Optimized Grid Partitioning and Scheduling in Multi-Energy Systems Using a Hybrid Decision-Making Approach

Peng Liu (), Tieyan Zhang, Furui Tian, Yun Teng and Miaodong Yang
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Peng Liu: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Tieyan Zhang: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Furui Tian: Zhuji Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Zhuji 311800, China
Yun Teng: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Miaodong Yang: Liaoning Qinghe Power Generation Co., Ltd., Tieling 112003, China

Energies, 2024, vol. 17, issue 13, 1-21

Abstract: This paper presents a thorough review of our state-of-the-art technique for enhancing dynamic grid partitioning and scheduling in multi-energy source systems. We use a hybrid approach to T-spherical fuzzy sets, combining the alternative ranking order method accounting for the two-step normalization (AROMAN) method for alternating ranking order to enable two-step normalisation with the method based on removal effects of criteria (MEREC) for eliminating criteria effects. This enables us to obtain the highest level of accuracy from our findings. To ascertain the relative importance of these criteria, we use MEREC to perform a rigorous examination of the influence that each evaluation criterion has on the outcomes of the decision-making process. In addition, we use AROMAN to provide a strong foundation for assessing potential solutions by accounting for spherical fuzzy sets to account for any ambiguity. We illustrate how our approach successfully considers several factors, such as social acceptability, technical feasibility, environmental sustainability, and economic feasibility, through the analysis of an extensive case study. Our approach provides decision-makers (DMs) with a rigorous and rational framework for assessing and choosing the best grid division and scheduling options. This is done in an effort to support the administration and design of resilient and sustainable multi-energy systems. This research contributes to the growing body of knowledge in this area by offering insights that help to direct policy, planning, and investment decisions in the shift towards more sustainable energy infrastructures. Moreover, it adds to the growing body of information on multi-criteria decision-making (MCDM) in energy system optimization.

Keywords: AROMAN; MEREC; grid partitioning; sustainable energy; T-spherical fuzzy sets; multi-energy systems (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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