ORIGINAL_ARTICLE
Optimal Placement of Substations Based on Economic and Technical Risk Management
Design and expansion of distribution systems seems inevitable in view of the need to satisfy the rise in energy consumption in a technical and economical way. Optimal location, sizing and determining the service area of substations is one of the principle problems in expansion of distribution systems. Also uncertainty is one of the important factors that increase risk of exact decision makings. This paper presents a fuzzy multi-objective model for HV/MV substations planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic and technical objectives as well as fuzzy values of investment, operation and loss cost of the substations and primary feeders. This model determines the optimal time, location and size of substations using a multi-objective genetic algorithm (NSGA-II). The proposed model is applied on a typical distribution system to assess the efficiency of the approach.
http://ijsee.iauctb.ac.ir/article_510088_b3b80d5a310c3568653c0a74c59a8a11.pdf
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1
9
Optimal locating
substation
risk management
Non-Dominated sort genetic algorithm
Amir
Navakhah
amir_navakhah@yahoo.com
true
1
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
MahmoudReza
Haghifam
haghifam@modares.ac.ir
true
2
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
AUTHOR
Soudabe
Soleymani
soodabeh_soleymani@yahoo.com
true
3
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
X. Wang, J. R. McDonald, “Modern Power System Planning”, MCGRAW-HILL publication,1994.
1
U.G.W.Knight, “The logical design of electrical networks using linear programming methods”, proc. Inst. Elect. Eng. A, vol.107, pp.306-316, 1960.
2
Oldfield. J.Vand Lang. M.A, “Dynamic programming network flow procedure for distribution system planning”, in proc. Power industry computer applications conf., 1965.
3
R. N. Adams and M. A. Laughton, “Optimal planning of power networks using mixed integer programming”, Proc. IEEE, vol. 121, no. 2, pp.139–147, Feb. 1974.
4
D. M. Crawford and S. B. Holt, “A mathematical optimization technique for locating and sizing distribution substations and deriving their optimal service areas”, IEEE Trans. Power App. Syst. vol.PAS-94, no. 2, pp. 230–235, Apr. 1975.
5
E. Masud, “An iterative procedure for sizing and timing distribution substations using optimization techniques”, in Proc. IEEE PES Winter Meeting, pp.1281–1286, Feb.1974.
6
T. Gönen and B. L. Foote, “Distribution system planning using mixed integer programming”, Proc. Inst. Elect. Eng, vol. 128, no. 2, pp. 70–79, Mar. 1981.
7
M. J. Carson and G. Cornfield, “Design of low voltage distribution networks”, Proc. IEEE, Vol.120, No. 5, pp. 585–
8
International Journal of Smart Electrical Engineering, Vol.2, No.1, Winter 2013 ISSN: 2251-9246
9
593, May 1973.
10
M. J. Carson and G. Cornfield, “Computer aided design of low-voltage distribution networks”, in Proc. IEE Computer Aided Design Conf, Vol.86, pp. 121–124, 1972.
11
T. Belgin, “Distribution system planning using mixed integer programming”, ELEKTRIK, Vol.6, No.1, pp. 37–48, 1998.
12
M. R. Haghifam and M. Shahabi, “Optimal location and sizing of HV/MV substation in uncertainty load environment using genetic algorithm”, Elect.Power Syst. Res, Vol.63, pp.37–50, 2002.
13
M. H. Sepasian, H. Seifi, A. A. Foroud, S. H. Hosseiniand E. M. Kabir, “A new approach for substation expansionplanning”, IEEE Trans. Power Syst, vol. 21, no. 2, pp. 997–1004,May 2006.
14
S. Najafi, S.H. Hosseinian, M. Abedi, A. Vahidnia and S.Abachezadeh, “A framework for optimal planning in largedistribution networks”, IEEE Trans. Power Syst, vol. 24, no. 2,2009.
15
T.H.M. El-Fouly, H.H. Zeineldin, E.F. El-Saadany andM.M.A. Salama, “A new optimization model for distributionsubstation sitting, sizing, and timing”, Electric Power SystemsResearch, vol. 30, pp. 308-315, 2008.
16
A. Mantawy and M. Al-Muhaini, “A new particle swarmbasedalgorithm for distribution system expansion planning includinggeneration”, Proceedings 2nd Iasme/Wseas Int. Conf. on Energy &Environment, Jun 2008.
