ORIGINAL_ARTICLE
A New Control Method for Single-Phase Grid-Connected Inverter Using Instantaneous Power Theory
Because of installation for local consumers and since it is free of all contaminations, connecting photovoltaic cells to the grid via single-phase inverter is significantly on the rise. In this paper, a new simple current control is proposed for a single-phase grid-connected voltage source inverter. Using the PQ theory and modelling a single-phase system as an unbalanced three-phase system, a method is provided for reference current generation. In the proposed method, it is not necessary to generate a fictitious phase for the current signal. Also, the removal of adjusting filter parameters which were used to generate fictitious current signal increases the simplicity of the control system and reduces computational efforts, especially in the presence of distortion in the current. The simulation results confirm that the proposed method provides a precise and fast current control with minimum harmonic distortions.
http://joape.uma.ac.ir/article_590_9736d892a2abca4e6a59d27556e5d257.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
105
116
10.22098/joape.2017.2592.1225
Grid- tie inverter
Modified instantaneous power theory
Reference current generation
M.
Heidari
morteza.heidari@birjand.ac.ir
true
1
Faculty of Electrical and Computer Engineering, University of Birjand
Faculty of Electrical and Computer Engineering, University of Birjand
Faculty of Electrical and Computer Engineering, University of Birjand
LEAD_AUTHOR
M. A.
Shams Nejad
mshamsi@birjand.ac.ir
true
2
Faculty of Electrical and Computer Engineering, University of Birjand
Faculty of Electrical and Computer Engineering, University of Birjand
Faculty of Electrical and Computer Engineering, University of Birjand
AUTHOR
M.
Monfared
m.monfared@um.ac.ir
true
3
Faculty of Engineering, Ferdowsi University of Mashhad
Faculty of Engineering, Ferdowsi University of Mashhad
Faculty of Engineering, Ferdowsi University of Mashhad
AUTHOR
[1] M. Schmela,“Global market outlook for solar power/2016-2020” , EPIA, Belgium, 2016.
1
[2] H. Komurcugil, N. Altin, S. Ozdemir and I. Sefa “Lyapunov-function and proportional-resonant-based control strategy for single-phase grid-connected VSI with LCL filter,” IEEE Trans. Ind. Electron., vol. 63, no. 5, pp. 2838-2849, 2016.
2
[3] M. Monfared and S. Golestan “Control strategies for single-phase grid integration of small-scale renewable energy sources: A review,” Renewable SustainableEnergy Rev., vol. 16, pp. 4982-4993, 2012.
3
[4] Y. Shi, B. Liu and S. Duan “Low-frequency input current ripple reduction based on load current feedforward in a two-stage single-phase inverter,” IEEE Trans. Power Electron., vol. 31, no. 11, pp. 7972-7985, 2016.
4
[5] Y. Hu, Y. Du, W. Xiao, S. Finney and W. Cao “DC-link voltage control strategy for reducing capacitance and total harmonic distortion in single-phase grid-connected photovoltaic inverters,” IET Power Electron., vol. 8, no. 8, pp. 1386-1393, 2015.
5
[6] G. Zhu, X. Ruan, L. Zhang and X. Wang “On the reduction of second harmonic current and improvement of dynamic response for two-stage single-phase inverter,” IEEE Trans. Power Electron., vol. 30, no. 2, pp. 1028-1041, 2015.
6
[7] M. Banaei “Multi-stage DC-AC converter based on new DC-DC converter for energy conversion,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 42-53, 2016.
7
[8] M. Kumar; R. Gupta “Sampled-Time Domain Analysis of Digitally Implemented Current Controlled Inverter,” IEEE Trans. Ind. Electron., vol. PP, no.99, pp.1-1, 2016
8
[9] F. Wu, F. Feng, L. Luo J. Duan and L. Sun “Sampling period online adjusting-based hysteresis current control without band with constant switching frequency,” IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 270-277, 2015.
9
[10] S. Gautam and R. Gupta “Unified time-domain formulation of switching frequency for hysteresis current controlled AC/DC and DC/AC grid connected converters,” IET Power Electron., vol. 6, pp. 683-692, 2013.
10
[11] Z. Yao and L. Xiao “Control of single-phase grid-connected inverters with nonlinear loads,” IEEE Trans. Ind. Electron., vol. 60, pp. 1384-138, 2013.
11
[12] F. Wu, L. Zhang, and Q. Wu “Simple unipolar maximum switching frequency limited hysteresis current control for grid-connected inverter,” IET Power Electron., vol. 7, pp. 933-945, 2014.
12
[13] M. Ebrahimi, S. A. Khajehoddin and M. Karimi-Ghartemani “Fast and robust single-phase DQ current controller for smart inverter applications,” IEEE Trans. Power Electron., vol. 31, no. 5, pp. 3968-3976, 2016.
13
[14] S. Somkun and V. Chunkag “Unified unbalanced synchronous reference frame current control for single-phase grid-connected voltage-source conver-ters,” IEEE Trans. Ind. Electron., vol. 63, no. 9, pp. 5425-5436, 2016.
14
[15] Shuhui Li, Xingang Fu, Malek Ramezani, Yang Sun, Hoyun Won “A novel direct-current vector control technique for single-phase inverter with L, LC and LCL filters,” Electr. Power Syst. Res., vol. 125, pp. 235-244, 2015
15
[16] B. Bahrani, M. Vasiladiotis, andA. Rufer “High-order vector control of grid-connected voltage-source converters with LCL-filters,” IEEE Trans. Ind. Electron., vol. 61, pp. 2767-2775, 2014.
16
[17] M. Monfared, S. Golestan, and J. M. Guerrero “Analysis, design, and experimental verification of a synchronous reference frame voltage control for single-phase inverters,” IEEE Trans. Ind. Electron., vol. 61, pp. 258-269, 2014.
