Privacy-Preserving Push-Based Distributed Implicit Gradient-Tracking Algorithm with Added Noises for Economic Dispatch in Microgrids

The distributed economic dispatch problem (EDP) in microgrids faces challenges related to the privacy leakage. This paper proposes a privacy-preserving push-based distributed implicit gradient-tracking algorithm. We utilize column stochastic matrices along with the push-sum protocol, enabling the al...

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Vydáno v:Chinese Control and Decision Conference s. 3719 - 3724
Hlavní autoři: Gong, Wei, Liu, Bing
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 16.05.2025
Témata:
Convergence
distributed algorithm
Distributed algorithms
Economic dispatch
Economics
Faces
implicit gradient-tracking
Microgrids
Noise
Privacy
privacy preservation
Protection
Protocols
Simulation
ISSN:1948-9447
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Shrnutí:The distributed economic dispatch problem (EDP) in microgrids faces challenges related to the privacy leakage. This paper proposes a privacy-preserving push-based distributed implicit gradient-tracking algorithm. We utilize column stochastic matrices along with the push-sum protocol, enabling the algorithm to be effectively applied to directed graphs, thereby ensuring compatibility with non-symmetric communication network, commonly found in distributed systems. In addition, by employing the implicit gradient-tracking method, our algorithm reduces communication and storage overhead compared to traditional gradient-tracking approaches, as it avoids exchanging an auxiliary variable used for tracking the global average gradient. Furthermore, we introduce conditional noises to the exchanged information, preventing external eavesdroppers with system information from inferring private information. With a properly chosen step size, it is proved that our algorithm ensures linear convergence while maintaining privacy protection. Finally, we validate the effectiveness of the proposed algorithm through simulations conducted on the IEEE 14-bus system.
ISSN:1948-9447
DOI:10.1109/CCDC65474.2025.11091037