Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727081 | PMC |
http://dx.doi.org/10.1007/s11228-021-00622-z | DOI Listing |
Sci Rep
January 2025
GE Renewable Energy, Noida, India.
In this research, demand response impact on the hosting capacity of solar photovoltaic for distribution system is investigated. The suggested solution model is formulated and presented as a tri-objective optimization that consider maximization of solar PV hosting capacity (HC), minimization of network losses (Loss) and maintaining node voltage deviation (V) within acceptable limits. These crucial objectives are optimized simultaneously as well as individually.
View Article and Find Full Text PDFNeural Netw
December 2024
The School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China. Electronic address:
To tackle high communication costs and privacy issues in Centralized Federated Learning (CFL), Decentralized Federated Learning (DFL) is an alternative. However, a significant discrepancy exists between local updates and the expected global update, known as client drift, which arises from inconsistency and heterogeneous data. Previous research in the DFL field has focused on local information during client updates, without considering global information, which fails to alleviate the client drift issue.
View Article and Find Full Text PDFSci Adv
January 2025
Department of Applied Physics and Energy Sciences Institute, Yale University, New Haven, CT 06511, USA.
Photonic design is a process of mathematical optimization of a desired objective (beam formation, mode conversion, etc.) subject to the constraint of Maxwell's equations. Finding the optimal design is challenging: Generically, these problems are highly nonconvex and finding global optima is NP hard.
View Article and Find Full Text PDFIEEE Trans Comput Aided Des Integr Circuits Syst
January 2025
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China. Dr. Luo is also with the Center for Energy-efficient Computing and Applications, Peking University, Beijing, China.
The feasibility-seeking approach offers a systematic framework for managing and resolving intricate constraints in continuous problems, making it a promising avenue to explore in the context of floorplanning problems with increasingly heterogeneous constraints. The classic legality constraints can be expressed as the union of convex sets. However, conventional projection-based algorithms for feasibility-seeking do not guarantee convergence in such situations, which are also heavily influenced by the initialization.
View Article and Find Full Text PDFNeural Netw
December 2024
School of Mathematics and Statistics, Yili Normal University, Yining 835000, China.
In this paper, a recurrent neural network is proposed for distributed nonconvex optimization subject to globally coupled (in)equality constraints and local bound constraints. Two distributed optimization models, including a resource allocation problem and a consensus-constrained optimization problem, are established, where the objective functions are not necessarily convex, or the constraints do not guarantee a convex feasible set. To handle the nonconvexity, an augmented Lagrangian function is designed, based on which a recurrent neural network is developed for solving the optimization models in a distributed manner, and the convergence to a local optimal solution is proven.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!