17
[18] T. Ye, N. Dai, C. S. Lam, M. C. Wong and J. M. Guerrero “Analysis, design, and implementation of a quasi-proportional-resonant controller for a multifunctional capacitive-coupling grid-connected inverter,” IEEE Trans. Ind. Appl., vol. 52, no. 5, pp. 4269-4280, 2016.
18
[19] G. Shen, X. Zhu, J. Zhang and D. Xu “A new feedback method for PR current control of LCL-filter-based grid-connected inverter,” IEEE Trans. Ind. Electron., vol. 57, no. 6, pp. 2033-2041,2010.
19
[20] M. Castilla, J. Miret, J. Matas, L. G. d. Vicuna, and J. M. Guerrero “Linear current control scheme with series resonant harmonic compensator for single-phase grid-connected photovoltaic inverters,” IEEE Trans. Ind. Electron., vol. 55, pp. 2724-2733, 2008.
20
[21] M. Castilla, J. Miret, J. Matas, L. G. d. Vicuna, and J. M. Guerrero “Control design guidelines for single-phase grid-connected photovoltaic inverters with damped resonant harmonic compensators,” IEEE Trans. Ind. Electron., vol. 56, pp. 4492-4501, 2009.
21
[22] J. He and Y. W. Li “Hybrid voltage and current control approach for DG-grid interfacing converters with LCL filters,” IEEE Trans. Ind. Electron., vol. 60, pp. 1797-1809, 2013.
22
[23] H. Akagi, E. H. Watanabe, M. Aredes, Instantaneous power theory and applications to power conditioning, Wiley-IEEE Press, 2007, pp. 41-107.
23
[24] W. Song, Z. Deng, S. Wang and X. Feng “A simple model predictive power control strategy for single-phase PWM converters with modulation function optimization,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5279-5289, 2016.
24
[25] V. Khadkikar and A. Chandra “A novel structure for three-phase four-wire distribution system utilizing unified power quality conditioner (UPQC),” IEEE Trans. Ind. Appl., vol. 45, no. 5, pp. 1897-1902, 2009.
25
[26] R. I. Bojoi, L. R. Limongi, D. Roiu and A. Tenconi “Enhanced power quality control strategy for single-phase inverters in distributed generation systems,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 798-806, 2011.
26
[27] X. Zong, A single phase grid connected DC/AC inverter with reactive power control for residential PV application, M.S. Thesis, Dept. Elec. Eng., Toronto Univ., Toronto, 2011.
27
[28] P. T. Krein, R. S. Balog, and M. Mirjafari “Minimum energy and capacitance requirements for single-phase inverters and rectifiers using a ripple port,” IEEE Trans. Power Electron. vol. 27, pp. 4690-4698, 2012.
28
[29] A. Reznik, M. G. Sim, A. Al-Durra, and S. M. Muyeen “Filter design and performance analysis for grid-interconnected systems,” IEEE Trans. Ind. Appl., vol. 50, pp. 1225-1232, 2014.
29
[30] M. Farhadi Kangarlu, E. Babaei, F. Blaabjerg “An LCL-filtered single-phase multilevel inverter for grid integration of PV systems,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 54-65, 2016.
30
ORIGINAL_ARTICLE
Optimal Capacitor Allocation in Radial Distribution Networks for Annual Costs Minimization Using Hybrid PSO and Sequential Power Loss Index Based Method
In the most recent heuristic methods, the high potential buses for capacitor placement are initially identified and ranked using loss sensitivity factors (LSFs) or power loss index (PLI). These factors or indices help to reduce the search space of the optimization procedure, but they may not always indicate the appropriate placement of capacitors. This paper proposes an efficient approach for the optimal capacitor placement in radial distribution networks with the aim of annual costs minimization based on the sequential placement of capacitors and calculation of power loss index. In the proposed approach, initially, the number of capacitors location is estimated using the total reactive power demand and the average range of capacitors available in the market. Then, the high potential buses can be identified using sequential power loss index-based method. This method leads to achieve the optimal or near optimal locations for the capacitors and decrease the search space of the optimization procedure significantly. The particle Swarm Optimization (PSO) algorithm takes the final decision for the optimum size and location of capacitors. To evaluate the efficiency of the conducted approach, it is tested on several well-known distribution networks, and the results are compared with those of existing methods in the literature. The comparisons verify the effectiveness of the proposed method in producing fast and optimal solutions.
http://joape.uma.ac.ir/article_591_d7319cfcf1dbcf30e053d2a5f11dc3ef.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
117
130
10.22098/joape.2017.2760.1233
Annual costs minimization
Capacitor allocation
Particle swarm optimizarion
Power loss reduction
Sequential power loss index
A.
Bagheri
a.bagheri@ut.ac.ir
true
1
Assistant Professor, Electrical Engineering Department, University of Zanjan, Zanjan, Iran.
Assistant Professor, Electrical Engineering Department, University of Zanjan, Zanjan, Iran.
Assistant Professor, Electrical Engineering Department, University of Zanjan, Zanjan, Iran.
LEAD_AUTHOR
R.
Noroozian
noroozian@znu.ac.ir
true
2
Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
Department of Electrical Engineering, University of Zanjan, Zanjan, Iran
AUTHOR
J.
Gholinezhad
javad.gholinezhad@gmail.com
true
3
West Mazandaran electrical power distribution company, Noshahr, Iran
West Mazandaran electrical power distribution company, Noshahr, Iran
West Mazandaran electrical power distribution company, Noshahr, Iran
AUTHOR
[1] A. A. Sallam, O. P. Malik, “Electric distribution systems”, John Wiley and Sons, New Jersey, 2011.
1
[2] A. A. El-Fergany, A. Almoataz, Y. Abdelaziz, “Capacitor placement for net saving maximization and system stability enhancement in distribution networks using artificial bee colony-based approach”, Electr. Power Energy Syst., vol. 54, pp. 235-243, 2014.
