ORIGINAL_ARTICLE
Mix proportioning of high-performance concrete by applying the GA and PSO
High performance concrete is designed to meets special requirements such as high strength, high flowability, and high
durability in large scale concrete construction. To obtain such performance many trial mixes are required to find desired
combination of materials and there is no conventional way to achieve proper mix proportioning. Genetic algorithm is a global
optimization technique based on mechanics of natural selection and natural genetics and can be used to find a near optimal
solution to a problem that may have many solutions. Particle swarm optimization is another evolutionary searching strategy
motivated by social behaviors to obtain optimum answer. This paper presents a method whereby the mixture proportion of
concrete can be optimized to reduce the number of trial mixtures with desired properties by using the genetic algorithm and
particle swarm optimization techniques.
http://ijsee.iauctb.ac.ir/article_510067_b098c7c30e54173ef924432b452d2ddf.pdf
2012-03-01T11:23:20
2017-11-20T11:23:20
1
8
High-performance concrete
Genetic algorithm
particle swarm optimization
Mixture
Alireza
rezaee
true
1
Ph.D. of Electrical Engineering, Islamic Azad university hashtgerd branch, hasthtgerd, Alborz, Iran
Ph.D. of Electrical Engineering, Islamic Azad university hashtgerd branch, hasthtgerd, Alborz, Iran
Ph.D. of Electrical Engineering, Islamic Azad university hashtgerd branch, hasthtgerd, Alborz, Iran
AUTHOR
mohamad reza
hasani ahangar
mrhassani@iust.ac.ir
true
2
Assistant Professor, center of ghadr, Imam hossein university, Tehran, Iran
Assistant Professor, center of ghadr, Imam hossein university, Tehran, Iran
Assistant Professor, center of ghadr, Imam hossein university, Tehran, Iran
AUTHOR
[1] A.M. Neville, P.C Aitcin, High-performance concrete- An
1
overview, 1998.
2
[2] I. Maruyama, M. Kanematsu, “Optimization of Mix Proportion
3
of Concrete under Various Severe Conditions by Applying the
4
Genetic Algorithm”, University of Tokyo, Japan, 2004.
5
[3] D. Goldberg, Genetic Algorithm in search, optimization and
6
Machine Learning, Addison Welsley publishing company,
7
[4] A. Chipperfield, Genetic algorithm user’s guide for use with
8
MATLAB, Version 1.2.
9
[5] J. Singh, PSO MATLAB Toolbox, PSOTOOLBOX Open
10
Source.2003.
11
[6] MATLAB, Version 6.5, The Math Works, 2002.
12
ORIGINAL_ARTICLE
Extraction of Sensory part of Ulnar Nerve Signal Using Blind Source Separation Method
A recorded nerve signal via an electrode is composed of many evokes or action potentials, (originated from individual axons)
which may be considered as different initial sources. Recovering these primitive sources in its turn may lead us to the anatomic
originations of a nerve signal which will give us outstanding foresights in neural rehabilitations. Accordingly, clinical interests
may be raised on extraction of sensory and motor components of the nerve signals in neural injuries. One example is to extract
sensory fraction in sacral nerve to sense the bladder filling up in paraplegic or quadriplegic people [3]. Blind Source Separation
(BSS) methods seem good solutions for extraction of the initial sources which are contributing in recorded mixed sources.
Considering the nerve signal as a superposition of many axonal or fascicular signals, we have encouraged to try BSS methods to
see whether it can recover the sensory and motor sources of a recorded nerve signal. Accordingly, both PCA and ICA techniques
were examined in a case study (human left arm), in which the response of the ADM muscle to the Ulnar nerve stimulation were
recorded in two points. The corresponded sensory signal was recorded on the pinkie at the same time (all recordings were done
via surface electrodes). It was shown that ICA (supremely better than PCA) was able to separate initial sources (ADM recorded
signals) into two signals so that one of them was most similar to the sensory (Pinkie) signal. The level of similarity was quantified
via correlation analysis. As the result, it is concluded that ICA is capable of extracting Sensory and Motor signals in PNS.
http://ijsee.iauctb.ac.ir/article_510068_7cbb10f08d27a637f31730c655096df6.pdf
2012-03-01T11:23:20
2017-11-20T11:23:20
9
15
PNS1
ENG2
surface electrode
Ulnar nerve signals
sensory signal
motor signal
BSS3
PCA4
ICA5
Correlation analysis
Alireza
Kashaninia
true
1
Assistant Professor in Electrical Engineering, Islamic Azad University, Central Tehran Branch (IAUCTB), Advanced Bionic Systems (ABS)
lab, Tehran, Iran.