17
A. Hajizadeh and H. Hajizadeh, “PSO-based planning ofdistri-bution systems with distributed generations,” World Academyof Science, Engineering and Technology 45, 2008.
18
J. F. Gomez, P. M. De Oliveira, L. Ocque J. M. Yusta R.Villasana, H.M. Khodr and A. J. Urdaneta, “Ant colony system algorithm for the planning of primary distribution circuits”, IEEE Trans. Power Syst., Vol.19, pp.996–1004, May. 2004.
19
J.M. Alvarado, E.V. Alvarado, M.A. Arévalo, S.P. Quituisaca, J.F. Gomez and P.M. De Oliveira-De Jesus, “Ant colony systems application for electric distribution network
20
planning”, IEEE 15th International Conference on Intelligent System Applications to Power Systems, 2009.
21
H. K. Temraz, M.M.A. Salama, “A planning model for sitting, sizing and timing of distribution substations and defining the associated service area”, Electric Power Systems Research 62, pp.145-151, 2002.
22
K. Aoki, K. Nara, T. Satoh, M. Kitagawa and K. Yamanaka, “New approximate method for distribution system planning”, IEEE Transactions on power systems, Vol.5, No.1, pp.126-132, 1990.
23
K. Yahav and G. Oron, “Optimal location of electrical substation in regional energy supply systems”, in Proc. IEEE EEIS, Jerusalem, Israel, pp. 307–310, Nov, 1996.
24
D. E. Bouchard, M. M. A. Salama and A. Y.Chilchani, “Optimal feeder routing and optimal substation sizing and placement using guided evolutionary simulated annealing”, in Proc. IEEECCECE, 1995, pp. 684–691.
25
D. Hongwei, Y. Yixin and H. Chunhuaetal, “Optimal planning of distribution substation locations and sizes-model and algorithm”, Elect. Power Energy Syst., Vol.18, No.6, pp.343–357, 1996.
26
M. R. Haghifam, H.Falaghi, O. P. Malik “ Risk-based distributed generation placement”, IET Generation Transmission and Distribution, Vol.2, No.2, pp.252–260, 2008.
27
D. S. Popovic, Z. N.Popovic,”A risk management procedure for supply restoration in distribution networks”, IEEE Transactions on Power Systems, Vol.19, No.1, pp.221–228, 2008.
28
Goldberg, E. David, ”Genetic Algorithms in search, optimization, and machine learning”, Addison-wesley, 1989.
29
ORIGINAL_ARTICLE
Hybrid Fuzzy-PID Application in Boilers to Obtain Optimum Efficiency
Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multi-input multi-output (MIMO) systems, whose states generally vary with operating conditions. The major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. Conventional controller previously used have a set of limitations, e.g. empirical tuning of their parameters when the operating conditions of the controlled process are changed. The application of fuzzy control scheme which is compounded with classic controller (PID) may provide more effective and flexible control of boilers in power stations. This research employing fuzzy logic systems due to their transparency and nonlinear features for controlling dynamics, uncertain and highly nonlinear boiler systems and PID controller due to its fast response
http://ijsee.iauctb.ac.ir/article_510089_fad814a132d48c183b6b57a2200ecd1b.pdf
2013-12-01T11:23:20
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11
16
fuzzy controller
PID controller
Boiler
Shahram
Javadi
sh.javadi@iauctb.ac.ir
true
1
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Abdolreza
Gohari
gohari_engineer@yahoo.com
true
2
Electrical Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Astrom. K., T. Hagglund, “PID Controllers; Theory, Design and Tuning”, Instrument Society of America, Research Triangle Park, 1995.
1
Yager R. R. and Filer D. P., “Essentials of Fuzzy Modelling and Control”, John Wiley, 1994.
2
Tiryaki, H., “Comparing of Fuzzy Logic Controllers with PID Controller in a Thermic Power Plant”, Graduate School of Natural and Applied Sciences, Department of Electrical and Electronic Engineering, M. Sc. Thesis, January 2005 (in Turkish).
3
İlhan Kocaarslan, “A Fuzzy PI Controller Application in Boilers of Thermal Power Plants”, Kirikkale University, Department of Electrical & Electronics Engineering, Kirikkale, Turkey.
4
Li, W., Chang, X., “Application of hybrid fuzzy logic proportional plus conventional integral-derivative controller to combustion control of stoker-fired boilers”.
