Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory

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: State Grid Zhejiang Electric Power Company, Ltd., Zhuji Power Supply Company, Zhuji 311800, China
Yun Teng: School of Electrical Engineering, Shenyang University of Technology, Shenyang 110000, China
Miaodong Yang: Liaoning Qinghe Power Generation Company Ltd., Tieling 112003, China

Energies, 2024, vol. 17, issue 24, 1-20

Abstract: This study introduces a multi-criteria decision-making (MCDM) framework for optimizing multi-energy network scheduling (MENS). As energy systems become more complex, the need for adaptable solutions that balance consumer demand with environmental sustainability grows. The proposed approach integrates conventional and alternative energy sources, addressing uncertainties through fermatean fuzzy sets (FFS), which enhances decision-making flexibility and resilience. A key component of the framework is the use of stochastic optimization and cooperative game theory (CGT) to ensure efficiency and reliability in energy systems. To evaluate the importance of various scheduling criteria, the study applies the logarithmic percentage change-driven objective weighing (LOPCOW) method, offering a systematic way to assign weights. The weighted aggregated sum product assessment (WASPAS) method is then used to rank potential solutions. The hybrid scheduling alternative, combining distributed and centralized solutions, stands out as the best alternative, significantly improving resource optimization and system resilience. While implementation costs may increase, the hybrid approach balances flexibility and rigidity, optimizing resource use and ensuring system adaptability. This work provides a comprehensive framework that enhances the efficiency and sustainability of energy systems, helping decision-makers address fluctuating demands and renewable energy integration challenges.

Keywords: decision support framework; energy optimization; hybrid scheduling; stochastic optimization; cooperative game theory; energy resource management; renewable energy integration; sustainability in energy systems; flexible decision-making; risk management in 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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