Assistant Professor in Electrical Engineering, Islamic Azad University, Central Tehran Branch (IAUCTB), Advanced Bionic Systems (ABS)
lab, Tehran, Iran.
Assistant Professor in Electrical Engineering, Islamic Azad University, Central Tehran Branch (IAUCTB), Advanced Bionic Systems (ABS)
lab, Tehran, Iran.
AUTHOR
S
Nooreddin Jafari
jafari@iaul.ac.ir
true
2
M. Sc Degree in Electrical Engineering, Department of electrical engineering, Islamic Azad University, Langaroud branch, Guilan, Iran
M. Sc Degree in Electrical Engineering, Department of electrical engineering, Islamic Azad University, Langaroud branch, Guilan, Iran
M. Sc Degree in Electrical Engineering, Department of electrical engineering, Islamic Azad University, Langaroud branch, Guilan, Iran
AUTHOR
[1] Z. Navarro et al, "A critical review of interfaces with the
1
peripheral nervous system for the control of neuroprostheses
2
and hybrid bionic systems", Journal of the Peripheral Nervous
3
System, Vol.10, pp.229–258, 2005.
4
[2] Ming-Shaung Ju, Hsin-Chun Chien, Gin-Shin Chen, Chou-
5
Ching K. Lin1, Cheng-Hung Chang, Chi-Wen Chang, "Design
6
and Fabrication of Multi-microelectrode Array for Neural
7
Prosthesis", Journal of Medical and Biological Engineering,
8
Vol.22, No.1, pp.33-40, Accepted 8 April 2002.
9
[3] A. Harb, Y. HU, M. Sawan, A. Abdelkerim, M.M. elhilali",
10
Low-Power CMOS Interface for Recording and Processing
11
Very Low Amplitude Signals", Analog Integrated Circuits and
12
Signal Processing, Vol.39, pp.39–54, 2004.
13
[4] Tessler, A., “Intraspinal transplants”, Ann. Neurol., Vol.29,
14
pp.115-123, 1991.
15
[5] Schnell, L., Schwab, M.E., “Sprouting and regeneration of
16
lesioned corticospinal tract fibres in the adult rat spinal cord”,
17
Eur. J. Neurosci., Vol.5, pp.1156-1171, 1993.
18
[6] Schwab, M.E., Kapfhammer, J.P., Bandtlow, C.E., “Inhibitors
19
of neurite growth.”, Ann. Rev. Neurosci., Vol.16, pp.565-595,
20
[7] Faden, A.I., Salzman, S.K., “The Neurobiology of Central
21
Nervous System Trauma.”, Experimental pharmacology. In:
22
Salzman, S.K., Faden, A.I. (Eds.), Oxford University Press,
23
Oxford, pp.227-244, 1994.
24
[8] Reier, P.J., Anderson, D.K., Schrimsher, G.W., Bao, J.,
25
Friedman, R.M., Ritz, L.A., Stokes, B.T., “Neural cell grafting:
26
anatomical and functional repair of the spinal cord”, In:
27
Salzman, S.K., Faden, A.L. (Eds.), “The Neurobiology of
28
Central Nervous System Trauma”, Oxford University Press,
29
Oxford, pp.288-311, 1994.
30
[9] Li, Y., Field, P.M., Raisman, G, “Repair of adult rat
31
corticospinal tract by transplants of olfactory ensheathing
32
cells”, Science 277, pp.2000-2002, 1997.
33
[10] P. Decherchia,_P. Gauthierb, “Regeneration of Acutely and
34
Chronically Injured Descending Respiratory Pathways Within
35
Post-Traumatic Nerve Grafts”, Neuroscience Vol.112, No.1,
36
pp.141-152, 2002.
37
[11] M. Firuzi, P. Moshayedi, H. Saberi, H. Mobasheri, F.
38
Abolhassani, MA. Oghabian, “Effects of schwan cell
39
transplantation on recovery of spinal cord injury of rat: A
40
remedy for spinal cord injuries FENS”, Federation of European
41
Neurosciences, 4th Forum of European Nerosciences,Hosted
42
by Federation of European Neurosciences Societies (FENS),
43
Lisbon, Portugal, July 10-14, 2004.
44
[12] Akin T, Najafi K, Smoke RH, Bradley RM, “A micromachined
45
silicon sieve electrode for nerve regeneration applications”,
46
IEEE Trans Biomed Eng, Vol.41, pp.305–313, 1994.
