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Journal articles on the topic 'Energy Distribution'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Vasudev, Arpitha, A. M. Sowmya, and G. Manjula. "Applying Intermittent Energy Distribution for Evading Energy Holes in Wireless Sensor Network." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 217–19. http://dx.doi.org/10.9756/bijsesc.8281.

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2

Conti, Stefania, Santi A. Rizzo, Nunzio Salerno, and Giuseppe M. Tina. "Distribution network topology identification based on synchrophasor." AIMS Energy 6, no. 2 (2018): 245–60. http://dx.doi.org/10.3934/energy.2018.2.245.

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3

Lee, Soon-myung, and Jeong-Uk Kim. "The Application Method of DC Distribution in Microgrid." Journal of Energy Engineering 25, no. 1 (March 31, 2016): 92–99. http://dx.doi.org/10.5855/energy.2015.25.1.092.

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4

Feijóo, Andrés, and Daniel Villanueva. "Polynomial approximations of the Normal toWeibull Distribution transformation." AIMS Energy 2, no. 4 (2014): 342–58. http://dx.doi.org/10.3934/energy.2014.4.342.

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5

Moncecchi, Matteo, Davide Falabretti, and Marco Merlo. "Regional energy planning based on distribution grid hosting capacity." AIMS Energy 7, no. 3 (2019): 264–84. http://dx.doi.org/10.3934/energy.2019.3.264.

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6

Kavousi-Fard, Abdollah, and Amin Khodaei. "Multi-objective optimal operation of smart reconfigurable distribution grids." AIMS Energy 4, no. 2 (2016): 206–21. http://dx.doi.org/10.3934/energy.2016.2.206.

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7

Mashud Hyder, Md, and Kaushik Mahata. "Reconfiguration of distribution system using a binary programming model." AIMS Energy 4, no. 3 (2016): 461–80. http://dx.doi.org/10.3934/energy.2016.3.461.

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8

Cavanagh, Ralph, and Richard Sonstelie. "Energy Distribution Monopolies." Electricity Journal 11, no. 7 (August 1998): 13–23. http://dx.doi.org/10.1016/s1040-6190(98)00066-9.

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9

Bansal, Manoj. "Optimization Modelling for Renewable Energy Resources based Distribution Generation." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 1510–19. http://dx.doi.org/10.47059/revistageintec.v11i3.2027.

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10

ZAHIRUDDIN, Mohd, and Masanori KUNIEDA. "E35 Energy Distribution into Micro EDM Electrodes(Electrical machining)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2009.5 (2009): 835–40. http://dx.doi.org/10.1299/jsmelem.2009.5.835.

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11

Charalampopoulos, Constantinos, Constantinos S. Psomopoulos, George Ch. Ioannidis, and Stavors D. Kaminaris. "Implementing the EcoDesign Directive in distribution transformers: First impacts review." AIMS Energy 5, no. 1 (2017): 113–24. http://dx.doi.org/10.3934/energy.2017.1.113.

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12

Poullikkas, Andreas, Savvas Papadouris, George Kourtis, and Ioannis Hadjipaschalis. "Storage Solutions for Power Quality Problems in Cyprus Electricity Distribution Network." AIMS Energy 2, no. 1 (2014): 1–17. http://dx.doi.org/10.3934/energy.2014.1.1.

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13

Trung, Tran Thai, Seon-Ju Ahn, and Joon-Ho Choi. "Real Time Simulation of Distribution System with Distributed Energy Resources." Journal of Clean Energy Technologies 3, no. 1 (2015): 57–61. http://dx.doi.org/10.7763/jocet.2015.v3.169.

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14

SUN, Shuping, Zhongwei JIANG, and Haibin WANG. "1204 Heart Sound Clustering Method Using Time-Frequency Distribution Energy." Proceedings of Conference of Chugoku-Shikoku Branch 2010.48 (2010): 365–66. http://dx.doi.org/10.1299/jsmecs.2010.48.365.

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15

Araujo, J., F. Giroire, J. Moulierac, Y. Liu, and R. Modrzejewski. "Energy Efficient Content Distribution." Computer Journal 59, no. 2 (November 6, 2015): 192–207. http://dx.doi.org/10.1093/comjnl/bxv095.

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16

Cascarini de Torre, L. E., and E. J. Bottani. "Adsorption energy distribution functions." Colloids and Surfaces A: Physicochemical and Engineering Aspects 116, no. 3 (September 1996): 285–91. http://dx.doi.org/10.1016/0927-7757(96)03630-8.

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17

Halpern, Paul, and Michael Pecorino. "The Localized Energy Distribution of Dark Energy Star Solutions." ISRN Astronomy and Astrophysics 2013 (March 7, 2013): 1–4. http://dx.doi.org/10.1155/2013/939876.

