Risk Assessment of PPP Waste to Energy Generation Based on Cloud Model

被引:0
|
作者
Xie, Gaomei [1 ]
Han, Wenhua [1 ]
Wang, Weihua [2 ]
机构
[1] Shanghai Univ Elect Power, Coll Automat Engn, Shanghai, Peoples R China
[2] Guangdong Power Grid Co Ltd, Zhanjiang Power Supply Bur, Power Supply Serv Ctr, Zhanjiang, Guangdong, Peoples R China
关键词
PPP; waste incineration power generation; cloud Model; fuzzy analytic hierarchy process; risk assessment;
D O I
10.1109/PSGEC51302.2021.9542749
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Due to the large number of participants in PPP projects, long operation period, large investment and other factors, the project risk factors have a high degree of ambiguity and randomness. In order to accurately carry out the risk evaluation of PPP projects, this paper proposes a risk evaluation method based on cloud model. Firstly, select the main risk factors and establish a risk factor identification system. At the same time, according to the expert evaluation method and principles of comment index, establish the main risk evaluation index system, and then use improved fuzzy analytic hierarchy process method and entropy weight method to obtain the weight of the risk from the subjective and objective perspectives. According to the reverse cloud model algorithm to obtain the digital characteristics of the secondary risk. The digital characteristics of the secondary risk and the weight are combined to finally obtain the digital characteristics of the total risk of the PPP project, and the forward cloud model generator is used to obtain the cloud model of the total risk. Then according to the cloud model, the risk level standard cloud is given, and the risk level is determined by comparing the similarity of the two cloud images. This paper takes the PPP waste incineration power generation project as an example to verify the proposed method. The results show that the method is effective and scientific, which can provide a reference for future risk assessment of PPP projects.
引用
收藏
页码:209 / 213
页数:5
相关论文
共 50 条
  • [31] Cloud-Model-Based Method for Risk Assessment of Mountain Torrent Disasters
    Yang, Shengmei
    Han, Xianquan
    Cao, Bo
    Li, Bo
    Yan, Fei
    WATER, 2018, 10 (07)
  • [32] Risk Assessment Model and Applied Research on Municipal PPP Projects
    Wang, Liping
    Zhang, Pu
    Zhang, Pei
    Zhao, Yawei
    Ma, Yuan
    PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON ARCHITECTURAL ENGINEERING AND CIVIL ENGINEERING, 2016, 72 : 292 - 297
  • [33] Bridge Construction Risk Assessment Based on Variable Weight Theory and Cloud Model
    Yao, Bo
    Wang, Lianguang
    Gao, Haiyang
    Ren, Lijie
    BUILDINGS, 2024, 14 (03)
  • [34] Cloud model-based seismic risk assessment of road in earthquake region
    Jia, Xingli
    Xu, Jinliang
    Tongji Daxue Xuebao/Journal of Tongji University, 2014, 42 (09): : 1352 - 1358
  • [35] Risk Assessment Based on Combined Weighting-Cloud Model of Tunnel Construction
    Wang, Jing-chun
    Liu, Jia-qi
    Wei, Qiang
    Wang, Peng
    TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2021, 28 (01): : 203 - 210
  • [36] Risk Situation Assessment Model Based on Interdomain Interaction in Cloud Computing Environment
    Wang, Gaocai
    Yu, Ning
    SECURITY AND COMMUNICATION NETWORKS, 2020, 2020 (2020)
  • [37] Research on New Electric Power System Risk Assessment Based on Cloud Model
    Tang, Mingrun
    Li, Ruoyang
    Zhang, Rujia
    Yang, Shuxia
    SUSTAINABILITY, 2024, 16 (05)
  • [38] A Fuzzy Comprehensive Assessment System of Dam Failure Risk Based on Cloud Model
    Jiang, Ying
    Zhang, QiuWen
    JOURNAL OF COMPUTERS, 2013, 8 (04) : 1043 - 1049
  • [39] Cloud Communication based Ship Communication Network Security Risk Assessment Model
    Zhou, Hongzhi
    Yu, Gan
    Li, Linguo
    JOURNAL OF COASTAL RESEARCH, 2020, : 991 - 995
  • [40] Risk Assessment of Debris Flow Based on Multidimensional Connection Normal Cloud Model
    Wang M.
    Wang X.
    Long J.
    Jin J.
    Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering, 2021, 29 (02): : 368 - 375