Comprehensive evaluation research of hybrid energy systems driven by renewable energy based on fuzzy multi-criteria decision-making

Autor: Xiangyu Chen, Chunsheng Chen, Guang Tian, Yang Yang, Yunhao Zhao
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Energy Research, Vol 11 (2023)
Druh dokumentu: article
ISSN: 2296-598X
DOI: 10.3389/fenrg.2023.1294391
Popis: The worsening of climate conditions is closely related to the large amount of carbon dioxide produced by human use of fossil fuels. Under the guidance of the goal of “carbon peaking and carbon neutrality goals”, with the deepening of the structural reform of the energy supply side, the hybrid energy system coupled with renewable energy has become an important means to solve the energy problem. This paper focuses on the comprehensive evaluation of hybrid energy systems. A complete decision support system is constructed in this study. The system primarily consists of four components: 1) Twelve evaluation criteria from economic, environmental, technological, and socio-political perspectives; 2) A decision information collecting and processing method in uncertain environment combining triangular fuzzy numbers and hesitation fuzzy language term sets; 3) A comprehensive weighting method based on Lagrange optimization theory; 4) Solution ranking based on the fuzzy VIKOR method that considers the risk preferences of decision-makers. Through a case study, it was found that the four most important criteria are investment cost, comprehensive energy efficiency, dynamic payback period and energy supply reliability with weights of 7.21%, 7.17%, 7.17%, and 7.15% respectively. A1 is the scheme with the best comprehensive benefit. The selection of solutions may vary depending on the decision-maker’s risk preference. Through the aforementioned research, the decision framework enables the evaluation of the overall performance of the system and provides decision-making references for decision-makers in selecting solutions.
Databáze: Directory of Open Access Journals