5
Engin Yesil, “Automatic Generation Control with Fuzzy Logic Controller in the Power System Including Three Areas”, Department of Electrical Eng., Electric & Electronic Faculty Istanbul Technical University, Maslak, Istanbul, Turkey.
6
L. X. Wang, “Adaptive Fuzzy System & Control design & Stability Analysis”, Prentice-Hall, 1994.
7
S. Samyuktha and P. Kanagasabapathy, “Optimized Fuzzy Modelling of a Boiler Super Heater”, Department of Instrumentation Engg, M.I.T, Anna University, Chennai.
8
ORIGINAL_ARTICLE
A New Real-Time Pricing Scheme Considering Smart Building Energy Management System
Real-time pricing schemes make the customers to feel the energy price volatility and improve their load profiles. However, these schemes have no significant effect on demand-side uncertainty reduction. In this paper, considering smart grid infrastructures and smart building Energy Management System (EMS), a new real-time pricing scheme is presented to reduce the uncertainty of demand-side. In the proposed method, EMS announces its electric demand during each period of next day to the retailer. The price of energy for the pre-specified amounts is day-ahead price, but any deviation from this amount is settled through spot market price will be determined several minutes before the corresponding period by retailer. Numerical results of an illustrative example are implemented to demonstrate how this scheme makes motivation in customers to reduce their demand uncertainties
http://ijsee.iauctb.ac.ir/article_510090_3b7e5ed251dd60b08c5053935e7e8a54.pdf
2013-12-01T11:23:20
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17
22
Real-Time Pricing, Energy Management System (EMS), Uncertainty
Retailer, Customer
Mohammad-Hossein
Shariatkhah
m.shariatkhah@modares.ac.ir
true
1
Tarbiat Modares University, Tehran, Iran
Tarbiat Modares University, Tehran, Iran
Tarbiat Modares University, Tehran, Iran
AUTHOR
Mahmoud-Reza
Haghifam
haghifam@modares.ac.ir
true
2
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
AUTHOR
Mohammad-Kazem
Sheikh-El-Eslami
aleslam@modares.ac.ir
true
3
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
AUTHOR
[1] D. Kirschen, G. Strbac , Fundamentals of Power System Economics, Hoboken, NJ: Wiley, 2004.
1
[2] M. Chick, “Le tarif vert retrouve,” The Marginal Cost Concept and the Pricing of Electricity in Britain and France 1945–1970, Energy Journal, vol. 23, no. 1, pp.97–116, 2002.
2
[3] K. KOK, “Dynamic Pricing as Control Mechanism,” in proc. IEEE Power Eng. Soc. Gen. Meet., July 2011, pp.1-8.
3
[4] M. Nikzad, B. Mozafari, A.M. Ranjbar, “Reliability Enhancement and Price Reduction of Restructured Power System with Probabilistic Day-Ahead Real Time Pricing Contract,” Int. Conf. Power Sys. Tech. (POWERCON), Oct. 2010.
4
[5] A. J. Conejo, J. M. Morales,L. Baringo, “Real-Time Demand Response Model,” IEEE Tran. Smart Grid, vol. 1, no. 3, Dec 2010, pp.236 – 242.
5
[6] E. Çelebi, J.D. Fuller, “A Model for Efficient Consumer Pricing Schemes in Electricity Markets” IEEE Trans. Power Sys., vol. 22, no. 1, Feb. 2007, pp.60-67.
6
[7] P. Joskow and J. Tirole, Retail Electricity Competition, 2004, NBER Working Paper No. 10473.
7
[8] Pengwei Du, Member, IEEE, and Ning Lu, “Appliance Commitment for Household Load Scheduling,” IEEE Trans. Smart Grid, vol. 2, no. 2, June 2011, pp.411 - 419.
8
[9] A.-H. Mohsenian-Rad, A. Leon-Garcia, “Optimal Residential Load Control with Price Prediction in Real-Time Electricity Pricing Environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, Sep. 2010, pp.120 –133.
