2013
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Optimal Placement of Substations Based on Economic and Technical Risk Management
2
2
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 multiobjective 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 multiobjective genetic algorithm (NSGAII). The proposed model is applied on a typical distribution system to assess the efficiency of the approach.
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1
9


Amir
Navakhah
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science
Iran
amir_navakhah@yahoo.com


MahmoudReza
Haghifam
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Electrical and Computer Engineering,
Iran
haghifam@modares.ac.ir


Soudabe
Soleymani
Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering, Science
Iran
soodabeh_soleymani@yahoo.com
Optimal locating
substation
risk management
NonDominated sort genetic algorithm
[X. Wang, J. R. McDonald, “Modern Power System Planning”, MCGRAWHILL publication,1994. ##U.G.W.Knight, “The logical design of electrical networks using linear programming methods”, proc. Inst. Elect. Eng. A, vol.107, pp.306316, 1960. ##Oldfield. J.Vand Lang. M.A, “Dynamic programming network flow procedure for distribution system planning”, in proc. Power industry computer applications conf., 1965. ##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. ##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.PAS94, no. 2, pp. 230–235, Apr. 1975. ##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. ##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. ##M. J. Carson and G. Cornfield, “Design of low voltage distribution networks”, Proc. IEEE, Vol.120, No. 5, pp. 585– ##International Journal of Smart Electrical Engineering, Vol.2, No.1, Winter 2013 ISSN: 22519246 ##593, May 1973. ##M. J. Carson and G. Cornfield, “Computer aided design of lowvoltage distribution networks”, in Proc. IEE Computer Aided Design Conf, Vol.86, pp. 121–124, 1972. ##T. Belgin, “Distribution system planning using mixed integer programming”, ELEKTRIK, Vol.6, No.1, pp. 37–48, 1998. ##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. ##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. ##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. ##T.H.M. ElFouly, H.H. Zeineldin, E.F. ElSaadany andM.M.A. Salama, “A new optimization model for distributionsubstation sitting, sizing, and timing”, Electric Power SystemsResearch, vol. 30, pp. 308315, 2008. ##A. Mantawy and M. AlMuhaini, “A new particle swarmbasedalgorithm for distribution system expansion planning includinggeneration”, Proceedings 2nd Iasme/Wseas Int. Conf. on Energy &Environment, Jun 2008. ##A. Hajizadeh and H. Hajizadeh, “PSObased planning ofdistribution systems with distributed generations,” World Academyof Science, Engineering and Technology 45, 2008. ##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. ##J.M. Alvarado, E.V. Alvarado, M.A. Arévalo, S.P. Quituisaca, J.F. Gomez and P.M. De OliveiraDe Jesus, “Ant colony systems application for electric distribution network ##planning”, IEEE 15th International Conference on Intelligent System Applications to Power Systems, 2009. ##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.145151, 2002. ##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.126132, 1990. ##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. ##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. ##D. Hongwei, Y. Yixin and H. Chunhuaetal, “Optimal planning of distribution substation locations and sizesmodel and algorithm”, Elect. Power Energy Syst., Vol.18, No.6, pp.343–357, 1996. ##M. R. Haghifam, H.Falaghi, O. P. Malik “ Riskbased distributed generation placement”, IET Generation Transmission and Distribution, Vol.2, No.2, pp.252–260, 2008. ##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. ##Goldberg, E. David, ”Genetic Algorithms in search, optimization, and machine learning”, Addisonwesley, 1989.##]
Hybrid FuzzyPID Application in Boilers to Obtain Optimum Efficiency
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2
Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multiinput multioutput (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
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11
16


Shahram
Javadi
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
sh.javadi@iauctb.ac.ir