2
[3] R. Sirjani, M. Azah, H. Shareef, “Heuristic optimization techniques to determine optimal capacitor placement and sizing in radial distribution networks: a comprehensive review”, Electr. Rev., vol. 88, no. 7a, pp. 1-7, 2012.
3
[4] K. Prakash, M. Sydulu, “Particle swarm optimization based capacitor placement on radial distribution systems”, IEEE Power Eng. Soc. Gen. Meeting, pp. 1-5, 2007.
4
[5] RS. Rao, SVL. Narasimham, M. Ramalingaraju, “Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm”, Electr. Power Energy Syst., vol. 33, no. 5, pp. 1133-1139, 2011.
5
[6] A. A. El-Fergany, “Optimal capacitor allocations using evolutionary algorithm”, IET Gener. Transm. Distrib., vol. 7, no. 6, pp. 593-601, 2013.
6
[7] Y. Shuiab, M. Kalavathi, C. Rajan, “Optimal capacitor placement in radial distribution system using gravitational search algorithm”, Electr. Power Energy Syst., vol. 64, pp. 384-397, 2015.
7
[8] A.Y. Abdelaziz, E.S. Ali, S.M. Abd Elazim, “Flower pollination algorithm and loss sensitivity factors for optimal sizing and placement of capacitors in radial distribution systems”, Electr. Power Energy Syst., vol. 78, pp. 207-214, 2016.
8
[9] A. A. El-Fergany, A. Y. Abdelaziz, “Cuckoo search-based algorithm for optimal shunt capacitors allocations in distribution networks”, Electr. Power Compon. Syst., vol. 41, no. 16, pp. 1567-1581, 2013.
9
[10] A. A. El-Fergany, A.Y. Abdelaziz, “Capacitor allocations in radial distribution networks using cuckoo search algorithm”, IET Gener. Transm. Distrib., vol. 8, no. 2, pp. 223-232, 2014.
10
[11] M. Sydulu, V. V. K. Reddy, “Index and GA based optimal location and sizing of distribution system capacitors”, IEEE power Eng. Soc. Gen. Meeting, pp. 1-4, 2007.
11
[12] S. K. Injeti, V. K. Thunuguntla, M. Shareef, “Optimal allocation of capacitor banks in radial distribution systems for minimization of real power loss and maximization of network savings using bio-inspired optimization algorithms”, Electr. Power Energy Syst., vol. 69, pp. 441-455, 2015.
12
[13] A. Elmaouhab, M. Boudour, R. Gueddouche, “New evolutionary technique for optimization shunt capacitors in distribution networks”, Electr. Eng., vol. 62, no. 3, pp. 163-167, 2011.
13
[14] S. Sneha, R. Provas Kumar, “Optimal capacitor placement in radial distribution systems using teaching learning based optimization”, Electr. Power Energy Syst., vol. 54, pp. 387-398, 2014.
14
[15] M. R. Raju, K. V. S. R. Murthy, K. R. Avindra, “Direct search algorithm for capacitive compensation in radial distribution systems”, Electr. Power Energy Syst., vol. 42, no. 1, pp. 24-30, 2012.
15
[16] J. H. Teng, “A direct approach for distribution system load flow solutions”, IEEE Trans. Power Delivery, vol. 18, no. 3, pp. 882-887, 2003.
16
[17] J. Kennedy, R. Eberhart, “Particle swarm optimization”, Proc. IEEE Int. Conf. Neural Networks, pp. 1942-1948, 1995.
17
[18] M. Darabian, A. Jalilvand, R. Noroozian, “Combined use of sensitivity analysis and hybrid Wavelet-PSO-ANFIS to improve dynamic performance of DFIG-based wind generation”, J. Oper. Autom. Power Eng., vol. 2, no. 1, pp. 60-73, 2007.
18
[19] H. Shayeghi, A. Ghasemi, “FACTS devices allocation using a novel dedicated improved PSO for optimal operation of power system”, J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 124-135, 2013.
19
[20] E. Babaei, A. Ghorbani, “Combined economic dispatch and reliability in power system by using PSO-SIF algorithm”, J. Oper. Autom. Power Eng., vol. 3, no. 1, pp. 23-33, 2015.
20
[21] M. Chis, MMA. Salama, S. Jayaram, “Capacitor placement in distribution systemusing heuristic search strategies”, IET Gener. Transm. Distrib., vol. 144, no. 3, pp. 225-230, 1997.
21
[22] D. Das, D. P. Kothari, A. Kalam, “Simple and efficient method for load flow solution of radial distribution network”, Electr. Power Energy Syst., vol. 17, no. 5, pp. 335-346, 1995.
22
[23] D. F. Pires, CH. Antunes, A.G. Martins, “NSGA-II with local search for a multi-objective reactive power compensation problem”, Electr. Power Energy Syst., vol. 43, no. 1, pp. 313-324, 2012.
23
ORIGINAL_ARTICLE
Control and Management of Hybrid Renewable Energy Systems: Review and Comparison of Methods
Hybrid renewable energy systems (HRES) have been introduced to overcome intermittent nature of single-source renewable energy generation. In order to utilize HRES optimally, two issues must be considered: optimal sizing and optimal operation. The first issue has been considered vastly in several articles but the second one needs more attention and work. The performance of hybrid renewable energy systems highly depends on how efficient the control of energy production is. In this paper, paradigms and common methods available for control and management of energy in HRES are reviewed and compared with each other. At the end, a number of challenges and future research in relation to HRES are addressed.
http://joape.uma.ac.ir/article_592_c711bf8f03c9ab5ebfbd3e728f45a29a.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
131
138
10.22098/joape.2017.2477.1215
Hybrid energy systems
Control paradigm
Energy management
Renewable energy
M.