9
ORIGINAL_ARTICLE
Congestion Management in Power Systems Via Intelligent Method
In the deregulated power systems, transmission congestion is one of the significant and main problems of the electrical networks which can cause incremental cost in the energy. This problem has resulted to new challenging issues in different parts of power systems which there was not in the traditional systems or at least had very little importance. Transmission congestion happens when the maximum available power transmission capacity is lower than the consumption side. As congestion happens, the system power losses are increased which can cause problem in the voltage constraints. Therefore, this paper proposes a new method to handle the optimal management and control of congestion problem by the use of distributed generations (DGs). In this regard, the optimal size and location of DGs are investigated using the powerful bacteria foraging algorithm (BFA) as a new intelligence-based optimization technique to solve the congestion problem on the three IEEE 14-bus, 30-bus and 57-bus test systems. The simulation results show the high speed, fast convergence and accurate performance of the proposed algorithm to solve the congestion problem in the system
http://ijsee.iauctb.ac.ir/article_510091_03755cbfb40a233cc3f7f7046dded601.pdf
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23
31
Bactria foraging algorithm
Power losses
congestion management
Distributed generations (DGs
S.A
Hashemi Zadeh
sa_hashemizadeh@yahoo.com
true
1
Department of electrical engineering, Islamic Azad University, Rafsanjan, Iran,
Department of electrical engineering, Islamic Azad University, Rafsanjan, Iran,
Department of electrical engineering, Islamic Azad University, Rafsanjan, Iran,
AUTHOR
A.A
Gharaveisi
a_gharaveisi@yahoo.com
true
2
Assistant, of Control Engineering, Department of electrical engineering, Shahid bahonar University, Kerman, Iran,
Assistant, of Control Engineering, Department of electrical engineering, Shahid bahonar University, Kerman, Iran,
Assistant, of Control Engineering, Department of electrical engineering, Shahid bahonar University, Kerman, Iran,
AUTHOR
GH
Shahgholian
shahgholian@iaun.ac.ir
true
3
Associate, of Electrical Engineering, Department of electrical engineering, Islamic Azad University, Najaf abad, Iran,
Associate, of Electrical Engineering, Department of electrical engineering, Islamic Azad University, Najaf abad, Iran,
Associate, of Electrical Engineering, Department of electrical engineering, Islamic Azad University, Najaf abad, Iran,
AUTHOR
Sujatha Balaraman, “Congestion management deregulated power system using real coded genetic algorithm”, International Journal of Engineering Science and Technology, Vol.2, No.11, pp.6681-6690, 2010.
1
A. F. Kaptue Kamga, S. Voller, J. F. Verstege, “Congestion Management in Transmission Systems with Large Scale Integration of Wind Energy”, Integration of Wide-Scale Renewable Resources In to the Power Delivery System, CIGRE/IEEE PES Joint Symposium, 2009.
2
A. Vergnol, J. Sprooten, B. Robyns, V. Rious, J. Deuse, “Optimal network congestion management using wind farms”, Integration of Wide-Scale Renewable Resources Into the Power Delivery System, CIGRE/IEEE PES Joint Symposium, 2009.
3
J. Hazra, and A. K. Sinha, “Congestion management using ultiobjective particle swarm optimization”, IEEE Trans. Power Syst., Vol.22, No.4, pp. 1726-1734, Feb. 2007.
4
T. Jibiki, E. Sakakibara, and S. Iwamoto, “Line Flow Sensitivities of Line Reactances for Congestion Management”, IEEE Power Eng. society general Meeting, Vol.24, pp.1-6, Jun. 2007.
5
M. Gitizadeh, and M. Kalantar, ”A New Approach for Congestion Management via Optimal Location of FACTS Devices in Deregulated Power System”, Electric Utility Third International Conference on Deregulation and Restructuring and Power Technologies, DRPT 2008.
6
P. Kaymaz, J. Valenzuela, and C. S. Park, “Transmission Congestion and Competition on Power Generation Expansion”, IEEE Trans. Power Syst., Vol.22, No.1, pp.156-163, Feb. 2007.
7
J. Hazra, and A. K. Sinha, “Congestion management using ultiobjective particle swarm optimization”, IEEE Trans. Power Syst., Vol.22, No.4, pp.1726-1734, Feb. 2007.
8
S. Pichaisawat, Y. H. Song, and G. A. Taylor, “Congestion management considering voltage security constraint”, in Proc. Int. Conf. on Power Syst. Tech., Vol.3, pp.1819-1823, 2002.
9
Khanabadi, M. Doostizadeh, M. Esmaeilian, A. Mohseninezhad, “Transmission Congestion Management through Optimal Distributed Generation's Sizing”, International Conference on Environment and Electrical Engineering (EEEIC), 2011.