Abdolreza
Gohari
Electrical Engineering Department, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, South
Iran
gohari_engineer@yahoo.com
fuzzy controller
PID controller
Boiler
[Astrom. K., T. Hagglund, “PID Controllers; Theory, Design and Tuning”, Instrument Society of America, Research Triangle Park, 1995. ##Yager R. R. and Filer D. P., “Essentials of Fuzzy Modelling and Control”, John Wiley, 1994. ##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). ##İlhan Kocaarslan, “A Fuzzy PI Controller Application in Boilers of Thermal Power Plants”, Kirikkale University, Department of Electrical & Electronics Engineering, Kirikkale, Turkey. ##Li, W., Chang, X., “Application of hybrid fuzzy logic proportional plus conventional integralderivative controller to combustion control of stokerfired boilers”. ##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. ##L. X. Wang, “Adaptive Fuzzy System & Control design & Stability Analysis”, PrenticeHall, 1994. ##S. Samyuktha and P. Kanagasabapathy, “Optimized Fuzzy Modelling of a Boiler Super Heater”, Department of Instrumentation Engg, M.I.T, Anna University, Chennai.##]
A New RealTime Pricing Scheme Considering Smart Building Energy Management System
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2
Realtime pricing schemes make the customers to feel the energy price volatility and improve their load profiles. However, these schemes have no significant effect on demandside uncertainty reduction. In this paper, considering smart grid infrastructures and smart building Energy Management System (EMS), a new realtime pricing scheme is presented to reduce the uncertainty of demandside. In the proposed method, EMS announces its electric demand during each period of next day to the retailer. The price of energy for the prespecified amounts is dayahead 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
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17
22


MohammadHossein
Shariatkhah
Tarbiat Modares University, Tehran, Iran
Tarbiat Modares University, Tehran, Iran
Iran
m.shariatkhah@modares.ac.ir


MahmoudReza
Haghifam
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
Iran
haghifam@modares.ac.ir


MohammadKazem
SheikhElEslami
Tarbiat Modares University, Tehran, Iran.
Tarbiat Modares University, Tehran, Iran.
Iran
aleslam@modares.ac.ir
RealTime Pricing, Energy Management System (EMS), Uncertainty
Retailer, Customer
[[1] D. Kirschen, G. Strbac , Fundamentals of Power System Economics, Hoboken, NJ: Wiley, 2004. ##[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. ##[3] K. KOK, “Dynamic Pricing as Control Mechanism,” in proc. IEEE Power Eng. Soc. Gen. Meet., July 2011, pp.18. ##[4] M. Nikzad, B. Mozafari, A.M. Ranjbar, “Reliability Enhancement and Price Reduction of Restructured Power System with Probabilistic DayAhead Real Time Pricing Contract,” Int. Conf. Power Sys. Tech. (POWERCON), Oct. 2010. ##[5] A. J. Conejo, J. M. Morales,L. Baringo, “RealTime Demand Response Model,” IEEE Tran. Smart Grid, vol. 1, no. 3, Dec 2010, pp.236 – 242. ##[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.6067. ##[7] P. Joskow and J. Tirole, Retail Electricity Competition, 2004, NBER Working Paper No. 10473. ##[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. ##[9] A.H. MohsenianRad, A. LeonGarcia, “Optimal Residential Load Control with Price Prediction in RealTime Electricity Pricing Environments,” IEEE Trans. Smart Grid, vol. 1, no. 2, Sep. 2010, pp.120 –133.##]
Congestion Management in Power Systems Via Intelligent Method
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2
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 intelligencebased optimization technique to solve the congestion problem on the three IEEE 14bus, 30bus and 57bus 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
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23
31


S.A
Hashemi Zadeh
Department of electrical engineering, Islamic Azad University, Rafsanjan, Iran,
Department of electrical engineering, Islamic
Iran
sa_hashemizadeh@yahoo.com


A.A
Gharaveisi
Assistant, of Control Engineering, Department of electrical engineering, Shahid bahonar University, Kerman, Iran,
Assistant, of Control Engineering, Department
Iran
a_gharaveisi@yahoo.com