Ahangari Hassas
stu.morteza.ahangar@iaut.ac.ir
true
1
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
AUTHOR
K.
Pourhossein
k.pourhossein@gmail.com
true
2
Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
Department of Electrical Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
LEAD_AUTHOR
[1] Luna-Rubio R, Trejo-Perea M, Vargas-Va zquez D, Rios-Moreno GJ, “Optimal sizing of renewable hybrids energy systems: A review of methodologies,” Sol. Energy, vol. 86, no. 4, pp. 1077-88, 2012.
1
[2] Erdinc O, Uzunoglu M, “Optimum design of hybrid renewable energy systems: overview of different approaches,” Renew. Sustain. Energy Rev., vol. 16, no. 3, pp. 1412-26, 2012.
2
[3] Md. Mustafizur Rahman, Md. Mohib-Ul-Haque K, Mohammad Ahsan U, Xiaolei Z, Amit K, “A hybrid renewable energy system for a North American off-grid community,” Energy, vol. 97, no. 1, pp. 151-160, 2016.
3
[4] A. Maleki, F. Pourfayaz, M. A. Rosen, “A novel framework for optimal design of hybrid renewable energy-based autonomous energy systems: A case study for Namin, Iran,” Energy, vol. 98, no. 1, pp. 168-180, 2016.
4
[5] Sunanda Sinha, S.S.Chandel, “Review of software tools for hybrid renewable energy systems,” Renew. Sustain. Energy Rev., vol. 32, no. 1, pp. 192-205, 2014.
5
[6] Bajpai P, Dash V, “Hybrid renewable energy systems for power generation in stand-alone applications: a review,” Renew. Sustain. Energy Rev., vol. 16, no. 5, pp. 2926-36, 2012.
6
[7] S. Upadhyay, M.P. Sharma, “A review on configurations, control and sizing methodologies of hybrid energy systems,” Renew. Sustain. Energy Rev., vol. 38, no. 1, pp. 47- 63, 2014.
7
[8] A. Maleki, M. Gholipour Khajeh, M. Ameri, “Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty,” Int. J. Electr. Power Energy Syst., vol. 83, no. 1, pp. 514-524, 2016
8
[9] S. Bahramara, M. Parsa Moghaddam, M.R. Haghifam, “Optimal planning of hybrid renewable energy systems using HOMER: A review,” Renewable Sustainable Energy Rev., vol. 62, no. 1, pp. 609-620, 2016.
9
[10] H. Khorramdel, B. Khorramdel, M. Tayebi Khorrami, H. Rastegar, “A multi-objective economic load dispatch considering accessibility of wind power with Here-And-Now approach,” J. Oper. Autom. Power Eng., vol. 2, no. 1, pp. 49-59, 2014.
10
[11] K. Afshar, A. Shokri Gazafroudi, “Application of stochastic programming to determine operating reserves with considering wind and load uncertainties,” J. Oper. Autom. Power Eng., vol. 1, no. 2, pp. 96–109, 2013.
11
[12] I. Janghorban Esfahani, P. Ifaei, J. Kim, C. Kyoo Yoo, “Design of hybrid renewable energy systems with battery/hydrogen storage considering practical power losses: A MEPoPA (Modified Extended-Power Pinch Analysis),” Energy, vol. 100, no. 1, pp. 40-50, 2016.
12
[13] Nehrir MH, Wang C, Strunz K, Aki H, Ramakumar R, Bing J, et al., “A review of hybrid renewable/alternative energy systems for electric power generation: configurations, control and applications,” IEEE Trans. Sustainable Energy, vol. 2, no. 4, pp. 392-403, 2011.
13
[14] D. AL, H. ND, “Operation of a multiagent system for microgrid control,” IEEE Trans. Power Syst., vol. 20, no. 3, pp. 1447-55, 2005.
14
[15] A. Chauhan, R.P.Saini. “A review on integrated renewable energy system based power generation for stand-alone applications: configurations, storage options, sizing methodologies and control,” Renewable Sustainable Energy Rev., vol. 38, no. 1, pp. 99-120, 2014.
15
[16] F. Valenciaga, P. F. Puleston, “Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy,” IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 398-405, 2005.
16
[17] C. Wang and M. H. Nehrir, “Power management of a stand-alone wind/ photovoltaic/fuel-cell energy system,” IEEE Trans. Energy Convers., vol. 23, no. 3, pp. 957-967, 2008.
17
[18] A. M Azmy and I. Erlich, “Online optimal management of PEM fuel cells using neural networks,” IEEE Trans. Power Deliv., vol. 29, no. 2, pp. 1051-1058, 2005.
18
[19] J. Lagorse, M. Simoes, G. Miraoui, Abdellatif, “A multiagent fuzzy-logic-based energy management of hybrid systems,” IEEE Trans. Ind. Appl., vol. 45, no. 6, pp. 2123-2129, 2009.
19
[20] M.A. Abido, “Environmental/economic power dispatch using multiobjective evolutionary algorithms,” IEEE Trans. Power Syst., vol. 18, no. 4, pp. 1529-1537, 2003.
20
[21] H. Ko and J. Jatskevich, “Power quality control of wind-hybrid power generation system using fuzzy-LQR controller,” IEEE Trans. Energy Convers., vol. 22, no. 2, pp. 516-527, 2007.
21
[22] A. Hajizadeh and M. A. Golkar, “Fuzzy neural control of a hybrid fuel cell/battery distributed power generation system,” IET Renewable Power Gener., vol. 3, no. 4, pp. 402-414, 2009.
22
[23] Z. Jiang, R. Dougal, “Hierarchical microgrid paradigm for integration of distributed energy resources,” Proce. IEEE Power Eng. Soc. Gen. Meeting, pp. 1-8, 2008.
23
[24] R. Chedid, S. Rahman, “Unit sizing and control of hybrid wind-solar power systems,” IEEE Trans. Energy Convers., vol. 12, no. 1, pp. 79-85, 1997.