10
Ray D.Zimmerman and Carlos E.Murillo-Sanchez. Matpower, “A MATLAB power system, simulation package”, Sep.21, 2007
11
ORIGINAL_ARTICLE
Robust Model for Networked Control System with Packet Loss
The Networked Control System in modern control widely uses to decrease the implementation cost and increasing the performance. NCS in addition to its advantages is inevitable. Nevertheless they suffer of some limitations and deficiencies. Packet loss is one of the main limitations which affect the control system in different conditions and finally may lead to system instability. For this reason, it is important to model the system properly. In this paper, a new model has been proposed that is very simple and independent from the main system. This model based on Robust Theory structure. Robust theory is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. Robust control methods are designed to function properly so long as uncertain parameters or disturbances are within some (typically compact) set.
http://ijsee.iauctb.ac.ir/article_510092_6497bf798e948fadc3e82d8a4109e369.pdf
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33
37
network control system (NCS)
Packet Loss
Robust Theory
Mohsen
Jahanshahi
mjahanshahi@iauctb.ac.ir
true
1
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Sayyid Mohsen
Houshyar
houshyar.m@srbiau.ac.ir
true
2
Department of Electrical Engineering Science & Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering Science & Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering Science & Research Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Amir Reza
Zare Bidaki3
azare@buiniau.ac.ir
true
3
Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran.
Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran.
Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran.
AUTHOR
Luca Schenato, “To Zero or to Hold Control Inputs With Lossy Links”, IEEE Trans. On Automatic Control, Vol.54, No.5, May 2009.
1
Huiying Chen, Wanliang Wang, Zuxin Li, “Synthetic Modeling and Control of Networked Control Systems with
2
Multi-packet Transmission”, International Conference on Advanced Computer Control, Aug. 2012.
3
Xiaosheng Fang, Jingcheng Wang, “Stochastic Observer-based Guaranteed Cost Control for Networked Control Systems with Packet Dropouts”, Proceedings of the 17th World Congress The International Federation of Automatic Control Seoul, Korea, July 6-11, 2013.
4
Ling Q., Lemmon M.D., “Soft real-time scheduling of networked control systems with dropouts governed by a Markov chain”, Proc. American Control Conf., pp.4845 –4850, 2003.
5
Seiler P., Sengupta R.,”An H approach to networked control”, IEEE Trans. Autom. Control, Vol.50, No.3, pp.356 –364, 2005.
6
Wu J., Chen T., “Design of networked control systems with packet dropouts”, IEEE Trans. Autom. Control, Vol.52, No7, pp. 1314–1319, 2007.
7
Wang Z., Ho D.W.C., Liu X., “Variance-constrained control for uncertain stochastic systems with missing measurements”, IEEE Trans. Syst. Man. And Cybern, Vol.5, No.35, pp.746–753, 2005.
8
Wang Z., Yang F., Ho D.W.C., Liu X., “Robust H1control for networked systems with random packet losses”, IEEE Trans. Syst. Man Cybern., Vol.37, No.4, pp.916–924, 2007.
9
Yang F., Wang Z.D., Ho W.C., Gani M., “Robust H control with missing measurements and time-delays”, IEEE Trans. Autom. Control, Vol.52, No.9, pp.1666–1672, 2007.
10
Y. Wang, G. Yang, “H∞ Controller Design for Networked Control Systems via Active-varying Sampling Period Method”, Automatica sinica, Jul. 2008.
11
Radek Matusu, Roman Prokop, and Libor Pekar, “Parametric and unstructured approach to uncertainty modelling and robust stability analysis”, International Journal of Mathematical Models and Methods in Applied Sciences, Feb. 2013.
12
S. Lun, S. Wang, “Robust H∞ cost control for Networked
13
control systems based on T-S model”, IEEE Transactions on systems, Apr. 2010.