GH
Shahgholian
Associate, of Electrical Engineering, Department of electrical engineering, Islamic Azad University, Najaf abad, Iran,
Associate, of Electrical Engineering, Department
Iran
shahgholian@iaun.ac.ir
Bactria foraging algorithm
Power losses
congestion management
Distributed generations (DGs
[Sujatha Balaraman, “Congestion management deregulated power system using real coded genetic algorithm”, International Journal of Engineering Science and Technology, Vol.2, No.11, pp.66816690, 2010. ##A. F. Kaptue Kamga, S. Voller, J. F. Verstege, “Congestion Management in Transmission Systems with Large Scale Integration of Wind Energy”, Integration of WideScale Renewable Resources In to the Power Delivery System, CIGRE/IEEE PES Joint Symposium, 2009. ##A. Vergnol, J. Sprooten, B. Robyns, V. Rious, J. Deuse, “Optimal network congestion management using wind farms”, Integration of WideScale Renewable Resources Into the Power Delivery System, CIGRE/IEEE PES Joint Symposium, 2009. ##J. Hazra, and A. K. Sinha, “Congestion management using ultiobjective particle swarm optimization”, IEEE Trans. Power Syst., Vol.22, No.4, pp. 17261734, Feb. 2007. ##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.16, Jun. 2007. ##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. ##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.156163, Feb. 2007. ##J. Hazra, and A. K. Sinha, “Congestion management using ultiobjective particle swarm optimization”, IEEE Trans. Power Syst., Vol.22, No.4, pp.17261734, Feb. 2007. ##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.18191823, 2002. ##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. ##Ray D.Zimmerman and Carlos E.MurilloSanchez. Matpower, “A MATLAB power system, simulation package”, Sep.21, 2007##]
Robust Model for Networked Control System with Packet Loss
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2
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.
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33
37


Mohsen
Jahanshahi
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Department of Computer Engineering, Central
Iran
mjahanshahi@iauctb.ac.ir


Sayyid Mohsen
Houshyar
Department of Electrical Engineering Science & Research Branch, Islamic Azad University, Tehran, Iran.
Department of Electrical Engineering Science
Iran
houshyar.m@srbiau.ac.ir


Amir Reza
Zare Bidaki3
Young Researchers and Elite Club, Buinzahra Branch, Islamic Azad University, Buinzahra, Iran.
Young Researchers and Elite Club, Buinzahra
Iran
azare@buiniau.ac.ir
network control system (NCS)
Packet Loss
Robust Theory
[Luca Schenato, “To Zero or to Hold Control Inputs With Lossy Links”, IEEE Trans. On Automatic Control, Vol.54, No.5, May 2009. ##Huiying Chen, Wanliang Wang, Zuxin Li, “Synthetic Modeling and Control of Networked Control Systems with ##Multipacket Transmission”, International Conference on Advanced Computer Control, Aug. 2012. ##Xiaosheng Fang, Jingcheng Wang, “Stochastic Observerbased 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 611, 2013. ##Ling Q., Lemmon M.D., “Soft realtime scheduling of networked control systems with dropouts governed by a Markov chain”, Proc. American Control Conf., pp.4845 –4850, 2003. ##Seiler P., Sengupta R.,”An H approach to networked control”, IEEE Trans. Autom. Control, Vol.50, No.3, pp.356 –364, 2005. ##Wu J., Chen T., “Design of networked control systems with packet dropouts”, IEEE Trans. Autom. Control, Vol.52, No7, pp. 1314–1319, 2007. ##Wang Z., Ho D.W.C., Liu X., “Varianceconstrained control for uncertain stochastic systems with missing measurements”, IEEE Trans. Syst. Man. And Cybern, Vol.5, No.35, pp.746–753, 2005. ##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. ##Yang F., Wang Z.D., Ho W.C., Gani M., “Robust H control with missing measurements and timedelays”, IEEE Trans. Autom. Control, Vol.52, No.9, pp.1666–1672, 2007. ##Y. Wang, G. Yang, “H∞ Controller Design for Networked Control Systems via Activevarying Sampling Period Method”, Automatica sinica, Jul. 2008. ##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. ##S. Lun, S. Wang, “Robust H∞ cost control for Networked ##control systems based on TS model”, IEEE Transactions on systems, Apr. 2010.##]
A Novel Method for VANET Improvement using Cloud Computing
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2
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.
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39
44


Saied
Raeeszadeh
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
saied.raeeszadeh@gmail.com


