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Distributed Economic Dispatch Algorithm With Quantized Communication Mechanism
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AI
DOI：10.1109/TASE.2024.3487214.png)
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Abstract
En 中文
Due to the limited bandwidth and energy of communication channels among agents in practical applications, the communication-efficient distributed optimization method has emerged as a pressing research topic in recent years. The distributed economic dispatch problem with restricted data communication/finite communication bandwidth is investigated in this study, where the communication among agents can be described as a strongly connected directed network. For this purpose, a robust push-pull distributed optimization algorithm with a dynamic scaling quantization mechanism is developed based on the gradient tracking technique. A novel surplus variable is designed to prevent the accumulation of quantization errors, and then, a heavy-ball momentum is introduced to speed up convergence performance. In addition, a linear convergence rate of the developed approach is deduced for the strongly convex and Lipschitz smooth cost function. Finally, we offer two instances for illustration.
Keywords:
Convergence
Quantization (signal)
Heuristic algorithms
Cost function
Bandwidth
Costs
Smart grids
Optimization methods
Graph theory
Energy Internet
Distributed economic dispatch
gradient tracking
heavy-ball momentum
limited communication bandwidth
smart grid
Journal
IF:
6.4
Papers:
4.9K
Citations:
1.6W