14
ORIGINAL_ARTICLE
A Novel Method for VANET Improvement using Cloud Computing
In this paper, we present a novel algorithm for VANET using cloud computing. We accomplish processing, routing and traffic control in a centralized and parallel way by adding one or more server to the network. Each car or node is considered a Client, in such a manner that routing, traffic control, getting information from client and data processing and storing are performed by one or more server in different bases. The procedure is about each client that receives its situation by GPS system and sends it online to the concerned server via GPRS networks. In order to perform routing and displaying data, each client sends a request to server. All processes and operations will be executable by the software which is inside the server. Finally, we propose an algorithm to work in server and make decision on route selection on the basis of three priorities of traffic, safety and shortest route. This algorithm is represented for the first time. The results indicate that by using this method VANET network is improved. By using this algorithm, we can communicate generally, not regionally, and improvement of processing and accurate statistics is achieved. In fact, the processing in the server is Fuzzy. It means that these priorities are Fuzzy.
http://ijsee.iauctb.ac.ir/article_510093_4f94b9ba71d83718319fca04862961bf.pdf
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39
44
Cloud computing
Monitoring
LISP
Algorithm
Vanet
MANET
GPS
Saied
Raeeszadeh
saied.raeeszadeh@gmail.com
true
1
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
Reza
Sabbaghi-Nadooshan
r_sabbaghi@iauctb.ac.ir
true
2
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
AUTHOR
R. Moskowitz and P. Nikander, “Host Identity Protocol (HIP) Architecture”, RFC 4423, IETF Network Working Group, May 2006.
1
M. O’Dell, “GSE – An Alternate Addressing Architecture for IPv6”, Internet Draft draft-ietf-ipngwg-gseaddr-00.txt, IETF NetworkWorking Group, February 1997.
2
J. Saltzer, “On the Naming and Binding of Network Destinations, RFC”, 1498, IETF Network Working Group, August 1993.
3
R. Hiden, “New Scheme for Internet Routing and Addressing, (ENCAPS) for IPNG”, RFC 1955, IETF Network Working Group, June 1996.
4
N. Chiappa, “Endpoints and Endpoint Names: A ProposedEnhancement to the Internet Architecture”, 1999. Available from:http://ana.lcs.mit.edu/jnc/tech/endpoints.txt
5
C. Bettstetter, “Smooth is better than sharp: A random mobility model for simulation of wireless networks”, MSWiM, pp. 19–27, 2000.
6
C. Bettstetter, “Mobility modeling in wireless networks: Categorization, smooth movement, and border effects”, ACM SIGMOBILE’01, pp. 55–67, 2001.
7
J. Broch, D. Maltz, D. Johnson, Y. Hu, J. Jetcheva, “performance comparison of multi-hop wireless ad hoc network routing protocols”, MOBICOM’98, pp. 85–97, 1998.
8
T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research”, In MOBICOM’02, pp. 483–502, 2002.
9
R. Morris, J. Jannotti, F. Kaashoek, J. Li, and D. De Couto, Carnet: a scalable ad hoc wireless network system, ACM SIGOPS European Workshop, 2000, pp. 61–65.
10
G. Pei, M. Gerla, X. Hong, and C. Chiang, “A wireless hierarchical routing protocol with group mobility”, IEEE WCNC’99, pp. 1536–1540, 1999.
11
H. Sawant, J. Tan, and Q. Yang, “Study of an inter-vehicle communication protocol for vehicle-infrastructure integration (VII)”, In Transportation Research Board 84th Annual Meeting, Washington, DC, 2005.
12
J. Yoon, M. Liu, and B. Noble, “Random waypoint considered harmful”, INFOCOM’03, 2003, pp. 1312–1321.
13
M. Abuelela and S. Olariu, “Content delivery in zero-infrastructure VANET”, Vehicular Networks: From Theory to Practice, Taylor & Francis, pp. 8.1-8.15, 2009.
14
A.Aijaz, B. Bochow, F. Dotzer, A. Festag, M. Gerlach, R. Kroh, and T. Leinmuller, “Attacks on inter-vehicle communication systems – an analysis”, Proc. International Workshop on Intelligent Transportation (WIT’2006), Hamburg, Germany, pp. 189-194, 2006.
15
J.Anda, J. LeBrun, D. Ghosal, C.N. Chuah, and M. Zhang, “VGrid: vehicular adhoc networking and computing grid for intelligent traffic control”, Proc. IEEE Vehicular Technology Conference, pp. 2905-2909, May 2005.
16
K. Czajkowski, S. Fitzgerald, I. Foster, and C. Kesselman, “Grid information services for distributed resource sharing”, Proc. 10th IEEE International Symposium on High Performance Distributed Computing, New York, NY, USA, pp. 181-184, 2001.