47
[13] Wallman L, Zhang Y, Laurell T, Danielsen N., “The geometric
48
design of micromachined silicon sieve electrodes influences
49
functional nerve regeneration.”, Biomaterials, Vol.22,
50
pp.1187–1193, 2001.
51
[14] Najafi K, Wise KD., “An implantable multielectrode array with
52
on-chip signal processing”, IEEE J Solid State Circuits Vol.21,
53
pp.1035–1044, 1986.
54
[15] Najafi K, Wise KD, Mochizuki T., “A high-yield ICcompatible
55
multichannel recording array”, IEEE Trans Electron
56
Devices, Vol.32, pp.1206–1211, 1985.
57
[16] John W. Clark, Jr., “Medical Instrumentation, Application and
58
Design, chapter 4: The Origin of Biopotentials”, Houghton
59
Mifflin Company, 1992.
60
[17] A.V. Holden, “Lecture notes in biomathematics models of
61
stochastic activity of neurons”, Vol. 12, Springer Verlag, 1976.
62
[18] A. Pappolis, “Probability and stochastic process”, Prentice
63
Hall, 1991.
64
[19] W. Tesfayesus', P. Yoo, D. M. Durand, "Blind Source
65
Separation of Nerve Cuff Recordings" Proceedings of the 25*
66
Annual International Conference of the IEEE EMBS, Cancun,
67
Mexico - September 17-21, 2003.
68
[20] WTesfayesus and D M Durand, "Blind source separation of
69
peripheral nerve recordings", J. Neural Eng., Vol.4, pp. S157–
70
S167, 2007.
71
[21] W. Tesfayesus, P. Yoo, M. Moffitt, and D. M. Durand, "Blind
72
Source Separation of Nerve Cuff Recordings", Proceedings of
73
the 26th Annual International Conference of the IEEE EMBS,
74
San Francisco CA, September 1-5, 2004.
75
[22] Jolliffe IT. “Principal Component Analysis”. New York:
76
Springer-Verlag, 1988.
77
[23] Jezernik S, Grill WM, Sinkjaer T., "Detection and inhibition of
78
hyperreflexia-like bladder contractions in the cat by sacral
79
nerve root recording and electrical stimulation", Neurourol
80
Urodyn., Vol.20, No.2, pp.215-30, 2001
81
ORIGINAL_ARTICLE
Loss of Load Expectation Assessment in Deregulated Power Systems Using Monte Carlo Simulation and Intelligent Systems
Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In
this paper, generation reliability is considered, and a method for its assessment using intelligent systems is proposed. Also,
because of power market and generators’ forced outages stochastic behavior, Monte Carlo Simulation is used for reliability
evaluation. Generation reliability merely focuses on interaction between generation complex and load. Therefore, in this
research, based on market type and its concentration, reserve margin, and various future times, a Neuro-Fuzzy system is
proposed for evaluation of generation reliability which is valid and usable for all kinds of power pool markets. Finally, the
proposed method is assessed on IEEE-Reliability Test System with satisfactory results. It will be shown that if market
becomes more concentrated or price elasticity of demand increases, reliability will improve
http://ijsee.iauctb.ac.ir/article_510069_df938ae65ccea79dd05a68822a5aa72d.pdf
2012-03-01T11:23:20
2017-11-20T11:23:20
17
25
Generation reliability
Power pool market
Monte Carlo simulation
Intelligent systems
H
Haroonabadi
haroonabadi@iiau.ac.ir
true
1
Electrical Dep. Islamic Azad University, Islamshahr Branch, Tehran, IRAN
Electrical Dep. Islamic Azad University, Islamshahr Branch, Tehran, IRAN
Electrical Dep. Islamic Azad University, Islamshahr Branch, Tehran, IRAN
AUTHOR
M.-R
Haghifam
haghifam@modares.ac.ir
true
2
Professor, Electrical & Computer Dep., Tarbiat Modarres University, Tehran, IRAN
Professor, Electrical & Computer Dep., Tarbiat Modarres University, Tehran, IRAN
Professor, Electrical & Computer Dep., Tarbiat Modarres University, Tehran, IRAN
AUTHOR
[1] Billinton R., Allan R., “Reliability Evaluation of Power
1
Systems”, Second edition. New York: Plenum press, p.1,
2
[2] Billinton R., Allan R., “Reliability Evaluation of Power
3
Systems”, Second edition. New York: Plenum press, p.10,
4
[3] Billinton R., Allan R., “Reliability Evaluation of Engineering
5
Systems”, Second edition. New York: Plenum press, p. 372,
6
[4] L. Salvaderi, “Electric Sector Restructuring in Italy”, IEEE
7
Power Engineering Review, Vol. 20, No. 4, pp.12-16, 2000.
