平均LCOH | |||||
A1 | (7.18,3.26,2.02) | 81 | 39.18 | |||||
A2 | (6.82,2.92,1.84) | 102 | 39.19 | |||||
A3 | (6.61,2.68,1.85) | 55 | 800 | 31.90 | ||||
A4 | (6.56,2.71,1.87) | 65 | 864 | 33.51 | ||||
A5 | (6.57,2.64,1.72) | 59 | 33.51 | |||||
方案 | 技术因素 | 社会因素 | 环境因素 | |||||
可利用小时数/h | GDP增长/亿元 | 节能效益/万t | 碳减排/万t | |||||
A1 | 650.75 | |||||||
A2 | ||||||||
A3 | 433.83 | |||||||
A4 | 468.54 | |||||||
A5 | 572.66 |
Table 3 Quantitative decision information aggregation matrix
方案 | 自然因素 | 经济因素 | ||||||
风速 | 水深/m | 用地面积/km2 | 平均LCOH | |||||
A1 | (7.18,3.26,2.02) | 81 | 39.18 | |||||
A2 | (6.82,2.92,1.84) | 102 | 39.19 | |||||
A3 | (6.61,2.68,1.85) | 55 | 800 | 31.90 | ||||
A4 | (6.56,2.71,1.87) | 65 | 864 | 33.51 | ||||
A5 | (6.57,2.64,1.72) | 59 | 33.51 | |||||
方案 | 技术因素 | 社会因素 | 环境因素 | |||||
可利用小时数/h | GDP增长/亿元 | 节能效益/万t | 碳减排/万t | |||||
A1 | 650.75 | |||||||
A2 | ||||||||
A3 | 433.83 | |||||||
A4 | 468.54 | |||||||
A5 | 572.66 |
方案 | 风速 | 水深 | 用地面积 | LCOH | ||||
A1 | 1.000 | 0.779 | 0.889 | 0.600 | ||||
A2 | 0.775 | 0.600 | 0.600 | 0.600 | ||||
A3 | 0.628 | 1.000 | 1.000 | 1.000 | ||||
A4 | 0.600 | 0.915 | 0.982 | 0.912 | ||||
A5 | 0.651 | 0.966 | 0.929 | 0.912 | ||||
方案 | 可利用小时数 | GDP增长 | 节能效益 | 碳减排 | ||||
A1 | 1.000 | 0.727 | 0.711 | 0.711 | ||||
A2 | 0.751 | 1.000 | 1.000 | 1.000 | ||||
A3 | 0.813 | 0.600 | 0.600 | 0.600 | ||||
A4 | 0.600 | 0.607 | 0.618 | 0.618 | ||||
A5 | 0.780 | 0.671 | 0.671 | 0.671 |
Table 4 Standardized quantitative decision information matrix
方案 | 风速 | 水深 | 用地面积 | LCOH | ||||
A1 | 1.000 | 0.779 | 0.889 | 0.600 | ||||
A2 | 0.775 | 0.600 | 0.600 | 0.600 | ||||
A3 | 0.628 | 1.000 | 1.000 | 1.000 | ||||
A4 | 0.600 | 0.915 | 0.982 | 0.912 | ||||
A5 | 0.651 | 0.966 | 0.929 | 0.912 | ||||
方案 | 可利用小时数 | GDP增长 | 节能效益 | 碳减排 | ||||
A1 | 1.000 | 0.727 | 0.711 | 0.711 | ||||
A2 | 0.751 | 1.000 | 1.000 | 1.000 | ||||
A3 | 0.813 | 0.600 | 0.600 | 0.600 | ||||
A4 | 0.600 | 0.607 | 0.618 | 0.618 | ||||
A5 | 0.780 | 0.671 | 0.671 | 0.671 |
方案 | 技术成熟度 | 政府支持 | 公众支持 | 氢气需求 | ||||
A1 | 0.60 | 1.00 | 1.00 | 0.60 | ||||
A2 | 0.60 | 1.00 | 1.00 | 0.73 | ||||
A3 | 0.69 | 0.60 | 0.60 | 1.00 | ||||
A4 | 1.00 | 0.71 | 0.60 | 0.86 | ||||
A5 | 1.00 | 0.71 | 0.60 | 0.73 | ||||
方案 | 极端天气 | 配电可靠性 | 气体泄漏 | 储运风险 | ||||
A1 | 0.60 | 0.60 | 0.60 | 0.62 | ||||
A2 | 0.60 | 0.60 | 0.60 | 0.62 | ||||