17
M. Eltoweissy, S. Olariu, and M. Younis, “Towards autonomous vehicular clouds”, Proc. Ad Hoc Nets, Victoria, BC, Canada, pp. 1 -16, August 2010.
18
J. Eriksson, H. Balakrishnan, and S. Madden, “Cabernet: vehicular content delivery using WiFi”, Proc. 14-th ACM International Conference on Mobile Computing and Networking (MobiCom’2008), San Francisco, CA, USA, pp.199-210, September 2008.
19
M. Fontaine, “Traffic monitoring, Vehicular Networks: From Theory to Practice”, Taylor & Francis, Boca Raton, FL, pp. 1.1-1.28, 2009.
20
S. Hodgson, “What is cloud computing?”, May 2008, available at: http://winextra.com/2008/05/02/what-is-cloud-computing
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J.N. Hoover and R. Martin, “Demystifying the cloud”, InformationWeek Research &Reports, pp. 30-37, June 2008.
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C. Lochert, B. Scheuermann, M. Caliskan, and M. Mauve, “The feasibility of information dissemination in vehicular ad-hoc networks”, Proc. 4th Annual Conference on Wireless On-demand Network Systems and Services (WONS’07), Oberguyrgl, Austria, pp. 92-99, January 2007.
24
C. Lochert, B. Scheuermann, C. Wewetzer, A. Luebke, and M. Mauve, “Data aggregation and roadside unit placement for a VANET traffic information system”, Proc. 5-th ACM International Workshop on Vehicular Ad Hoc Networks (VANET’2008), pp.58-65, September 2008.
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J. Ott and D. Kutscher, “A disconnection-tolerant transport for drive-thru internet environments”, Proc. IEEE INFOCOM, pp. 1849-1862, 2005.
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29
G.Yan, S. Olariu, and M.C. Weigle, “Providing location
30
security in vehicular ad-hoc networks”, IEEE Wireless Communications, Vol. 16, No. 6, pp. 48-55, 2009.
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42
ORIGINAL_ARTICLE
Flexibility Enhancement of Energy Delivery Systems through Smart Operation of Micro Energy Hub
In this paper smart operation of micro energy hub is presented. Energy hub system consists of certain energy hubs and
interconnectors that are coordinated by energy hub system operator. An energy hub contains several converters and storages
to serve demanded services in most efficient manner from available energy carriers. In this paper, the flexibility of energy
delivery point is enhanced using micro energy hub concept. Smart micro energy hub is operated in minimum cost
considering the price of input energy carriers, the amount of forecasted output energy carriers and the available facilities
included in the hub. Regarding this matter, two micro energy hubs with different characteristics are modelled which are
equipped with CHP unit, warmer, boiler and heat storage. The effectiveness of the proposed system is validated by running
numerical study on a test system.
http://ijsee.iauctb.ac.ir/article_510094_38a0834b993049cda0fc986b74abc9ca.pdf
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46
52
Energy delivery
flexibility enhancement
micro energy hub
multi-energy system
M
Yazdani Damavandi
true
1
AUTHOR
P
Teimourzadeh Baboli,
true
2
AUTHOR
M
ParsaMoghaddam
parsa@modares.ac.ir
true
3
Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
AUTHOR
P. Mancarella, “Smart Multi-Energy Grids: Concepts, benefits and challenges”, IEEE Power and Energy Society General
1
Meeting, pp.1-2, 2012.
2
H. Hashim and W. S. Ho, “Renewable energy policies and initiatives for a sustainable energy future in Malaysia”, Renewable and Sustainable Energy Reviews, 2011.
3
P. Favre-Perrod, “A vision of future energy networks”, IEEE Power Engineering Society Inaugural Conference and Exposition in Africa, pp.13-17, 2005.
4
P. Favre-Perrod, “Hybrid energy transmission for multi-energy networks”, Diss. Eidgenössische Technische Hochschule ETH Zürich, Nr. 17905, 2008.
5
A. Hajimiragha, C. Canizares, M. Fowler, M. Geidl, and G. Andersson, “Optimal energy flow of integrated energy systems with hydrogen economy considerations”, in Bulk Power System Dynamics and Control-VII. Revitalizing Operational Reliability, iREP Symposium, pp.1-11, 2007.
6
M. Houwing, R. R. Negenborn, and B. De Schutter, “Demand response with micro-CHP systems”, Proceedings of the IEEE, Vol.99, pp.200-213, 2011.