24
[25] N. Bizon,” Load-following mode control of a standalone renewable/fuel cell hybrid power source,” Energy Convers. Manag., vol. 77, no. 1, pp. 763-72, 2014.
25
[26] M. Ashari, C. V. Nayar,” An optimum dispatch strategy using set points for a photovoltaic (PV)-diesel-battery hybrid power system,” Sol. Energy, vol. 66, no. 1, pp. 1-9, 1999.
26
[27] O. C Onar, M. Uzunoglu, M. S. Alam, “Modeling, control and simulation of an autonomous wind turbine/ photovoltaic/fuel cell/ultra-capacitor hybrid power system,” J. Power Sources, vol. 185, no. 2, pp. 1273-83, 2008.
27
[28] T. F. El-Shater, M. Eskander, M. El-Hagry M “Hybrid PV/fuel cell system design and simulation,” Renewable Energy, vol. 27, no. 3, pp. 479-85, 2002.
28
[29] J. P. Torreglosa, P. García, L. M. Fernández, F. Jurado “Hierarchical energy management system for stand-alone hybrid system based on generation costs and cascade control,” Energy Convers. Manag., vol. 77, no. 1, pp. 514-26, 2014.
29
[30] M. Uzunoglu, O. C. Onar, M. S. Alam. Modelling, “Control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications,” Renew. Energy, vol. 34, no. 3, pp. 509-20, 2009.
30
[31] P. Thounthong, A. Luksanasakul, P. Koseeyaporn, B. Davat, “Intelligent model based control of a standalone photovoltaic/fuel cell power plant with super-capacitor energy storage,” IEEE Trans. Sustainable Energy, vol. 4, no. 1, pp. 240-9, 2013.
31
[32] T. Senjyu, D. Hayashi, N. Urasaki, T. Funabashi “Optimum configuration for renewable generating systems in residence using genetic algorithm,” IEEE Trans. Energy Convers., vol. 21, no. 1, pp. 459-67, 2006.
32
[33] S. G. Malla, C. N. Bhende “Voltage control of stand-alone wind and solar energy system,” Electr. Power Energy Syst., vol. 56, no. 1, pp. 361-73, 2014.
33
[34] D.C. Das, A.K. Roy, N. Sinha “GA based frequency controller for solar thermal diesel-wind hybrid energy generation/energy storage system,” Electr. Power Energy Syst., vol. 43, no. 1, pp. 262-79, 2012.
34
[35] Kang KH, Won DJ. “Power management strategy of stand-alone hybrid system to reduce the operation mode changes,” Proce. Trans. Distrib. Conf. Expos., pp. 1-4, 2009.
35
[36] O. C. Onar, Uzunoglu M, Alam MS. “Dynamic modeling, design and simulation of a wind/fuel cell/ultra-capacitor-based hybrid power generation system,” J. Power Sources, vol. 161, no. 1, pp. 707-22, 2006.
36
[37] Chauhan A, Khatod DK. “Modeling and simulation of self-excited induction generator (SEIG) with electronic load controller (ELC) reference to a stand-alone micro hydro-power plant,” Proce. Int. Conf. Adv. Renew. Energy, pp.1-6, 2010.
37
[38] D. Ipsakis, S. Voutetakis, Seferlis P, Stergiopoulos F, Elmasides C, “Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage,” Int. J. Hydrog. Energy, vol. 34, no. 16, pp. 7081-95, 2009.
38
[39] J. S. Park, T. Katagi, Yamamoto S, Hashimoto T, “Operation control of photovoltaic/diesel hybrid generating system considering fluctuation of solar radiation,” Sol. Energy Mater. Solar Cells, vol. 67, no. 1-4, pp. 535-42, 2001.
39
[40] B. Nicu, O. Mihai, R. Mircea, “Efficient energy control strategies for a standalone renewable/Fuel Cell hybrid power source,” Energy Convers. Manag., vol. 90, no. 1, pp. 93-110, 2015.
40
[41] A. Tareq, D. Said, M. Driss, L. Chrifi-Alaoui, B. Rafik, H. Aziz, “Dynamic control and advanced load management of a stand-alone hybrid renewable power system for remote housing,” Energy Convers. Manag., vol. 105, no. 1, pp. 377-392, 2015.
41
[42] P.G. Arul, Vigna K. Ramachandaramurthy, R.K. Rajkumar, “Control strategies for a hybrid renewable energy system: A review,” Renew. Sustain. Energy Rev., vol. 42, no. 1, pp. 597-608, 2015.
42
[43] V. Dash, P. Bajpai, “Power management control strategy for a stand-alone solar photovoltaic-fuel cell-battery hybrid system,” Sustain. Energy Technol. Assess., vol. 9, no. 1, pp. 68-80, 2015.
43
[44] S. Nasri, B. Sami, A. Cherif, “Power management strategy for hybrid autonomous power system using hydrogen storage,” Int. J. Hydrog. Energy, vol. 41, no. 2, pp. 857-65, 2016.
44
[45] G. Bruni, S. Cordiner, V. Mulone, V. Rocco, F. Spagnolo, “A study on the energy management in domestic micro-grids based on model predictive control strategies,” Energy Convers. Manag., vol. 102, no. 1, pp. 50-58, 2015.
45
[46] M. Basir Khan, R. Jidin, J. Pasupuleti, “Multi-agent based distributed control architecture for microgrid energy management and optimization,” Energy Convers. Manag., vol. 112, no. 1, pp. 288-307, 2016.
46
[47] A. Brka, G. Kothapalli, Y. Al-Abdeli, “Predictive power management strategies for stand-alone hydrogen systems: Lab-scale validation,” Int. J. Hydrog. Energy, vol. 40, no. 32, pp. 9907-16, 2015.