A3 | 1.00 | 1.00 | 0.73 | 0.60 | ||||
A4 | 0.80 | 0.74 | 1.00 | 1.00 | ||||
A5 | 0.80 | 0.74 | 1.00 | 1.00 |
Table 5 Standardized qualitative decision information matrix
方案 | 技术成熟度 | 政府支持 | 公众支持 | 氢气需求 | ||||
A1 | 0.60 | 1.00 | 1.00 | 0.60 | ||||
A2 | 0.60 | 1.00 | 1.00 | 0.73 | ||||
A3 | 0.69 | 0.60 | 0.60 | 1.00 | ||||
A4 | 1.00 | 0.71 | 0.60 | 0.86 | ||||
A5 | 1.00 | 0.71 | 0.60 | 0.73 | ||||
方案 | 极端天气 | 配电可靠性 | 气体泄漏 | 储运风险 | ||||
A1 | 0.60 | 0.60 | 0.60 | 0.62 | ||||
A2 | 0.60 | 0.60 | 0.60 | 0.62 | ||||
A3 | 1.00 | 1.00 | 0.73 | 0.60 | ||||
A4 | 0.80 | 0.74 | 1.00 | 1.00 | ||||
A5 | 0.80 | 0.74 | 1.00 | 1.00 |
一级指标 | 二级指标 | 属性权重 | ||
自然因素 | 风速 | 8.11 | ||
水深 | 4.05 | |||
用地面积 | 5.41 | |||
经济因素 | LCOH | 8.11 | ||
技术因素 | 可利用小时数 | 5.41 | ||
技术成熟度 | 5.26 | |||
环境因素 | 节能效益 | 5.41 | ||
碳减排 | 5.41 | |||
社会因素 | 政府支持 | 7.89 | ||
公众支持 | 7.89 | |||
氢气需求 | 6.58 | |||
GDP增长 | 8.11 | |||
风险因素 | 极端天气 | 3.95 | ||
配电可靠性 | 3.95 | |||
气体泄漏 | 6.58 | |||
储运风险 | 7.89 |
Table 6 Attribute weight information 单位:%
一级指标 | 二级指标 | 属性权重 | ||
自然因素 | 风速 | 8.11 | ||
水深 | 4.05 | |||
用地面积 | 5.41 | |||
经济因素 | LCOH | 8.11 | ||
技术因素 | 可利用小时数 | 5.41 | ||
技术成熟度 | 5.26 | |||
环境因素 | 节能效益 | 5.41 | ||
碳减排 | 5.41 | |||
社会因素 | 政府支持 | 7.89 | ||
公众支持 | 7.89 | |||
氢气需求 | 6.58 | |||
GDP增长 | 8.11 | |||
风险因素 | 极端天气 | 3.95 | ||
配电可靠性 | 3.95 | |||
气体泄漏 | 6.58 | |||
储运风险 | 7.89 |
指标权重 | 评分 | |||||||||||
定性 | 定量 | A1 | A2 | A3 | A4 | A5 | ||||||
1.0 | 0 | |||||||||||
0.9 | 0.1 | |||||||||||
0.8 | 0.2 | |||||||||||
0.7 | 0.3 | |||||||||||
0.6 | 0.4 | |||||||||||
0.5 | 0.5 | |||||||||||
0.4 | 0.6 | |||||||||||
0.3 | 0.7 | |||||||||||
0.2 | 0.8 | |||||||||||
0.1 | 0.9 | |||||||||||
0 | 1.0 |
Table 7 Scheme evaluating for different combinations of subjective and objective weighting
指标权重 | 评分 | |||||||||||
定性 | 定量 | A1 | A2 | A3 | A4 | A5 | ||||||
1.0 | 0 | |||||||||||
0.9 | 0.1 | |||||||||||
0.8 | 0.2 | |||||||||||
0.7 | 0.3 | |||||||||||
0.6 | 0.4 | |||||||||||
0.5 | 0.5 | |||||||||||
0.4 | 0.6 | |||||||||||
0.3 | 0.7 | |||||||||||
0.2 | 0.8 | |||||||||||
0.1 | 0.9 | |||||||||||
0 | 1.0 |
1 |
SHI M F, LI X M, XU C B. Two-stage site selection of hydrogen refueling stations coupled with gas stations considering cooperative effects based on the CRITIC-ITFAHP-MABAC method: a case study in Beijing[J]. International Journal of Hydrogen Energy, 2024, 49, 1274- 1292.
