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

Mao, S.; Dong, Z.; Schultz, Paul; Tang, Y.; Meng, K.; Dong, Z. Y.; Qian, F.

doi:10.1109/TSMC.2019.2931846

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

このアイテムの新しいバージョンが利用可能です:
https://publications.pik-potsdam.de/pubman/item/item_23366_6

詳細要約

公開

学術論文

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

Authors

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;

URL

There are no locators available

フルテキスト (公開)

There are no public fulltexts stored in PIKpublic

付随資料 (公開)

There is no public supplementary material available

引用

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. doi:10.1109/TSMC.2019.2931846.

引用: https://publications.pik-potsdam.de/pubman/item/item_23366

要旨

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.