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We examine the question of energy localization for an exact solution of Einstein's equations with a scalar field corresponding to the phantom energy interpretation of dark energy. We apply three different energy-momentum complexes, the Einstein, the Papapetrou, and the Møller prescriptions, to the exterior metric and determine the energy distribution for each. Comparing the results, we find that the three prescriptions yield identical energy distributions.

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18

Kim, Ho-Young, Seul-Ye Lim, and Seung-Hoon Yoo. "Analysis of residential natural gas consumption distribution function in Korea - a mixture model." Journal of Energy Engineering 23, no. 3 (September 30, 2014): 36–41. http://dx.doi.org/10.5855/energy.2014.23.3.036.

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19

Lai, Baixi, Ping Yi, Yu Sui, and Qingquan Zhang. "Energy distribution in EV energy network under energy shortage." Neurocomputing 444 (July 2021): 179–88. http://dx.doi.org/10.1016/j.neucom.2020.08.090.

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20

Al Qaisi, Zeid, Qais Alsafasfeh, and Ahmad Harb. "Stability impact of integrated small scale hybrid (PV/Wind) system with electric distribution network." AIMS Energy 6, no. 5 (2018): 832–45. http://dx.doi.org/10.3934/energy.2018.5.832.

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21

Chiodo, Elio, Pasquale De Falco, Luigi Pio Di Noia, and Fabio Mottola. "Inverse Log-logistic distribution for Extreme Wind Speed modeling: Genesis, identification and Bayes estimation." AIMS Energy 6, no. 6 (2018): 926–48. http://dx.doi.org/10.3934/energy.2018.6.926.

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22

Komali, K., and G. Mutyalamma. "Energy Efficient Routing Protocols for Node Distribution in Wireless Sensor Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (December 31, 2017): 1335–40. http://dx.doi.org/10.31142/ijtsrd8224.

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23

Khan, Baseem, Hassan Haes Alhelou, and Fsaha Mebrahtu. "A holistic analysis of distribution system reliability assessment methods with conventional and renewable energy sources." AIMS Energy 7, no. 4 (2019): 413–29. http://dx.doi.org/10.3934/energy.2019.4.413.

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24

A. Al-Ammar, Essam, Ghazi A. Ghazi, Wonsuk Ko, and Hamsakutty Vettikalladi. "Temperature impact assessment on multi-objective DGs and SCBs placement in distorted radial distribution systems." AIMS Energy 8, no. 2 (2020): 320–38. http://dx.doi.org/10.3934/energy.2020.2.320.

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25

Gill, Amandeep, Pushpendra Singh, Jalpa H. Jobanputra, and Mohan Lal Kolhe. "Placement analysis of combined renewable and conventional distributed energy resources within a radial distribution network." AIMS Energy 10, no. 6 (2022): 1216–29. http://dx.doi.org/10.3934/energy.2022057.

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<abstract> <p>System islanding, relay tripping, and reverse power flow-like issues in the distribution network are all caused by randomly placed distributed energy resources. To minimize such problems, distributed energy resource (DER) optimal placement in the radial distribution network (RDN) is essential to reduce power loss and enhance the voltage profile. When placing DERs, consideration of constraints like size, location, number, type, and power factor (PF) should be considered. For optimal placement, renewable and nonrenewable DERs are considered. The effects of different types and PFs of DER placements have been tested on the IEEE 33 bus RDN to satisfy all limitations. Using various intelligent techniques, distributed energy resource units of optimal type, PF, size, quantity, and position were placed in the IEEE 33 bus RDN. These intelligent strategies for minimizing power loss, enhancing the voltage profile, and increasing the convergence rate are based on an adaptive neuro-fuzzy inference system, a genetic algorithm, and enhanced particle swarm optimization.</p> </abstract>

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26

Seki, T., and J. Matsuo. "Energy distribution of high-energy cluster ion beams." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 261, no. 1-2 (August 2007): 647–50. http://dx.doi.org/10.1016/j.nimb.2007.04.004.

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27

Guo, Ran, and Jiulin Du. "Energy distribution and energy fluctuation in Tsallis statistics." Physica A: Statistical Mechanics and its Applications 391, no. 9 (May 2012): 2853–59. http://dx.doi.org/10.1016/j.physa.2011.12.059.

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28

Rizzo, Santi Agatino. "Editorial to the 'Special Issue—Distribution network reliability in Smart Grids and Microgrids' of AIMS Energy." AIMS Energy 10, no. 3 (2022): 533–34. http://dx.doi.org/10.3934/energy.2022026.

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29

Simpemba, Prospery. "Spectral energy distribution of blazars." Proceedings of the International Astronomical Union 15, S356 (October 2019): 374. http://dx.doi.org/10.1017/s1743921320003464.