8
[5] H. B.Puttgen, D. R. Volzka, M. I. Olken, “Restructuring and
9
Reregulation of The US Electric Utility Industry”, IEEE
10
Power Engineering Review, Vol. 21, No. 2, pp.8-10, 2001.
11
[6] Robert S. Pindyck, D. L. Rubinfeld., “Microeconomics”, Fifth
12
edition. USA: Prentice Hall, 2001.
13
[7] Azami, R. Abbasi, A.H. Shakeri, J. Fard, A.F., “Impact of
14
EDRP on Composite Reliability of Restructured Power
15
Systems”, in: Proc. PowerTech, 2009 IEEE Bucharest
16
Conference, pp.1-8, 2009.
17
[8] Wang, P. Ding, Y. Goel, L., “Reliability Assessment of
18
Restructured Power Systems Using Optimal Load Shedding
19
Technique”, Generation, Transmission & Distribution, IET,
20
pp.628 – 640, 2009.
21
[9] R. Meziane, Y. Massim, A. Zeblah, A. Ghoraf, R. Rahli,
22
“Reliability Optimization Using Ant Colony Algorithm Under
23
Performance and Cost Constraints”, Electric Power System
24
Research journal, Vol.76, pp. 1-8, 2005.
25
[10] Haroonabadi, H. Haghifam, M.-R., “Generation Reliability
26
Evaluation in Power Markets Using Monte Carlo Simulation
27
and Neural Networks”, in: Proc. 15th international conference
28
on Intelligent System Applications to Power Systems (ISAP
29
09), pp.1-6, 2009.
30
[11] International Energy Agency (IEA), “The Power to Choose-
31
Demand Response in Liberalized Electricity Markets”,
32
France: IEA, p.21, 2003.
33
[12] Severin Borenstein, “Understanding competitive pricing and
34
market power in wholesale electricity market”, University of
35
California energy institute, Feb.1999.
36
[13] International Energy Agency (IEA), “The Power to Choose-
37
Demand Response in Liberalized Electricity Markets”,
38
France: IEA, p.54, 2003.
39
[14] Nordic competition authorities, “A Powerful Competition
40
Policy- Towards a more coherent competition policy in the
41
Nordic market for electric power”, Copenhagen, Oslo,
42
Stockholm, June 2003.
43
[15] International Energy Agency (IEA), “Security of Supply in
44
Electricity Markets - Evidence and Policy Issues”, France:
45
IEA, p.16, 2002.
46
[16] “Reliability Test System Task Force of The IEEE
47
Subcommittee on the application of probability Methods”,
48
IEEE Reliability Test System, IEEE Transactions, Pas-98,
49
No.6, pp.2047-2054, Nov/Dec 1979
50
ORIGINAL_ARTICLE
Analysis and Comparison of Load Flow Methods for Distribution Networks Considering Distributed Generation
Conventional passive distribution networks are changing to modern active distribution networks which are not radial.
Conventional load flow methods should be modified for new distribution networks analysis. In modern distribution networks
distributed generation (DG) units are embedded with conventional and/or renewable resources. DG units are generally modeled
as PV or PQ nodes which inject active power electricity to the network. Modeling of a DG unit is dependent on the operation and
its type of connection to the grid. This paper considers the most important new load flow methods for DG integrated distribution
networks. The methods are analyzed and compared with each other. Every method has advantages and disadvantages in different
conditions. So, comparison of these methods can be useful to select the best method for a typical network. As a result, some
suggestions are proposed to apply the new methods.
http://ijsee.iauctb.ac.ir/article_510070_052e005a33bbcfd2339112cf3a34071d.pdf
2012-03-01T11:23:20
2017-11-20T11:23:20
27
32
Load flow
modern distribution networks
Distributed Generation
M
Sedghi
true
1
K.N. Toosi University of Technology, Iran
K.N. Toosi University of Technology, Iran
K.N. Toosi University of Technology, Iran
AUTHOR
M
Aliakbar-Golkar
true
2
Professor, K.N. Toosi University of Technology, Iran
Professor, K.N. Toosi University of Technology, Iran
Professor, K.N. Toosi University of Technology, Iran
AUTHOR
[1] M. Aliakbar-Golkar, “Design and operation of electric
1
distribution systems”, K.N.Toosi University, Tehran, 2001.
