Building a Sustainable Future: A Three-Stage Risk Management Model for High-Permeability Power Grid Engineering

Weijie Wu, Dongwei Li (), Hui Sun, Yixin Li, Yining Zhang and Mingrui Zhao
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Weijie Wu: Power Grid Planning Research Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Dongwei Li: CEC Technical & Economic Consulting Center of Power Construction, Beijing 100053, China
Hui Sun: Power Grid Planning Research Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Yixin Li: Power Grid Planning Research Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Yining Zhang: Power Grid Planning Research Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Mingrui Zhao: CEC Technical & Economic Consulting Center of Power Construction, Beijing 100053, China

Energies, 2024, vol. 17, issue 14, 1-23

Abstract: Under the background of carbon neutrality, it is important to construct a large number of high-permeability power grid engineering (HPGE) systems, since these can aid in addressing the security and stability challenges brought about by the high proportion of renewable energy. Construction and engineering frequently involve multiple risk considerations. In this study, we constructed a three-stage comprehensive risk management model of HPGE, which can help to overcome the issues of redundant risk indicators, imprecise risk assessment techniques, and irrational risk warning models in existing studies. First, we use the fuzzy Delphi model to identify the key risk indicators of HPGE. Then, the Bayesian best–worst method (Bayesian BWM) is adopted, as well as the measurement alternatives and ranking according to the compromise solution (MARCOS) approach, to evaluate the comprehensive risks of projects; these methods are proven to have more reliable weighting results and a larger sample separation through comparative analysis. Finally, we established an early warning risk model on the basis of the non-compensation principle, which can help prevent the issue of actual risk warning outcomes from being obscured by some indicators. The results show that the construction of the new power system and clean energy consumption policy are the key risk factors affecting HPGE. It was found that four projects are in an extremely high-risk warning state, five are in a relatively high-risk warning state, and one is in a medium-risk warning state. Therefore, it is necessary to strengthen the risk prevention of HPGE and to develop a reasonable closed-loop risk control mechanism.

Keywords: high-permeability power grid engineering; comprehensive risk management; fuzzy Delphi; Bayesian best–worst method; MARCOS approach; early risk warning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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