Reza
SabbaghiNadooshan
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Electrical Engineering Department, Central
Iran
r_sabbaghi@iauctb.ac.ir
Cloud computing
Monitoring
LISP
Algorithm
Vanet
MANET
GPS
[R. Moskowitz and P. Nikander, “Host Identity Protocol (HIP) Architecture”, RFC 4423, IETF Network Working Group, May 2006. ##M. O’Dell, “GSE – An Alternate Addressing Architecture for IPv6”, Internet Draft draftietfipngwggseaddr00.txt, IETF NetworkWorking Group, February 1997. ##J. Saltzer, “On the Naming and Binding of Network Destinations, RFC”, 1498, IETF Network Working Group, August 1993. ##R. Hiden, “New Scheme for Internet Routing and Addressing, (ENCAPS) for IPNG”, RFC 1955, IETF Network Working Group, June 1996. ##N. Chiappa, “Endpoints and Endpoint Names: A ProposedEnhancement to the Internet Architecture”, 1999. Available from:http://ana.lcs.mit.edu/jnc/tech/endpoints.txt ##C. Bettstetter, “Smooth is better than sharp: A random mobility model for simulation of wireless networks”, MSWiM, pp. 19–27, 2000. ##C. Bettstetter, “Mobility modeling in wireless networks: Categorization, smooth movement, and border effects”, ACM SIGMOBILE’01, pp. 55–67, 2001. ##J. Broch, D. Maltz, D. Johnson, Y. Hu, J. Jetcheva, “performance comparison of multihop wireless ad hoc network routing protocols”, MOBICOM’98, pp. 85–97, 1998. ##T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research”, In MOBICOM’02, pp. 483–502, 2002. ##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. ##G. Pei, M. Gerla, X. Hong, and C. Chiang, “A wireless hierarchical routing protocol with group mobility”, IEEE WCNC’99, pp. 1536–1540, 1999. ##H. Sawant, J. Tan, and Q. Yang, “Study of an intervehicle communication protocol for vehicleinfrastructure integration (VII)”, In Transportation Research Board 84th Annual Meeting, Washington, DC, 2005. ##J. Yoon, M. Liu, and B. Noble, “Random waypoint considered harmful”, INFOCOM’03, 2003, pp. 1312–1321. ##M. Abuelela and S. Olariu, “Content delivery in zeroinfrastructure VANET”, Vehicular Networks: From Theory to Practice, Taylor & Francis, pp. 8.18.15, 2009. ##A.Aijaz, B. Bochow, F. Dotzer, A. Festag, M. Gerlach, R. Kroh, and T. Leinmuller, “Attacks on intervehicle communication systems – an analysis”, Proc. International Workshop on Intelligent Transportation (WIT’2006), Hamburg, Germany, pp. 189194, 2006. ##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. 29052909, May 2005. ##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. 181184, 2001. ##M. Eltoweissy, S. Olariu, and M. Younis, “Towards autonomous vehicular clouds”, Proc. Ad Hoc Nets, Victoria, BC, Canada, pp. 1 16, August 2010. ##J. Eriksson, H. Balakrishnan, and S. Madden, “Cabernet: vehicular content delivery using WiFi”, Proc. 14th ACM International Conference on Mobile Computing and Networking (MobiCom’2008), San Francisco, CA, USA, pp.199210, September 2008. ##M. Fontaine, “Traffic monitoring, Vehicular Networks: From Theory to Practice”, Taylor & Francis, Boca Raton, FL, pp. 1.11.28, 2009. ##S. Hodgson, “What is cloud computing?”, May 2008, available at: http://winextra.com/2008/05/02/whatiscloudcomputing ##J.N. Hoover and R. Martin, “Demystifying the cloud”, InformationWeek Research &Reports, pp. 3037, June 2008. ##U. Lee, R. Cheung, and M. Gerla, “Emerging vehicular applications, Vehicular Networks: From Theory to Practice”, Taylor & Francis, Boca Raton, FL, pp. 6.16.30, 2009. ##C. Lochert, B. Scheuermann, M. Caliskan, and M. Mauve, “The feasibility of information dissemination in vehicular adhoc networks”, Proc. 4th Annual Conference on Wireless Ondemand Network Systems and Services (WONS’07), Oberguyrgl, Austria, pp. 9299, January 2007. ##C. Lochert, B. Scheuermann, C. Wewetzer, A. Luebke, and M. Mauve, “Data aggregation and roadside unit placement for a VANET traffic information system”, Proc. 5th ACM International Workshop on Vehicular Ad Hoc Networks (VANET’2008), pp.5865, September 2008. ##J. Ott and