DOI |
2 |
XU M J, WU Y N, LIAO Y J, et al. Optimal sites selection of oil-hydrogen combined stations considering the diversity of hydrogen sources[J]. International Journal of Hydrogen Energy, 2023, 48 (3): 1043- 1059.
DOI |
3 |
ZHANG W X, GENG X L, CHENG S, et al. Simultaneous evaluation of criteria and alternatives method-based site selection for solar hydrogen production plant in Inner Mongolia, China[J]. Sustainable Energy Technologies and Assessments, 2024, 61, 103583.
DOI |
4 |
WU Y N, DENG Z Q, TAO Y, et al. Site selection decision framework for photovoltaic hydrogen production project using BWM-CRITIC-MABAC: a case study in Zhangjiakou[J]. Journal of Cleaner Production, 2021, 324, 129233.
DOI |
5 |
GULERIA A, BAJAJ R K. A robust decision making approach for hydrogen power plant site selection utilizing (R, S)-norm pythagorean fuzzy information measures based on VIKOR and TOPSIS method[J]. International Journal of Hydrogen Energy, 2020, 45 (38): 18802- 18816.
DOI |
6 |
WU Y N, HE F Y, ZHOU J L, et al. Optimal site selection for distributed wind power coupled hydrogen storage project using a geographical information system based multi-criteria decision-making approach: a case in China[J]. Journal of Cleaner Production, 2021, 299, 126905.
DOI |
7 |
KARIPOĞLU F, SERDAR GENÇ M, AKARSU B. GIS-based optimal site selection for the solar-powered hydrogen fuel charge stations[J]. Fuel, 2022, 324, 124626.
DOI |
8 |
WAN Q F, XU X H, HAN J. A dimensionality reduction method for large-scale group decision-making using TF-IDF feature similarity and information loss entropy[J]. Applied Soft Computing, 2024, 150, 111039.
DOI |
9 |
WU D, YANG R X, SHEN C. Sentiment word co-occurrence and knowledge pair feature extraction based LDA short text clustering algorithm[J]. Journal of Intelligent Information Systems, 2021, 56 (1): 1- 23.
DOI |
10 | QIU D, ZHENG Q. Improving TextRank algorithm for automatic keyword extraction with tolerance rough set[J]. International Journal of Fuzzy Systems, 2022, 24 (3): 1332- 1342. |
11 | RICHARZ J, WEGEWITZ S, HENN S, et al. Graph-based research field analysis by the use of natural language processing: an overview of German energy research[J]. Technological Forecasting and Social Change, 2023, 186 122139. |
12 |
KORACH Z T, YANG J, ROSSETTI S C, et al. Mining clinical phrases from nursing notes to discover risk factors of patient deterioration[J]. Int J Med Inform, 2020, 135, 104053.
DOI |
13 | 徐选华, 黄丽, 陈晓红. 基于共词网络的群智知识挖掘方法: 在应急决策中应用[J]. 管理科学学报, 2023, 26 (5): 121- 137. |
XU Xuanhua, HUANG Li, CHEN Xiaohong. Collectire intelligence knowledge mining method based on co-word network: application in emergency decision-making[J]. Journal of Management Sciences in China, 2023, 26 (5): 121- 137. | |
14 |
QIN J D, LI M X, WANG X J, et al. Collaborative emergency decision-making: a framework for deep learning with social media data[J]. International Journal of Production Economics, 2024, 267, 109072.
DOI |
15 | GUDIVADA V N, RAO D L, GUDIVADA A R. Information retrieval: concepts, models, and systems[M]//Handbook of Statistics. Amsterdam: Elsevier, 2018: 331–401. |
16 | DRŽÍK D, ŠTEFLOVIČ K. Text vectorization techniques based on wordnet[J]. Journal of Linguistics, 2023, 74 (1): 310- 322. |
17 |
JI S H, SATISH N, LI S, et al. Parallelizing Word2Vec in shared and distributed memory[J]. IEEE Transactions on Parallel and Distributed Systems, 2019, 30 (9): 2090- 2100.