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AbstractThis study focuses on spectral energy distributions and light-curves of blazars and radio galaxies, and the testing of the existing models with a view to appropriately predict a new model that will nearly accurately present the nature of the energy outflows of these super-massive bodies. Understanding blazar emission is very important as it relates more directly to the physics of the AGN’s central black hole. X-ray, radio and gamma-ray wavelength range data on blazars and radio galaxies from archived data has been collected and a detailed investigation of the spectral energy distribution patterns of the blazars and radio galaxies carried out so as to fit the data in the various models. The results of this investigation will be discussed in detail in this presentation.

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30

S.B, Sanukrishnan. "A Cognitive Energy Distribution System." International Journal of Peer to Peer Networks 5, no. 2 (May 31, 2014): 1–15. http://dx.doi.org/10.5121/ijp2p.2014.5201.

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31

Pavlenko, Ya V., H. R. A. Jones, Yu Lyubchik, J. Tennyson, and D. J. Pinfield. "Spectral energy distribution for GJ406." Astronomy & Astrophysics 447, no. 2 (February 2006): 709–17. http://dx.doi.org/10.1051/0004-6361:20052979.

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32

Kriventsov, A. N., and V. I. Kuz'min. "Energy distribution in explosion welding." Welding International 19, no. 10 (October 2005): 814–20. http://dx.doi.org/10.1533/wint.2005.3530.

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33

Dalgaard, Carl-Johan, and Holger Strulik. "Energy distribution and economic growth." Resource and Energy Economics 33, no. 4 (November 2011): 782–97. http://dx.doi.org/10.1016/j.reseneeco.2011.04.004.

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34

Giani, S., A. V. Bagulya, and V. M. Grichine. "Synchrotron radiation energy loss distribution." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 452, no. 1-2 (September 2000): 179–84. http://dx.doi.org/10.1016/s0168-9002(00)00420-4.

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35

Awbi, H. B. "Energy efficient room air distribution." Renewable Energy 15, no. 1-4 (September 1998): 293–99. http://dx.doi.org/10.1016/s0960-1481(98)00176-1.

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36

SHARIF, M., and M. AZAM. "ENERGY–MOMENTUM DISTRIBUTION: SOME EXAMPLES." International Journal of Modern Physics A 22, no. 10 (April 20, 2007): 1935–51. http://dx.doi.org/10.1142/s0217751x0703515x.

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In this paper, we elaborate the problem of energy–momentum in General Relativity with the help of some well-known solutions. In this connection, we use the prescriptions of Einstein, Landau–Lifshitz, Papapetrou and Möller to compute the energy–momentum densities for four exact solutions of the Einstein field equations. We take the gravitational waves, special class of Ferrari–Ibanez degenerate solution, Senovilla–Vera dust solution and Wainwright–Marshman solution. It turns out that these prescriptions do provide consistent results for special class of Ferrari–Ibanez degenerate solution and Wainwright–Marshman solution but inconsistent results for gravitational waves and Senovilla–Vera dust solution.

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37

Llorca, Jaime, Antonia M. Tulino, Matteo Varvello, Jairo Esteban, and Diego Perino. "Energy Efficient Dynamic Content Distribution." IEEE Journal on Selected Areas in Communications 33, no. 12 (December 2015): 2826–36. http://dx.doi.org/10.1109/jsac.2015.2485618.

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38

Kilambi, Gopal C. "Energy distribution of Be stars." Symposium - International Astronomical Union 162 (1994): 422–24. http://dx.doi.org/10.1017/s0074180900215556.

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Be stars are defined to be non-supergiant early-type stars of spectral type B showing at times Balmer emission lines in their spectra. These stars often develop strong stellar winds considered to be variable in nature (Slettebak 1988) and have high rotational velocities compared to normal stars of similar spectral types. They also tend to show an excess amount of energy in the near- and far-infrared region compared to normal stars which is presumed to be due the surrounding material around the central star. Thus, the observed energy is a combination of that due to the stellar source and the surrounding material. Various attempts have been made to disentangle the stellar energy component from that of the circumstellar component in order to understand the nature, size and temperature of the envelope. These include:a) Radius determination based on IR excess (Gehrz et al. 1974, Dachs and Hanuschik 1984; Waters et al. 1987),b) Radius estimates from polarization and spectrophotometric data (Jones 1979),c) Envelope dimensions derived from the width of shell absorption cores (Kogure 1969; Hirata and Kogure 1977),d) Dachs et al. (1992) attempted to understand the physical properties, flow patterns and density distribution of the gas by a comparison of synthetic emission line profiles and empirical profiles measured for real Be stars.

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39

Singh, Markandey, and Jai Prakash Chaturvedi. "Energy distribution for comet Halley." Earth, Moon and Planets 39, no. 2 (October 1987): 197–201. http://dx.doi.org/10.1007/bf00054062.

