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Many-objective virtual power plants resource scheduling based on evolutionary multifactorial optimization
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AI
DOI：10.1016/j.eswa.2026.132186.png)
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Abstract
En 中文
• We proposed a many-objective model to address multiple uncertainties with VPP resource scheduling. • We introduce meta-learning to optimize the population and parameters of the evolutionary algorithm. • Conduct scenario simulations and experiments using real-world data to highlight innovative.
Keywords:
Many-objective optimization
Virtual power plants
Resource scheduling
Evolutionary algorithms
Meta-learning
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
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