DOI |
18 |
GOEL A, MAJUMDAR A. Contrastive deep convolutional transform k-means clustering[J]. Information Sciences, 2024, 661, 120191.
DOI |
19 | YU H, ZHOU C H, BAO J C, et al. Analysis and effect evaluation of offshore wind power output characteristics based on Gaussian mixed clustering[J]. Procedia Computer Science, 2023, 224 (C): 389- 394. |
20 | REBAFKA T. Model-based clustering of multiple networks with a hierarchical algorithm[J]. Statistics and Computing, 2024, (1): 527- 542. |
21 |
GOU X J, XU Z S, WANG X X, et al. Managing consensus reaching process with self-confident double hierarchy linguistic preference relations in group decision making[J]. Fuzzy Optimization and Decision Making, 2021, 20 (1): 51- 79.
DOI |
22 |
LIU W Q, DONG Y C, CHICLANA F, et al. Group decision-making based on heterogeneous preference relations with self-confidence[J]. Fuzzy Optimization and Decision Making, 2017, 16 (4): 429- 447.
DOI |
23 | LIU W Q, ZHANG H J, CHEN X, et al. Managing consensus and self-confidence in multiplicative preference relations in group decision making[J]. Knowledge-Based Systems, 2018, 162 (C): 62- 73. |
24 | GOU X J, LIAO H C, XU Z S, et al. Group decision making with double hierarchy hesitant fuzzy linguistic preference relations: consistency based measures, index and repairing algorithms and decision model[J]. Information Sciences: an International Journal, 2019, 489 (C): 93- 112. |
25 |
MILLET I. The effectiveness of alternative preference elicitation methods in the analytic hierarchy process[J]. Journal of Multi-Criteria Decision Analysis, 1997, 6 (1): 41- 51.
DOI |
26 |
CHICLANA F, HERRERA-VIEDMA E, ALONSO S, et al. Cardinal consistency of reciprocal preference relations: a characterization of multiplicative transitivity[J]. IEEE Transactions on Fuzzy Systems, 2009, 17 (1): 14- 23.
DOI |
27 |
尹儇鹏, 徐选华, 陈晓红. 风险视域下的大群体应急决策策略选择研究[J]. 系统工程理论与实践, 2021, 41 (3): 678- 690.
DOI |
YIN Xuanpeng, XU Xuanhua, CHEN Xiaohong. Study on the selection of large group emergency decision-making strategies under the perspective of risk[J]. Systems Engineering-Theory & Practice, 2021, 41 (3): 678- 690.
DOI |
|
28 | 高建伟, 黄宁泊, 缑迅杰, 等. 基于对偶自信双层语言偏好关系的多属性群决策方法[J]. 系统科学与数学, 2024, 44 (4): 935- 961. |
GAO Jianwei, HUANG Ningbo, GOU Xunjie, et al. Multi-attribute group decision-making method based on dual-layer linguistic preference relations with self-confidence[J]. Systems Science and Mathematical Sciences, 2024, 44 (4): 935- 961. | |
29 | KAHNEMAN D, TVERSKY A. Prospect theory: an analysis of decision under risk[M]//Decision, Probability and Utility. Cambridge: Cambridge University Press, 1988: 183–214. |
30 | 李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995, 32 (6): 15- 20. |
31 |
李德毅, 刘常昱. 论正态云模型的普适性[J]. 中国工程科学, 2004, 6 (8): 28- 34.
DOI |
LI Deyi, LIU Changyu. Study on the universality of the normal cloud model[J]. Engineering Science, 2004, 6 (8): 28- 34.
DOI |
|
32 |
LU H W, REN L X, CHEN Y Z, et al. A cloud model based multi-attribute decision making approach for selection and evaluation of groundwater management schemes[J]. Journal of Hydrology, 2017, 555, 881- 893.
DOI |
33 | 徐选华, 王佩, 蔡晨光. 基于云相似度的语言偏好信息多属性大群体决策方法[J]. 控制与决策, 2017, 32 (3): 459- 466. |
XU Xuanhua, WANG Pei, CAI Chenguang. Linguistic multi-attribute large group decision-making method based on similarity measurement of cloud model[J]. Control and Decision, 2017, 32 (3): 459- 466. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||