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JCR Impact Factor: 0.459
JCR 5-Year IF: 0.442
Issues per year: 4
Current issue: Feb 2017
Next issue: May 2017
Avg review time: 76 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

Broken Bar Fault Detection in IM Operating Under No-Load Condition, RELJIC, D., JERKAN, D., MARCETIC, D., OROS, D.
Issue 4/2016

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2017-Apr-04
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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "Big Data - " before the paper title in OpenConf.

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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

2016-Jun-14
Thomson Reuters published the Journal Citations Report for 2015. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.459, and the JCR 5-Year Impact Factor is 0.442.

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  4/2014 - 4

Incentive Driven Distributed Generation Planning with Renewable Energy Resources

KAUR, S. See more information about KAUR, S. on SCOPUS See more information about KAUR, S. on IEEExplore See more information about KAUR, S. on Web of Science, KUMBHAR, G. B. See more information about KUMBHAR, G. B. on SCOPUS See more information about KUMBHAR, G. B. on SCOPUS See more information about KUMBHAR, G. B. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (837 KB) | Citation | Downloads: 391 | Views: 1,413

Author keywords
distributed power generation, heuristic algorithms, optimization, power generation planning, sustainable development

References keywords
power(10), energy(10), planning(9), optimization(9), generation(9), distributed(9), distribution(8), systems(6), system(6), search(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 21 - 28
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04004
Web of Science Accession Number: 000348772500004
SCOPUS ID: 84921651204

Abstract
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Renewable DGs may not be economically viable due to the stochastic generation and huge capital investment, but are an inevitable choice for sustainable energy development and future planning. An appropriate incentive scheme for clean Distributed Generation (DG) technologies is able to address this issue in an economical manner and is considered in proposed distributed generation planning model. The proposed model minimizes the annualized cost with Emission Offset Incentive (EOI) and the penalty for Green-house Gas (GHG) emissions. A meta-heuristic approach with dynamic tuning of control parameters is adopted to improve the success and the convergence rate of optimal solutions. The algorithm provides the optimal solution in terms of type, size, and location of DG. The proposed technique is implemented on IEEE 33-bus system. Proposed model helps the Distribution Network Operators (DNOs) to decide the proper DG technology from an economic prospective for eco-friendly energy planning.


References | Cited By  «-- Click to see who has cited this paper

[1] A. Keane, L. F. Ochoa, C. Borges, G. Ault, A. Alarcon, R. Currie, F. Pilo, C. Dent, G. P. Harrison, "State of the art techniques and challenges ahead for DG planning and optimization," IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1493-1502, May 2013.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 89]


[2] P. S. Georgilakis, N. D. Hatziargyriou, "Optimal distributed generation placement in power distribution Network: Models, methods and future", IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3420-3428, 2013.
[CrossRef] [Web of Science Times Cited 142] [SCOPUS Times Cited 183]


[3] W. El-Khattam, K. Bhattacharya, Y. Hegazy, M. M. A. Salama, "Optimal investment planning for distributed generation in a competitive electricity market," IEEE Transactions on Power Systems, vol. 19, no. 3, pp. 1674-1684, 2004.
[CrossRef] [Web of Science Times Cited 197] [SCOPUS Times Cited 311]


[4] A. Zangeneh, S. Jadid, A. R. Kian, "Promotion strategy of clean technologies in distributed generation expansion planning," Renewable Energy, vol. 34, no. 12, pp. 2765-2773, 2009.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 51]


[5] W. S. Tan, M.Y. Hassan, H. A. Rahman, M. P. Abdullah, F. Hussin, "Multi-distributed generation planning using hybrid partical swarm optimization - gravitational search algorithm inclusing voltage rise issue," Generation, Transmission & Distribution, IET, vol. 7, no. 9, pp. 929-942, 2013.
[CrossRef] [Web of Science Times Cited 23]


[6] T. Niknam, S. I. Taheri, J. Agahei, S. Tabatabaei, M. Nayeripour, "A modified mating optimization algorithm for multiobjective placement of renewable energy resources," Applied Energy, vol. 88, no. 12, pp. 4817-4830, 2011.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 86]


[7] M. A. Abdullah, A. P. Agalgaonkar, K. M. Muttaqi, "Quantification of emission reduction from electicity network with the integration of renewable resources," in Proc. IEEE Power and Energy Society General Meeting, 2011 , pp. 1-7.
[CrossRef] [SCOPUS Times Cited 6]


[8] A. Soroudi, M. Eshan, H. Zareipour, "A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy sources," Renewable Energy, vol. 36, no. 1, pp. 179-188, 2011.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 53]


