Print Email Facebook Twitter A Distributed Augmented Lagrangian Method over Stochastic Networks for Economic Dispatch of Large-Scale Energy Systems Title A Distributed Augmented Lagrangian Method over Stochastic Networks for Economic Dispatch of Large-Scale Energy Systems Author Ananduta, W. (TU Delft Team Bart De Schutter) Ocampo-Martinez, Carlos (Universitat Politecnica de Catalunya) Nedic, Angelia (Arizona State University) Date 2021 Abstract In this paper, we propose a distributed model predictive control (MPC) scheme for economic dispatch of energy systems with a large number of active components. The scheme uses a distributed optimization algorithm that works over random communication networks and asynchronous updates, implying the resiliency of the proposed scheme with respect to communication problems, such as link failures, data packet drops, and delays. The distributed optimization algorithm is based on the augmented Lagrangian approach, where the dual of the considered convex economic dispatch problem is solved. Furthermore, in order to improve the convergence speed of the algorithm, we adapt Nesterov's accelerated gradient method and apply the warm start method to initialize the variables. We show through numerical simulations of a well-known case study the performance of the proposed scheme. Subject AccelerationCommunication networksEconomicsIndex terms -economic dispatchmodel predictive controlmulti-agent optimizationOptimizationPredictive controlProductionStochastic processesstochastic time-varying network To reference this document use: http://resolver.tudelft.nl/uuid:8f220e48-add5-4063-9c74-8f5b8688789d DOI https://doi.org/10.1109/TSTE.2021.3073510 ISSN 1949-3029 Source IEEE Transactions on Sustainable Energy, 12 (4), 1927-1934 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2021 W. Ananduta, Carlos Ocampo-Martinez, Angelia Nedic Files PDF 09405428.pdf 593.69 KB Close viewer /islandora/object/uuid:8f220e48-add5-4063-9c74-8f5b8688789d/datastream/OBJ/view