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A Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Smart Grids

Urheber*innen

Mao,  S.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Dong,  Z.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

/persons/resource/Paul.Schultz

Schultz,  Paul
Potsdam Institute for Climate Impact Research;

Tang,  Y.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Meng,  K.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Dong,  Z. Y.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Qian,  F.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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Zitation

Mao, S., Dong, Z., Schultz, P., Tang, Y., Meng, K., Dong, Z. Y., Qian, F. (2021): A Finite-Time Distributed Optimization Algorithm for Economic Dispatch in Smart Grids. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 4, 2068-2079.
https://doi.org/10.1109/TSMC.2019.2931846


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_23366
Zusammenfassung
The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors' local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.