7
M. Geidl and G. Andersson, “Optimal coupling of energy infrastructures”, IEEE Lausanne in Power Tech, pp.1398-1403, 2007.
8
M. Geidl and G. Andersson, “Operational and topological optimization of multi-carrier energy systems”, International Conference on in Future Power Systems, pp.6 pp.-6, 2005.
9
M. Geidl, Integrated modeling and optimization of multi-carrier energy systems, ETH Diss. 17141, 2007.
10
M. Arnold and G. Andersson, “Decomposed electricity and natural gas optimal power flow”, in 16th Power Systems Computation Conference (PSCC 08), Glasgow, Scotland, 2008.
11
T. Krause, F. Kienzle, Y. Liu, and G. Andersson, “Modeling interconnected national energy systems using an energy hub approach”, IEEE Trondheim PowerTech, pp.1-7, 2011.
12
M. C. Bozchalui, S. A. Hashmi, H. Hassen, C. A. Canizares, and K. Bhattacharya, “Optimal Operation of Residential Energy Hubs in Smart Grids”, IEEE Transactions on Smart Grid, Vol.3, pp.1755-1766, 2012.
13
ORIGINAL_ARTICLE
New Ant Colony Algorithm Method based on Mutation for FPGA Placement Problem
Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optimization uses a similar mechanism to solve the optimization problem. Usually the main difficulties of evolutionary algorithm for solving the optimization problem are: early convergence, loss of population diversity, and placing in a local minimum .Therefore, it needs the way that preserves the variation and tries to avoid trapping in local minimum. In this paper by combining ant colony algorithm and mutation hybrid algorithms that leads to the better solution for optimization of FPGA (Field Programmable Gate Array) placement problem is made. They are different types of swarm intelligence algorithm. After designing the algorithm, its parameters tuning have been done by solving several problems, and then the proposed methods have been compared with the other approaches. The results show that in most problems, the proposed hybrid method is able to obtain better solutions and makes fewer errors.
http://ijsee.iauctb.ac.ir/article_510095_203f0249149d72d165de58c5c010c6a1.pdf
2013-12-01T11:23:20
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53
60
Ant colony algorithm
Mutation
FPGA placement
Optimization
Setareh
Shafaghi
strshafaghi@yahoo.com
true
1
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
AUTHOR
Fardad
Farokhi
true
2
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
AUTHOR
Reza
Sabbaghi-Nadooshan
r_sabbaghi@iauctb.ac.ir
true
3
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
AUTHOR
B. Premalatha and S. Umamaheswari “survey of online hardware task scheduling and placement algorithm for partially reconfigurable computing systems” International Journal of computing and Corporate Research, Vol.2, Issie.3, 2012.
1
E. M .Vasconcelos de Lima, A. C. Cavalcanti and L. dos Anjos Formiga Cabral, “A New Approach to VPR Tool’s FPGA Placement”, Proceedings of the World Congress on Engineering and Computer Science (WCECS), 2007.
2
M. Yang, A.E.A. Almaini, L. Wang and P.J .Wang, “FPGA Placement Using Genetic Algorithm with Simulated Annealing”, 6th international conference on ASIC, Vol.2, pp.808-810, 2005.
3
V. Chopra, and A. Singh, “Solving FPGA Routing using Ant Colony Optimization with Minimum CPU Time” International Journal of Computer Science & Technology (IJCST), Vol.2,
4
Issue.4, pp.223-226, 2011.
5
X. Shi, “FPGA Placement Methodologies: A Survey” Dept. of Computing Science, University of Alberta, 2009.
6
X. Wenyao, K. Xu and X. Xinmin, “A Novel Placement Algorithm for Symmetrical FPGA” Institute of Electronic Circuit and Information System, Zhejiang University”, 7th international conference, IEEE press, pp. 1281- 1284, 2007.
7
S. J .Lee and K. Raahemifar, “FPGA Placement Optimization Methodology Survey”, Canadian Conference on Electrical and Computer Engineering (CCECE), pp.001981- 001986, 2008.
8
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10
K. Wang, N Xu, “Ant Colony Optimization for Symmetrical FPGA Placement”, 11th IEEE international conference on Computer Aided Design and Computer Graphics, pp.561-563, 2009.
11
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16
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17
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21
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22