Academic Journal
SDecPP-DC: State Decomposition Privacy-Preserving Optimization Algorithm for Incentive-Based Demand Response in Smart Grid.
| Τίτλος: | SDecPP-DC: State Decomposition Privacy-Preserving Optimization Algorithm for Incentive-Based Demand Response in Smart Grid. |
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| Συγγραφείς: | Bi, Yang, Dong, Tao |
| Πηγή: | Mathematics (2227-7390); May2026, Vol. 14 Issue 9, p1511, 15p |
| Θεματικοί όροι: | Energy demand management, Data privacy, Mathematical optimization, Electronic data processing, Smart power grids, Iterative methods (Mathematics), Synchronization |
| Περίληψη: | This paper considers the incentive-based demand response (IDR) economic dispatch problem (EDP) in smart grid while preserving the privacy of sensitive information, where the sensitive information is the consumers' electricity consumption. The incentive-based demand response (IDR) optimization objective function as an EDP is established. A novel state decomposition-based privacy-preserving distributed consensus algorithm (SDecPP-DC) is designed to address this EDP, where the state decomposition mechanism is proposed to preserve the privacy of sensitive information. The feedback gains in the SDecPP-DC algorithm for the mismatch variables are non-coordinated and constant. The convergence of the proposed SDecPP-DC algorithm is theoretically proved by using multi-parameter perturbation theory. It is shown that the SDecPP-DC algorithm can deal with the directed network topology with a row-stochastic matrix, and the convergence point is the optimal solution of EDP. Finally, the correctness and effectiveness of SDecPP-DC are confirmed by the experiments. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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