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

Peng Liu,

Tieyan Zhang,

Furui Tian

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6386 - 6386

Published: Dec. 19, 2024

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

Language: Английский

Energy Trading Strategies for Integrated Energy Systems Considering Uncertainty DOI Creative Commons
Jin Gao, Zhenguo Shao, Feixiong Chen

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(4), P. 935 - 935

Published: Feb. 15, 2025

To improve the stable operation and promote energy sharing of integrated system (IES), a comprehensive trading strategy considering uncertainty is proposed. Firstly, an IES model incorporating power-to-gas (P2G) carbon capture (CCS) established to reduce emissions. Secondly, this into four-level robust optimization address fluctuation renewable sources in operations. This not only considers probability distribution scenarios its output, but also effectively reduces model’s conservatism by constructing multi-interval set. On basis, Nash–Harsanyi bargaining method used solve issue benefit allocation among multiple IESs. Finally, solved using distributed algorithm that ensures equitable benefits while protecting privacy each IES. The simulation results validate effectiveness proposed strategy.

Language: Английский

Citations

1
Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm DOI Creative Commons
Ghulam Abbas, Zhi Wu, Aamir Ali

et al.

IET Generation Transmission & Distribution, Journal Year: 2024, Volume and Issue: 18(16), P. 2625 - 2649

Published: July 16, 2024

Abstract This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, flexibility. A novel multi‐objective optimization framework is introduced this study minimize supply costs, emissions, losses while improving voltage deviation (VD) stability index (VSI). The proposed comprising normal boundary intersection (NBI) decomposition‐based evolutionary algorithms (DBEA) determines optimal siting sizing renewable‐based distributed resources, considering load demand variations intermittency solar outputs. comparative analysis establishes that strategy performs better than many contemporary algorithms, specifically when all objective functions are optimized simultaneously. validation was carried out on standard IEEE‐33 bus test network, which demonstrates significant percentage savings costs (49.6%), emission rate (62.2%), loss (92.3%), along enormous improvements VSI (91.9%) VD (99.8953%). obtained results categorically underline efficiency, robustness approach employed any complex network multiple renewable sources systems.

Language: Английский

Citations

5
Efficient optimal power flow learning: A deep reinforcement learning with physics-driven critic model DOI
Ahmed Rabee Sayed, Khaled Al Jaafari, Xian Zhang

et al.

International Journal of Electrical Power & Energy Systems, Journal Year: 2025, Volume and Issue: 167, P. 110621 - 110621

Published: March 29, 2025

Language: Английский

Citations

0
Hybrid Decision Support Framework for Energy Scheduling Using Stochastic Optimization and Cooperative Game Theory DOI Creative Commons

Peng Liu,

Tieyan Zhang,

Furui Tian

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(24), P. 6386 - 6386

Published: Dec. 19, 2024

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

Language: Английский

Citations

0