[9] R. Ebrahimi, M. Eshan, H. Nouri, "A profit-centric strategy for distributed generation planning considering time varying voltage dependent load demand," International Journal of Electrical Power & Energy Systems, vol. 44, no. 1, pp. 168-178, 2013.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]


[10] G. P. Harrison, A. Piccolo, P. Siano, A. R. Wallace, "Hybrid GA and OPF evaluation of network capacity for distributed generation connections," Electric Power System Research., vol. 78, no. 3, pp. 392-398, 2008.
[CrossRef] [Web of Science Times Cited 97] [SCOPUS Times Cited 127]


[11] Y.M. Atwa, E. F. E. Saadany, M. M. A. Salama, R. Seethapathi, "Optimal renewable resources mix for distribution system energy loss minimization," IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 360-370, 2010.
[CrossRef] [Web of Science Times Cited 303] [SCOPUS Times Cited 417]


[12] P. Siano, L.F. Ochoa, G.P. Harrison, A. Piccolo, "Assessing the strategic benefits of distributed generation ownership for DNOs," Generation, Transmission & Distribution, IET, vol. 3, no. 3, pp. 225-236, March 2009.
[CrossRef] [Web of Science Times Cited 57] [SCOPUS Times Cited 68]


[13] Z. W. Geem, J. H. Kim, G. V. Loganathan, "A New Heuristic Optimization Algorithm: Harmony Search," SIMULATION, vol. 76, no. 2, pp. 60-68, 2001.
[CrossRef] [SCOPUS Times Cited 2119]


[14] K. S. Lee, Z. W. Geem, S. Lee, K. Bae, "The harmony search heuristic algorithm for discrete structural optimization," Engineering Optimization, vol. 37, no. 7, pp. 663-684, 2005.
[CrossRef] [Web of Science Times Cited 165] [SCOPUS Times Cited 199]


[15] S. Das, A. Mukhopadhyay, A. Roy, A. Abraham, B. K. Panigrahi, "Exploratory Power of the Harmony Search Algorithm: Analysis and Improvements for Global Numerical Optimization," IEEE Transactions on System Man and Cybernetics, Part B: Cybernetics, vol. 41, no. 1, pp. 89-106, 2011.
[CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 133]


[16] M. Mahdavi, M. Fesanghary, E. Damangir, "An improved harmony search algorithm for solving optimization problems," Applied Mathematics and Computation, vol. 188, no. 2, pp. 1567-1579, 2007.
[CrossRef] [Web of Science Times Cited 503] [SCOPUS Times Cited 869]


[17] K. Nekooei, M. M. Farsangi, H. Nezamabadi-Pour, K. Y. Lee, "An Improved Multi-Objective Harmony Search for Optimal Placement of DGs in Distribution Systems," IEEE Transactions on Smart Grid, vol. 4, no. 1, pp. 557-567, 2013.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 64]


[18] K. Zou, A. P. Agalgaonkar, K. M. Muttaqi, S. Perera, "Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties," IEEE Transactions on Sustainable Energy, vol. 3, no. 1, pp. 112-123, 2012.
[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 130]


[19] N. Jain, S. N. Singh, S. C. Srivastava, "A Generalized Approach for DG Planning and Viability Analysis Under Market Scenario," IEEE Transactions on Industrial Electronics, vol. 60, pp. 5075-5085, 2013.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 34]


[20] D. Olivera, P. Feltrin, "Investigation of the relationship between load and load factors for a Brazillian electric utility," in Proc. Electric utility Transmission and distribution conference and exposition, Latin America, 2006, pp. 1-6
[CrossRef] [SCOPUS Times Cited 2]


[21] N. Jain, S.N. Singh, S. C. Srivastava, "Planning and impact evaluation of distributed generators in Indian context using Multi-Objective Particle Swarm Optimization," in Proc. IEEE Power and Energy Society General Meeting, 2011, pp. 1-8.
[CrossRef] [SCOPUS Times Cited 14]


[22] L. R. A. Deepa, N. Praveen, "Impact of Climate change and adaptation to green technology in India," in Proc. Recent Advances in Space Technology Services and Climate Change (RSTSCC), 2010, pp. 460-465.
[CrossRef] [SCOPUS Record]


[23] A. M. Jain, B. E. Kushare, "Techno-economics of solar wind hybrid system in Indian context: A case study," in Proc IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007), 2007, pp. 39-44.



References Weight

Web of Science® Citations for all references: 1,992 TCR
SCOPUS® Citations for all references: 4,963 TCR

Web of Science® Average Citations per reference: 83 ACR
SCOPUS® Average Citations per reference: 207 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-04-26 18:37 in 145 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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