2
[2] N. Hadjsaid, M.C. Alvarez-Hérault, R. Caire, B. Raison, J.
3
Descloux and W. Bienia, “Novel architectures and operation
4
modes of distribution network to increase DG integration”, IEEE
5
Power and Energy Society General Meeting, pp.1-6, 25-29 July,
6
[3] S.M. Moghadas-Tafreshi and E. Mashhour, “Distributed
7
generation modeling for power flow studies and a three-phase
8
unbalanced power flow solution for radial distribution systems
9
considering distributed generation”, Electric Power Syst.
10
Research, Vol. 79, pp.680-686, 2009.
11
[4] H. Yang, F. Wen, L. Wang and S.N. Singh, “Newton-downhill
12
algorithm for distribution power flow analysis”, 2nd IEEE Int.
13
Conf. on Power and Energy, Johor Baharu, Malaysia, Dec. 1-3,
14
[5] L.R. Araujo, D.R.R. Penido, S. Carneiro Jr, J.L.R. Pereira and
15
P.A.N. Garcia, “A comparative study on the performance of
16
TCIM full Newton versus backward-forward power flow
17
methods for large distribution systems”, IEEE Power Syst. Conf.
18
and Exposition, pp.522-526, Oct. 29- Nov. 1, 2006.
19
[6] Da Costa V.M., Martins M. and Preira J.L.R., “An augmented
20
Newton-Raphson power flow formulation based on current
21
injections”, Electrical Power and Energy Syst., Vol. 23, pp.305-
22
312, 2001.
23
[7] S. Kamel, M. Abdel-Akher and M.K. El-Nemr, “A new
24
technique to improve voltage controlled nodes (PV nodes) in the
25
current injection Newton-Raphson power flow analysis”, 45th
26
Int. Universities Power Engineering Conf. (UPEC), Agu. 31-
27
Sept. 3, pp.1-4, 2010.
28
[8] T.H. Chen, M.S. Chen, K.J. Hwang, P. Kotas and E.A. Chebli,
29
“Distribution system power flow analysis- A rigid approach”,
30
IEEE Trans. on Power Delivery, Vol. 6, No. 3, pp.1146-1152,
31
July 1991.
32
[9] A.G. Bhutad, S.V. Kulkarni and S.A. Khaparde, “Three-phase
33
load flow methods for radial distribution networks”, TENCON
34
Conf. on Convergent Technologies for Asia-Pacific Region,
35
Vol. 2, pp.781-785, 15-17 Oct., 2003.
36
[10] H. Chen, J. Chen, D. Shi and X. Duan, “Power flow study and
37
voltage stability analysis for distribution systems with
38
distributed generation”, IEEE Power Engineering Society
39
General Meeting, pp.1-8, 2006.
40
[11] C.A. Penuela, M. Granada E. and J.R.S Mantovani,
41
"Probabilistic analysis of the distributed power generation in
42
weakly meshed distribution systems", IEEE/PES Transmission
43
and Distribution Conf. and Exposition: Latin America T&D-LA,
44
pp.171-177, 8-10 Nov. 2010.
45
[12] G.X. Luo and A. Semlyen, “Efficient load flow for large weakly
46
meshed networks”, IEEE Trans. on Power Syst., Vol. 5, No. 4,
47
pp.1309-1316, 1990.
48
[13] A. Augugliaro, L. Dusonchet, S. Favuzza, M.G. Ippolito and
49
E.R. Sanseverino, “A new backward/forward method for
50
solving radial distribution networks with PV nodes”, Electric
51
Power Syst. Research, Vol. 78, pp.330-336, 2008.
52
[14] P. Acharjee and S.K. Goswami, “Simple but reliable two-stage
53
GA based load flow”, Electric Power Components and Syst.,
54
Vol. 36, No. 1, pp.47-62, 2008.
55
[15] D. Chakraborty, C.P. Sharma, B. Das, K. Abhishek and T.
56
Malakar, “Distribution load flow solution using genetic
57
algorithm”, 3rd IEEE Int. Conf. on Power Syst., Kharagpur,
58
India, Dec. 27-29, 2009.
59
[16] P. Acharjee, and S.K. Goswami, “Chaotic Particle Swarm
60
Optimization based reliable algorithm to overcome the
61
limitations of conventional power flow methods”, IEEE/PES
62
Power Syst. Conf. and Exposition (PSCE), pp.1-7, 15-18
63
March, 2009.
