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An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China

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  • Wu, Yunna
  • Xu, Chuanbo
  • Ke, Yiming
  • Chen, Kaifeng
  • Sun, Xiaokun

Abstract

Selecting a rational large-scale rooftop photovoltaic (LSR-PV) project portfolio is critical to the realization of long-term strategy objectives for PV enterprises. Difficulties related to LSR-PV project portfolio selection result from factors including uncertainties of decision-making environment, various properties of evaluation attributes and interactions between projects. However, the existing researches on portfolio selection have not solved these problems well. In this work, combining of fuzzy multi-attribute decision-making and fuzzy multi-objective programming, an integrated framework is proposed to address these issues simultaneously. First, the assessment values of attributes, objective functions and constrains all take the form of triangular intuitionistic fuzzy numbers (TIFNs) to fully describe the uncertainties inherent to LSR-PV project portfolio selection. Later, the weights of attributes determined by Analytic Hierarchy Process (AHP) are incorporated within PROMETHEE II method to sort the LSR-PV project alternatives by selecting preference functions and setting parameters for each attribute. Then, a fuzzy 0–1 programming model is formulated and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) algorithm is introduced to capture an optimal-Pareto set under objectives of benefit maximization and installed capacity maximization. Finally, to validate the effectiveness of the proposed framework, a case study of Zhejiang province is conducted and a comparative analysis is carried out.

Suggested Citation

  • Wu, Yunna & Xu, Chuanbo & Ke, Yiming & Chen, Kaifeng & Sun, Xiaokun, 2018. "An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China," Energy, Elsevier, vol. 143(C), pages 295-309.
    • Handle: RePEc:eee:energy:v:143:y:2018:i:c:p:295-309
    DOI: 10.1016/j.energy.2017.10.105
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