D. Kutscher, “Drivethru internet: IEEE 802.11b for automobile users”, Proc. IEEE INFOCOM, pp. 373, 2004. ##J. Ott and D. Kutscher, “A disconnectiontolerant transport for drivethru internet environments”, Proc. IEEE INFOCOM, pp. 18491862, 2005. ##I. Sreedevi, and J. Black, “Loop detectors, California Center for Innovative Transportation”, available at: ttp://calccit.org/itsdecision/serv_and_tech/Traffic_Surveillance/roadbased/inroad/loop_report.html, 2001. ##G.Yan, S. Olariu, and M.C. Weigle, “Providing VANET security through active position detection”, Computer Communications, Vol. 31, No.12, pp. 28832897, 2008. ##G.Yan, S. Olariu, and M.C. Weigle, “Providing location ##security in vehicular adhoc networks”, IEEE Wireless Communications, Vol. 16, No. 6, pp. 4855, 2009. ##Y.Yang and R. Bagrodia, “Evaluation of VANETbased advanced intelligent transportation systems”, Proc. 6th ACM International Workshop on Vehicular Ad Hoc Networks (VANET’2009), Beijing, China, pp. 312, September 2009. ##S. Olariu, I. Khalil, and M. Abuelela, “Taking VANET to the clouds, International Journal of Pervasive”, Computing and Communications, Vol. 7, No. 1, pp. 721, 2011. ##D. Farinacci et al., “Locator/ID Separation Protocol (LISP)”, IETF Internet Draft, draftfarinaccilisp05.txt, November 2007. ##T. Narten, “Routing and Addressing Problem Statement”, IETF Internet Draft, draftnartenradirproblemstatement00.txt, July 2007. ##Y. H. Chu, “Smart Cloud Computing Network Architecture and Services”, Chunghwa Telecom Labs., 22 Sept. 2011. ##J.A. Misener, S. Dickey, J. VanderWerf, and R. Sengupta, “Vehicleinfrastructure cooperation”, Vehicular Networks: From Theory to Practice, Taylor & Francis, CRC Press, Boca Raton, FL, pp. 3.18.35, 2009. ##R. Sengupta, S. Rezaei, S.E. Shlavoder, D. Cody, S. Dickey, and H. Krishnan, “Cooperative collision warning systems: concept definition and experimental implementation”, California PATH Technical Report UCBITSPRR20066, May 2006. ##R.P. Roess, E.S. Prassas, W.R. McShane, “Traffic Engineering”, 3rd ed., Pearson PrenticeHall, Upper Saddle River, NJ, 2004. ##P.Varaiya, X.Y. Lu, and R. Horowitz, “Deliver a set of tools for resolving bad inductive loops and correcting bad data”, October 2006, available at: http://path.berkeley.edu/,xylu/TO6327/TO6327_SEMP.pdf ##R.N. Calheiros , C. Vecchiola , D. Karunamoorthy, and R. Buyya, “The Aneka platform and QoSdriven resource provisioning for elastic applications on hybrid Clouds”, Future Generation Computer Systems, Vol. 28, pp. 861–870, 2012. ##S. Islam and J. C. Grégoire, “Giving users an edge: A flexible Cloud model and its application for multimedia”, Future Generation Computer Systems, Vol. 28, pp. 823–832, 2012. ##]
Flexibility Enhancement of Energy Delivery Systems through Smart Operation of Micro Energy Hub
2
2
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.
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52


M
Yazdani Damavandi
Iran


P
Teimourzadeh Baboli,
Iran


M
ParsaMoghaddam
Faculty of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.
Faculty of Electrical and Computer Engineering,
Iran
parsa@modares.ac.ir
Energy delivery
flexibility enhancement
micro energy hub
multienergy system
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New Ant Colony Algorithm Method based on Mutation for FPGA Placement Problem
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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.
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Setareh
Shafaghi
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central
Iran
strshafaghi@yahoo.com


Fardad
Farokhi
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central
Iran


Reza
SabbaghiNadooshan
Electrical Engineering Department, Central Tehran Branch, Islamic Azad University,
Electrical Engineering Department, Central
Iran
r_sabbaghi@iauctb.ac.ir
Ant colony algorithm
Mutation
FPGA placement
Optimization
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