A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach

Zhang, Zhuola; Lin, Shiyuan; Ye, Yingjin; Zhao, Xu; Zhao, Yihang; Zhao, Huiru; Sun, Jianyu

doi:10.3390/en15092978

Cited by 14 publications

(4 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BBWM is used here because it considers multi‐nominal distributions during pairwise comparisons (Mohammadi & Rezaei, 2020). Several studies used BBWM methods with a multi‐case approach in different domains, like barrier prioritisation in supply chain logistics (Gupta et al, 2022), health safety and risk calculation (AK et al, 2022), risk measurement (Zhang, Lin, et al, 2022), in blockchain technology (Liu, Lang, et al, 2021), marketing (Zhang, Zhao, et al, 2022), and in operation research (Li et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Gupta

Kharub²,

Shreshth

et al. 2023

Bus Strat Env

View full text Add to dashboard Cite

The agriculture industry is one of India's largest and most important economic contributors. This is vital to the Indian economy since it contributes around 18% to the GDP and employs over 60% of the labour force. Due to the nature of the goods, however, this sector confronts many challenges. An effective agri‐logistics network can be a viable solution, but the sector must be prepared to overcome various risks. Therefore, this study aimed to identify potential risks to the smart, sustainable agri‐logistics industry and strategies for mitigating those risks. Bayesian Best Worst Method (BBWM) was used to prioritise the identified risks and the mitigating strategies. Study results indicate that by obtaining the highest ratings, technological (0.351), social (0.187), and individual (0.169) are the dominating risks to the agri‐logistics sector. Further, it was discovered that combining multiple strategies is more effective than any one strategy alone in reducing the identified risks.

show abstract

Section: Methodsmentioning

confidence: 99%

Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Gupta

Kharub²,

Shreshth

et al. 2023

Bus Strat Env

View full text Add to dashboard Cite

show abstract

“…According to Ref. [33], the objective function is constructed to minimize the maximum absolute differences, as in Equation ( 12):…”

Section: Best-worst Methodsmentioning

confidence: 99%

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

Shao¹,

Wei²,

Liu

et al. 2023

Processes

Self Cite

View full text Add to dashboard Cite

The power sector may reduce carbon emissions and reach carbon neutrality by accelerating the energy transition and lowering its reliance on fossil fuels. However, there are limitations on the new power system’s ability to operate safely and steadily due to the randomness, volatility, and intermittent nature of renewable energy supply. The key to solving this issue is to harness the flexible resources that energy storage systems (ESSs) represent; however, ESSs have more than a value for providing system flexibility. Thus, this study suggested a flexible, technical, economic, and environmental value index system based on multi-criteria decision-making models for evaluating the multi-dimensional value of ESSs. First, the objective and subjective weights are obtained using the CRITIC model and best–worst method; then, the weights are combined using the minimum relative entropy model. The complete values of five typical ESSs are then evaluated using the TOPSIS model. Three scenarios that reflect the traits of the new power system are then created and analyzed. The outcome demonstrates the following: (1) as the new power system continues to grow, the value of ESSs’ flexibility has become ever more crucial; (2) as the need for system flexibility increases, the value of electrochemical ESSs gradually manifests.

show abstract

“…Furthermore, Mohammadi and Rezaei originally put forth the Bayesian BWM approach in 2020 based on the traditional BWM model [55]. The fundamental concept of this technique is to introduce probability distribution via the Bayesian theory, which more accurately captures the process of numerous experts' collaborative decision-making and weighing in a group environment [56]. The following are the fundamental Bayesian BWM steps:…”

Section: Comprehensive Benefit Evaluation Index System For Pgimentioning

confidence: 99%

A Novel Hybrid Power-Grid Investment Optimization Model with Collaborative Consideration of Risk and Benefit

Gao,

Wang,

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

Power-grid investment (PGI) optimization is crucial for boosting investment performance, lowering investment risks, and assuring the sustainable development of power-grid businesses. However, existing studies, which primarily concentrate on financial aspects, have not adequately considered the risk and benefit factors in the process of PGI. In this context, this research suggests a novel hybrid PGI optimization model that collaboratively accounts for the risks and benefits. In the first step, risk and benefit indicator systems for PGI are built, and a comprehensive evaluation model based on the Bayesian best–worst method and TOPSIS is suggested. In the second stage, a PGI optimization model considering the investment amount, power demand, and low-carbon restrictions is further developed based on the evaluation results. Furthermore, the incomprehensible but intelligible-in-time logic algorithm is adopted to solve the problem. By conducting an empirical analysis of ten projects within a power-grid company, the optimal investment plan and a differentiated investment portfolio strategy are obtained by adjusting the key elements.

show abstract

A Hybrid MCDM Model for Evaluating the Market-Oriented Business Regulatory Risk of Power Grid Enterprises Based on the Bayesian Best-Worst Method and MARCOS Approach

Cited by 14 publications

References 43 publications

Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Evaluation of strategies to manage risks in smart, sustainable agri‐logistics sector: A Bayesian‐based group decision‐making approach

Multi-Dimensional Value Evaluation of Energy Storage Systems in New Power System Based on Multi-Criteria Decision-Making

A Novel Hybrid Power-Grid Investment Optimization Model with Collaborative Consideration of Risk and Benefit

Contact Info

Product

Resources

About