47
[48] S. Upadhyay, M. Sharma, “Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India,” Energy, vol. 94, no. 1, pp. 352-66, 2016.
48
[49] V. Khare, S. Nema, P. Baredar, “Solar-wind hybrid renewable energy system: A review,” Renewable Sustain. Energy Rev., vol. 58, no. 1, pp. 23-33, 2016.
49
[50] H. M. Kelash, H. M. Faheem, and M. Amoon, “It takes a multiagent system to manage distributed systems,” IEEE Potentials, vol. 26, no. 2, pp. 39-45, 2007.
50
[51] K. Huang, D. A. Cartes, and S. K. Srivastava, “A multiagent-based algorithm for ring-structured shipboard power system reconfiguration,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 37, no. 5, pp. 1016-1021, 2007.
51
[52] K. Abdoul, L. Mamadou, A. Papa, “Decentralized control of the hybrid electrical system consumption: A multi-agent approach,” Renew. Sustain. Energy Rev., vol. 59, no. 1, pp. 972-978, 2016.
52
[53] M. Rastegar, M. Fotuhi-Firuzabad, “Load management in a residential energy hub with renewable distributed energy resources,” Energy Build., vol. 107, no. 1, pp. 234-242, 2015.
53
[54] H. Kamankesh, G. Vassilios, A. Kavousi-Fard, “Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand,” Energy, vol. 100, no. 1, pp. 285-297, 2016.
54
[55] R. Hemmati, H. Saboori, “Emergence of hybrid energy storage systems in renewable energy and transport applications-A review,” Renew. Sustain. Energy Rev., vol. 65, no. 1, pp. 11-23, 2016.
55
[56] A. Askarzadeh, L. dos Santos Coelho, “A novel framework for optimization of a grid independent hybrid renewable energy system: A case study of Iran,” Sol. Energy, vol. 112, no. 1, pp. 383-396, 2015.
56
[57] N. Ahmed, M. Miyatake, A. Al-Othman,” Power fluctuations suppression of stand-alone hybrid generation combining solar photovoltaic/wind turbine and fuel cell systems,” Energ Convers. Manage., vol. 49, no. 10, pp. 2711-2719, 2008.
57
[58] T. El-Shatter, M. Eskander, M. El-Hagry, “Energy flow and management of a hybrid wind/PV/fuel cell generation system,” Energy Convers. Manage., vol. 47, no. 10, pp. 1264-80, 2006.
58
[59] K. Shivarama Krishna, K. Sathish Kumar,” A review on hybrid renewable energy systems,” Renew. Sustain. Energy Rev., vol. 52, no. 1, pp. 907-916, 2015.
59
[60] R. Cozzolino, L. Tribioli, G. Bella,” Power management of a hybrid renewable system for artificial islands: A case study,” Energy, vol. 106, no. 1, pp. 774-789, 2016.
60
[61] L. Olatomiwa, S. Mekhilef, M.S. Ismail, M. Moghavvemi,” Energy management strategies in hybrid renewable energy systems: A review,” Renew. Sustain. Energy Rev., vol. 62, no. 1, pp. 821-835, 2016.
61
ORIGINAL_ARTICLE
Demand Response Based Model for Optimal Decision Making for Distribution Networks
In this paper, a heuristic mathematical model for optimal decision-making of a Distribution Company (DisCo) is proposed that employs demand response (DR) programs in order to participate in a day-ahead market, taking into account elastic and inelastic load models. The proposed model is an extended responsive load modeling that is based on price elasticity and customers’ incentives in which they participate in demand response program, voluntarily and would be paid according to their declared load curtailment amounts. It is supposed that DisCo has the ability to trade with the wholesale market and it can also use its own distributed generation (DG), while decision making process. In this regard, at first, DisCo’s optimization frameworks in two cases, with and without elastic load modelings are acquired. Subsequently, utilizing Hessian matrix and mathematical optimality conditions, optimal aggregated load curtailment amounts are obtained and accordingly, individual customer’s load reductions are calculated. Furthermore, effects of DG contributions and wholesale electricity market are investigated. An IEEE 18 bus test system is employed to obtain the results and show the accuracy of the proposed model.
http://joape.uma.ac.ir/article_593_9b15c68ed4dc89bc3fe626e9cea67a3f.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
139
149
10.22098/joape.2017.2475.1214
Decision making
Distribution Company
Demand response
Load elasticity
Distributed generation
M.
Khafri
majid.khafri@srttu.edu
true
1
Shahid Rajaee University
Shahid Rajaee University
Shahid Rajaee University
AUTHOR
A.
Badri
a_badri73@yahoo.com
true
2
Shahid Rajaee University
Shahid Rajaee University
Shahid Rajaee University
LEAD_AUTHOR
A. A.
Birjandi
motiebirjandi@srttu.edu
true
3
Shahid Rajaee University
Shahid Rajaee University
Shahid Rajaee University
AUTHOR
[1] S. Yousefi, M. P. Moghaddam and V. JohariMajd “Optimal real time pricing in an agent-based retail market using a comprehensive demand response model,” Energy, vol. 36, no. 9, pp. 16-27, 2011
1
[2] H.A. Aalami, M.P. Moghaddam and G.R. Yousefi “Modeling and prioritizing demand response programs in power markets,” Electr. Power Syst. Res., vol. 80, no. 4, pp.426-435, 2011.
2
[3] M. Kazemi, A. Zangeneh and A. Badri “Prioritization of demand response programs in electricity power markets using TOPSIS,” Proc. Smart Grid Conf., pp. 327-333, 2013.
3
[4] H. Arasteh M.P. Moghaddam M, Sheikh El Eslami, and et al “Integrating commercial demand response resources with unit commitment,” Electr. Power Energy Syst., vol. 51, no. 1, pp. 153-161, 2013.