64
[17] A. Rathinam, S. Padmini and V. Ravikumar, “Application of
65
supervised learning artificial neural networks [CPNN,BPNN]
66
for solving power flow problem”, IET-UK Int. Conf. on
67
Information and Communication Technology in Electrical
68
Science (ICTES), India, Dec. 20-22, pp.156-160, 2007.
69
[18] M. Cai, R. Chen and L. Kong, “Hyper-chaotic neural network
70
based on Newton iterative method and its application in solving
71
load flow equations of power system”, Int. Conf. on Measuring
72
Technology and Mechatronics Automation (ICMTMA), pp.226-
73
ORIGINAL_ARTICLE
The Influence of Smart Grid on TOU Programs With Respect to Production Cost and Load Factor, A Case Study of Iran
Reaching an electricity system which is both economically efficient and environmentally friendly is motivating countries to
design and execute different types of TOU demand response programs. But there are certain deficiencies which prevent these
programs to effectively modify the load shape. Smart grid as a means could help the electricity system to reach the highest
demand side management goals which are inaccessible through today’s methods. In this paper, problems facing today’s demand
response programs have been described and Iran’s recently designed TOU program has been analyzed as an example. The
influence of this program has been simulated on Iran’s actual load curve. As a solution to the problems facing DR programs,
Smart Grid has been introduced and the influence of Real Time Pricing (RTP) program in Smart Grid has been simulated on an
actual load curve of Iran’s grid, using multi period load model and adaptive periods. Reaching market equilibrium and the effect
of Smart Grid on price curve has also been simulated. Finally, the current TOU program designed for Iran and the RTP program
in smart grid have been compared with respect to load shape modification, load factor, price curve, and production cost.
Eventually, savings made possible through smart grid per day have been evaluated.
http://ijsee.iauctb.ac.ir/article_510071_d8dc1899dd625ee19c886b03ea4479c2.pdf
2012-03-01T11:23:20
2017-11-20T11:23:20
33
43
Demand response problems
Real time pricing
Smart grid
Time of use programs
Hassan
Monsef
hmonsef@ut.ac.ir
true
1
Associate Professor, Electrical Engineering Department, Tehran University, Tehran, Iran
Associate Professor, Electrical Engineering Department, Tehran University, Tehran, Iran
Associate Professor, Electrical Engineering Department, Tehran University, Tehran, Iran
AUTHOR
Pouyan
Khajavi
true
2
Electrical Engineering Department, Tehran University, Tehran, Iran
Electrical Engineering Department, Tehran University, Tehran, Iran
Electrical Engineering Department, Tehran University, Tehran, Iran
AUTHOR
[1] U. S. Department of Energy, "Energy policy Act of 2005",
1
section 1252, February 2006.
2
[2] Iran Grid Management Company, 2009, www.igmc.ir.
3
[3] D.S. Kirschen, "Demand-side view of electricity markets"
4
IEEE Transaction on power systems, Vol. 18, No.2, pp.520-
5
527, May 2003.
6
[4] US Department of Energy, "Benefits of Demand Response in
7
Electricity Markets and Recommendations for Achieving
8
Them”, Report to the United States Congress, February 2006.
9
[5] D. S. Kirschen, G. Strbac, P. Cumperayot, D. Mendes,
10
"Factoring the elasticity of demand in electricity prices", IEEE
11
Transaction on power systems, Vol.15, No.2, pp.612-617, May
12
[6] P. Khajavi, H. Monsef, "Load profile reformation through
13
demand response programs using smart grid", Modern Electric
14
Power Systems (MEPS), pp.1-4, 2010.
15
43 International Journal of Smart Electrical Engineering, Vol.1, No.1, Winter 2012
16
[7] Rahimi F., Ipakchi A. , "Overview of Demand Response under
17
the Smart Grid and Market paradigms", Innovative Smart Grid
18
Technologies (ISGT), pp.1-7, 19-21 Jan. 2010.
19
[8] U.S. Department of Energy, “Smart Grid: An Introduction”
20
[9] USA’s National Energy Technology Laboratory (NETL), “A
21
Vision For The Modern Grid”, March 2007.
22
[10] Powermag, Vol.152, No.5, pp.42-46, May 2008.
23
[11] H. Aalami, G. R. Yousefi, M. Parsa Moghadam, “Demand
24
Response Model Considering EDRP and TOU Programs”,
25
Transmission and Distribution Conference and Exposition,
26
pp.1-8, 2008.
27
[12] Mehr News, Jun 2010, www.mehrnews.com.
28