Distributed Algorithm for Nonsmooth Resource Allocation Problems With Nonlinear Constraints and Its Application to Smart Grids

In this article, we study nondifferentiable resource allocation problems (RAPs). In our problem, the decisions of agents are subject to coupling inequality constraints, local inequality constraints, and local convex set constraints. In contrast to existing RAPs, the cost functions are nonsmooth and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE systems journal Jg. 17; H. 2; S. 1 - 10
Hauptverfasser: Deng, Zhenhua, Chen, Tao, Zhao, Yan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Computational geometry
Convergence
Convexity
Cost function
Costs
Distributed algorithms
Graph theory
Inclusions
multiagent systems
nonlinear constraints
nonsmooth analysis
Optimization
Power dispatch
Resource allocation
Resource management
Smart grid
Smart grids
ISSN:1932-8184, 1937-9234
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this article, we study nondifferentiable resource allocation problems (RAPs). In our problem, the decisions of agents are subject to coupling inequality constraints, local inequality constraints, and local convex set constraints. In contrast to existing RAPs, the cost functions are nonsmooth and the inequality constraints are nonsmooth and nonlinear in our problem. Based on differential inclusions and projection methods, we exploit a fully distributed subgradient-based resource allocation algorithm to optimally allocate the network resources. With the help of the set-valued LaSalle invariance principle, we prove the global convergence of the algorithm to the optimal resource allocation of our problem. Finally, our method is applied to the economic dispatch problems of smart grids. With our method, the generations of generating units converge to the optimal power generation.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2022.3228242