4
[5] A. Abdollahi, M.P. Moghaddam, M, Rashidinejad and et al “Investigation of economic & environmental driven demand response measures incorporating UC,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 12–25, 2012.
5
[6] C. S. Kirschen and D. Quantifying “The effect of demand response on electricity markets,” IEEE Trans. Power Syst., vol. 24, no. 1, pp. 199-207, 2009.
6
[7] H.A.Aalami, M.P. Moghaddam and G.R.Yousefi “Demand response modeling considering interruptible /curtailable loads and capacity market programs,” Appl. Energy, vol. 87, no. 1, pp. 243-250, 2010.
7
[8] D. Nguen and H. Nguen and L. Le “Dynamic pricing design for demand response integration in power distribution networks ,” IEEE Trans. Power Syst., vol. 31, no. 5, pp. 3457-3472, 2016.
8
[9] F Meng and X. Zend , “A profit maximization approach to demand response management with customers behavior learning in smart grid,” IEEE Trans. Power Syst., vol. 7, no. 3, pp. 1516-1529, 2016.
9
[10] M. R. Sahebi, E. AbediniDuki, M. Kia, A. Soroudi and M. Ehsan, “Simultanous EDRP and unit commitment programming in comparison with interruptible load contracts”, IET Gener. Trans. Distrib., vol.6, no.7, pp. 605–611, 2012.
10
[11] H. Aalami, M. P. Moghadam, and G. R. Yousefi, “Determination of optimal demand response incentives using DR programs”, Proc. 22nd Int. Power Syst. Conf., pp. 132-136, 2007.
11
[12] N. Zareen, M. W. Mustafa, U. Sultana and etal, “Optimal real time cost benefit based demand response with intermittent resources”, Energy, vol. 90, no.2, pp.1695-1706, 2015.
12
[13] Y. Wang, and M. Li, Lin, “Time of use based electricity demand response for sustainable manufacturing systems”, Energy, vol. 63, no. 15, pp.233-244, 2013.
13
[14] M. Sarker, M. Vazquez and D.S. Kirschen “Optimal coordination and scheduling of demand response via monetary incentives,” IEEE Trans. Power Syst., vol. 6, no. 5, pp. 1341-1352, 2015.
14
[15] A. Badri, and K. Hosseinpour, “A stochastic multi period decision making framework of an electricity retailer considering aggregated optimal charging and discharging of electric vehicles”, J. Autom. Oper. Power Eng., vol. 3, no. 1, pp. 34-46, 2015.
15
[16] E. Bompard, R. Napoli, and B. Wan, “The effect of programs for demand response incentives in competitive electricity markets”, Eur. Trans. Electr. Power, vol. 19, no. 1, pp.127-139, 2009.
16
[17] N. Ruiz, B. Claessens, J. Jimeno, and etal, “Residential load forecasting under a demand response program based on economic incentives,” Int. Trans. Electr. Energy Syst., vol. 25, no. 8, pp.1436-1451, 2015.
17
[18] H. Arasteh, M. Sepasian, and V. Vahidinasab, “Toward a smart distribution system expansion planning by considering demand response resources”, J. Autom. Oper. Power Eng., vol. 3, no. 2, pp. 116-130, 2015.
18
[19] M.Peik-Herfeh, H. Seifi and M.K. Sheikh-El-Eslami, “Decision making of a virtual power plant under uncertainties for bidding in a day-ahead market using point estimate method”, Electr. Power Energy Syst., vol. 44, no. 1, pp. 88-98, 2013.
19
[20] H. Kwag and J. Kim, “Optimal combined scheduling of generation and demand response with demand resource constraints”, Appl. Energy, vol. 96, no. 2, pp.161-170, 2012.
20
[21] H. Aalami, G. R. Yousefi and M. P. Moghadam, “Demand response model considering EDRP and TOU programs”, Proc. IEEE/PES Transm. Distrib. Conf. Exhibition, 2008.
21
[22] F. C. Schweppe, M. C. Caramanis, R. D. Tabors and R. E. Bohn, “Spot Pricing of Electricity”, Boston, MA: Kluwer Academic Publishers, 1998.
22
ORIGINAL_ARTICLE
Distributed multi-agent Load Frequency Control for a Large-scale Power System Optimized by Grey Wolf Optimizer
This paper aims to design an optimal distributed multi-agent controller for load frequency control and optimal power flow purposes. The controller parameters are optimized using Grey Wolf Optimization (GWO) algorithm. The designed optimal distributed controller is employed for load frequency control in the IEEE 30-bus test system with six generators. The controller of each generator is considered as one agent. The controllers of agents are implemented in a distributed manner that is control rule of each agent depends on the agents’ own state and the states of their neighbors. Three other types of controllers including centralized controller, decentralized controller, and optimal centralized controller are considered for comparison. The performances of decentralized and distributed controllers are compared with two centralized controllers. In the optimal centralized controller and optimal distributed controller, the objective function is considered to achieve the objective of load frequency control as well as minimize power generation. Simulation results using MATLAB/SIMULINK show that although there is no global information of system in the optimal distributed controller, it has suitably reduced the frequency deviation. Meanwhile the power is optimally generated in the three scenarios of load increasing, load reduction and generator outage.
http://joape.uma.ac.ir/article_594_b030131136992a46bc18e989a5c4d1f0.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
151
162
10.22098/joape.2017.2522.1220
Load frequency control (LFC)
Distributed controller
Optimal power flow (OPF)
Grey wolf optimizer (GWO)
Multi-agent systems
A.
Akbarimajd
adelakbary@yahoo.com
true
1
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
LEAD_AUTHOR
M.
Olyaee
mohsenolyai@gmail.com
true
2
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
AUTHOR
H.
Shayeghi
hshayeghi@gmail.com
true
3
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
AUTHOR
B.
