This paper proposes three coordination laws for optimal energy generation and distribution in energy network, which is composed of physical flow layer and cyber communication layer. The physical energy flows through the physical layer; but all the energies are coordinated to generate and flow by distributed coordination algorithms on the basis of communication information. First, distributed energy generation and energy distribution laws are proposed in a decoupled manner without considering the interactive characteristics between the energy generation and energy distribution. Second, a joint coordination law to treat the energy generation and energy distribution in a coupled manner taking account of the interactive characteristics is designed. Third, to handle over- or less-energy generation cases, an energy distribution law for networks with batteries is designed. The coordination laws proposed in this paper are fully distributed in the sense that they are decided optimally only using relative information among neighboring nodes. Through numerical simulations, the validity of the proposed distributed coordination laws is illustrated.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TCYB.2017.2669041DOI Listing

Publication Analysis

Top Keywords

energy generation
20
energy distribution
16
distributed coordination
12
energy
12
coordination laws
12
generation energy
12
optimal energy
8
generation distribution
8
laws proposed
8
interactive characteristics
8

Similar Publications

Evidence for a metal-bosonic insulator-superconductor transition in compressed sulfur.

Proc Natl Acad Sci U S A

January 2025

State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China.

The abrupt drop of resistance to zero at a critical temperature is a key signature of the current paradigm of the metal-superconductor transition. However, the emergence of an intermediate bosonic insulating state characterized by a resistance peak preceding the onset of the superconducting transition has challenged this traditional understanding. Notably, this phenomenon has been predominantly observed in disordered or chemically doped low-dimensional systems, raising intriguing questions about the generality of the effect and its underlying fundamental physics.

View Article and Find Full Text PDF

Protein dynamics underlies strong temperature dependence of heat receptors.

Proc Natl Acad Sci U S A

January 2025

Department of Physiology and Biophysical Sciences, State University of New York at Buffalo, Buffalo, NY 14214.

Ion channels are generally allosteric proteins, involving specialized stimulus sensor domains conformationally linked to the gate to drive channel opening. Temperature receptors are a group of ion channels from the transient receptor potential family. They exhibit an unprecedentedly strong temperature dependence and are responsible for temperature sensing in mammals.

View Article and Find Full Text PDF

A hybrid meta on-top functional for multiconfiguration pair-density functional theory.

Proc Natl Acad Sci U S A

January 2025

Department of Chemistry, Chemical Theory Center, University of Minnesota, Minneapolis, MN 55455-0431.

Multiconfiguration pair-density functional theory (MC-PDFT) was proposed a decade ago, but it is still in the early stage of density functional development. MC-PDFT uses functionals that are called on-top functionals; they depend on the density and the on-top pair density. Most MC-PDFT calculations to date have been unoptimized translations of generalized gradient approximations (GGAs) of Kohn-Sham density functional theory (KS-DFT).

View Article and Find Full Text PDF

Prediction of Thermodynamic Properties of C-Based Fullerenols Using Machine Learning.

J Chem Theory Comput

January 2025

Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China.

Traditional machine learning methods face significant challenges in predicting the properties of highly symmetric molecules. In this study, we developed a machine learning model based on graph neural networks (GNNs) to accurately and swiftly predict the thermodynamic and photochemical properties of fullerenols, such as C(OH) ( = 1 to 30). First, we established a global method for generating fullerenol isomers through isomer fingerprinting, which can generate all possible isomers or produce diverse structural types on demand.

View Article and Find Full Text PDF

Heterojunctions, known for their decent separation of photo-generated electrons and holes, are promising for photocatalytic CO reduction. However, a significant obstacle in traditional post-assembled heterojunctions is the high interfacial barrier for charge transfer caused by atomic lattice mismatch at multiphase interfaces. Here, as research prototypes, the study creates a lattice-matched co-atomic interface within CsPbBr-CsPbBr polytypic nanocrystals (113-125 PNs) through the proposed in situ hybrid strategy to elucidate the underlying charge transfer mechanism within this unique interface.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!