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40

Stamboliadis, Elias Th. "The energy distribution theory of comminution specific surface energy, mill efficiency and distribution mode." Minerals Engineering 20, no. 2 (February 2007): 140–45. http://dx.doi.org/10.1016/j.mineng.2006.07.009.

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41

Sukkiramathi, K., and C. V. Seshaiah. "Some Characteristics of Weibull Distribution and its Contribution to Wind Energy Analysis." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 197–202. http://dx.doi.org/10.31142/ijtsrd21685.

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42

Lee, Dong-Gun, Hyo-Jin Shin, and Jong-Se Lim. "3D Spatial Distribution Modeling for Petrophysical Property of Gas Hydrate-Bearing Sediment using Well Data in Ulleung Basin." Journal of Energy Engineering 22, no. 2 (June 30, 2013): 156–68. http://dx.doi.org/10.5855/energy.2013.22.2.156.

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43

Oliveira, Daniela, and Rodolfo Oliveira. "Characterization of Energy Availability in RF Energy Harvesting Networks." Mathematical Problems in Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/7849175.

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The multiple nodes forming a Radio Frequency (RF) Energy Harvesting Network (RF-EHN) have the capability of converting received electromagnetic RF signals in energy that can be used to power a network device (the energy harvester). Traditionally the RF signals are provided by high power transmitters (e.g., base stations) operating in the neighborhood of the harvesters. Admitting that the transmitters are spatially distributed according to a spatial Poisson process, we start by characterizing the distribution of the RF power received by an energy harvester node. Considering Gamma shadowing and Rayleigh fading, we show that the received RF power can be approximated by the sum of multiple Gamma distributions with different scale and shape parameters. Using the distribution of the received RF power, we derive the probability of a node having enough energy to transmit a packet after a given amount of charging time. The RF power distribution and the probability of a harvester having enough energy to transmit a packet are validated through simulation. The numerical results obtained with the proposed analysis are close to the ones obtained through simulation, which confirms the accuracy of the proposed analysis.

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44

Guerra, Gerardo, and Juan A. Martinez-Velasco. "A review of tools, models and techniques for long-term assessment of distribution systems using OpenDSS and parallel computing." AIMS Energy 6, no. 5 (2018): 764–800. http://dx.doi.org/10.3934/energy.2018.5.764.

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45

V. Thang, V., and Thanhtung Ha. "Optimal siting and sizing of renewable sources in distribution system planning based on life cycle cost and considering uncertainties." AIMS Energy 7, no. 2 (2019): 211–26. http://dx.doi.org/10.3934/energy.2019.2.211.

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46

Y Nguyen, Minh. "Optimal voltage controls of distribution systems with OLTC and shunt capacitors by modified particle swarm optimization: A case study." AIMS Energy 7, no. 6 (2019): 883–900. http://dx.doi.org/10.3934/energy.2019.6.883.

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47

Mace, B. "Statistical energy analysis, energy distribution models and system modes." Journal of Sound and Vibration 264, no. 2 (July 2003): 391–409. http://dx.doi.org/10.1016/s0022-460x(02)01201-4.

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48

Kim, Hongseok, Joohee Lee, Shahab Bahrami, and Vincent W. S. Wong. "Direct Energy Trading of Microgrids in Distribution Energy Market." IEEE Transactions on Power Systems 35, no. 1 (January 2020): 639–51. http://dx.doi.org/10.1109/tpwrs.2019.2926305.

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49

Keihan Asl, Dariush, Ali Reza Seifi, Mohammad Rastegar, and Mohammad Mohammadi. "Optimal energy flow in integrated energy distribution systems considering unbalanced operation of power distribution systems." International Journal of Electrical Power & Energy Systems 121 (October 2020): 106132. http://dx.doi.org/10.1016/j.ijepes.2020.106132.

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50

Shukla, Prashant, and Sundaresh Sankrith. "Energy and angular distributions of atmospheric muons at the Earth." International Journal of Modern Physics A 33, no. 30 (October 30, 2018): 1850175. http://dx.doi.org/10.1142/s0217751x18501750.

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A fair knowledge of the atmospheric muon distributions at Earth is a prerequisite for the simulations of cosmic ray setups and rare event search detectors. A modified power law is proposed for atmospheric muon energy distribution which gives a good description of the cosmic muon data in low as well as high energy regime. Using this distribution, analytical forms for zenith angle [Formula: see text] distribution are obtained. Assuming a flat Earth, it leads to the [Formula: see text] form where it is shown that the parameter [Formula: see text] is nothing but the power of the energy distribution. Exact analytical function is obtained for inclined trajectory of muon. A new closed form for zenith angle distribution is obtained without assuming a flat Earth and which gives an improved description of the data at all angles even above [Formula: see text]. These distributions are tested with the available atmospheric muon data of energy and angular distributions. The parameters of these distributions can be used to characterize the cosmic muon data as a function of energy, angle and altitude.

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