4.7 Article

Optimal investment selection of industrial and commercial rooftop distributed PV project based on combination weights and cloud-TODIM model from SMEs' perspectives

期刊

JOURNAL OF CLEANER PRODUCTION
卷 234, 期 -, 页码 534-548

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.06.249

关键词

Industrial and commercial rooftop distributed photovoltaic; Small and medium-sized enterprise; Combination weight; Cloud-TODIM model

资金

  1. Special Project of Cultivation and Development of Innovation Base [Z171100002217024]
  2. Fundamental Research Funds for the Central Universities [2018ZD14]

向作者/读者索取更多资源

Photovoltaic with its main characteristics of clean and abundant reserves has been widely used. This paper investigates how to select a satisfactory industrial and commercial rooftop distributed photovoltaic (ICR-DPV) project to invest from the small and medium-sized enterprises' (SMEs) view. Flaws and inadequacies existing in the current decision-making process may cause an inaccurate investment result. Therefore, this paper establishes a cloud-TODIM framework to deal with the problems. First, criteria covering the economy, resource, risk factors and engineering feasibility are established. Second, hesitant fuzzy linguistic term set (HFLTS) and cloud model are applied to describe the indeterminate information so that the hesitation and randomness of linguistic variables can be fully expressed. Third, the analytic network process (ANP) method and entropy method are combined to gain the criteria weights, which can not only avoid too much subjectivity in weight determination but also measure the mutual influence between the various criteria simultaneously. Furthermore, the TODIM method considers the psychological behavior of investors, so it is utilized to rank alternatives to make the framework more applicable for practical evaluation. Finally, a case in Shandong province validates the applicability of the proposed framework. This paper provides a more rational and scientific decision-making framework for investors. (C) 2019 Elsevier Ltd. All rights reserved.

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