Sobhani
behrooz.sobhani@yahoo.com
true
4
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
AUTHOR
[1] G. Gross, and J. W. Lee “Analysis of load frequency control performance assessment criteria,” IEEE Trans. Power Syst., vol. 16, no. 3, pp. 520-525, 2001.
1
[2] S. Satyanarayana, R. K. Sharma, A. Mukta, and S. A. Kumar, “Automatic Generation control in power plant using PID, PSS and fuzzy-PID controller,” Smart Electr. Grid, pp. 1-8, 2014.
2
[3] O. Abedinia, N. Amjady, A. Ghasemi, and H. Shayeghi “Multi-stage fuzzy load frequency control based on multi-objective harmony search algorithm in deregulated environment,” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 63-73, 2013.
3
[4] M. Andreasson, H. Sandberg, D. Dimarogonas, and K. Johansson, “Distributed Integral Action: Stability Analysis and Frequency Control of Power Systems,” 51st IEEE Conf. Decis. Control, pp. 2077- 2083, 2012.
4
[5] E. Planas, A. Gil-de-Muro, J. Andreu, I. Kortabarria, and I. Martinez de Alegria, “General aspects, hierarchical controls and droop methods in micro grids: a review,” Renew. Sustain. Energy Rev., pp. 147-159, 2013.
5
[6] A. Del Barrio, S. Memik, M. Molina, J. Mendias, and R. Hermida, “A Distributed controller for Managing Speculative Functional Units in High Level Synthesis,” IEEE Trans.-Aided Des. Integr. Circuits Syst., vol. 30, pp. 350-363, 2011.
6
[7] F. Katiraei, M. Iravani, and P. Lehn “Micro-grid autonomous operation during and subsequent to islanding process,” IEEE Trans. Power Delivery, vol. 20, no. 1, pp. 248-257, 2005.
7
[8] F. Liu, Y. H. Song, J. Ma, S. Mei, and Q. Lu, “Optimal load-frequency control in restructured power systems. Generation,” IEE Proc.-Gener., Transm. Distrib., vol. 150, no. 1, pp. 87-95, 2003.
8
[9] J. Machowski, J. W. Bialek, and J. R. Bumby “Power system dynamics: stability and control,” Wiley, 2008.
9
[10] N. Senroy, G. T. Heydt, and V. Vittal “Decision tree assisted controlled islanding,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1790-1797, 2006.
10
[11] B. Yang, V. Vittal, and G.T Heydt “Slow-coherency-based controlled islanding: A demonstration of the approach on the august 14, 2003 blackout scenario,” IEEE Trans. Power Syst., vol. 21, no. 4, pp. 1840-1847, 2006.
11
[12] Momoh JA, Zhu JZ “Improved interior point method for OPF problems,” IEEE Trans. Power Syst., vol. 14, no. 3, pp. 1114-1120, 1999.
12
[13] K. Abaci and V. Yamacli “Differential search algorithm for solving multi-objective optimal power flow problem,” Int. J. Electr. Power Energy Sys., vol. 79, pp. 1-10, 2016.
13
[14] S. Surender Reddy and C. Srinivasa Rathnam “Optimal Power Flow using Glowworm Swarm Optimization,” Int. J. Electr. Power Energy Syst., vol. 80, pp. 128-139, 2016.
14
[15] S. Derafshi Beigvand, and H. Abdi “Optimal power flow in the smart grid using direct load control program,” J. Oper. Autom. Power Eng., vol. 3, no. 2, pp. 102-115, 2015.
15
[16] R. Sahu, S. Panda, and U. Rout, “DE optimized parallel 2-DOF PID controller for load frequency control of power system with governor dead-band nonlinearity,” Int. J. Electr. Power Energy Syst., vol. 49, pp. 19-33, 2013.
16
[17] S. Mirjalili, S. Mirjalili, and A. Lewis “Grey Wolf Optimizer,” Adv. Eng. Software, vol. 69, pp. 46-61, 2014.
17
[18] Y. Sharma, and L. Saikia, “Automatic generation control of a multi-area ST-Thermal power system using Grey Wolf Optimizer algorithm based classical controllers,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 853-862, 2015.
18
[19] M. Andreasson, D. Dimarogonas, H. Sandberg, and K. Johansson “Distributed Control of Networked Dynamical Systems: Static Feedback, Integral Action and Consensus,” IEEE Trans. Autom. Control, vol. 59, no. 7, pp. 1750-1764, 2014.
19
[20] M. Andreasson, “Control of Multi-Agent Systems with Applications to Distributed Frequency Control Power Systems,” [Thesis]. Stockholm: KTH R. Inst. Technol., 2013.
20
ORIGINAL_ARTICLE
Evaluation of Peak Shifting and Energy Saving Potential of Ice Storage Based Air Conditioning Systems in Iran
Thermal energy storage (TES) system has been introduced as a practical facility for shifting load from peak hours to off-peak hours. Because of different energy consumption during day and night, peak and off peak period is created on load curve. Ice storage technology which is a kind of TES system, is implemented in different points of the world with the purpose of solving load shifting problem. The basic process of this technology is storing energy in the ice during off-peak hours, utilizing an air conditioning unit in which the stored energy will be utilized during day. Utilization of ice storage system is a good solution for optimizing consumption of gas and electrical energy, which will be effective in urban pollution reduction. This paper aims to introduce load shifting problem and the implemented procedures to overcome this problem from the past, analyzing ice storage system as a solution to this problem. Moreover, feasibility of the ice storage technology on a case study in Iran is discussed to show the performance and efficiency of the technology. The obtained results for the case study show that by utilizing ice storage system the consumption and the paid cost will be reduced with respect to conventional system.
http://joape.uma.ac.ir/article_601_f7b4ea4b9adb3448ecde9d66a167427a.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
163
170
10.22098/joape.2017.2743.1231
Load shifting
thermal energy storage
ice storage system
air conditioning unit
B.
Mohammadi ivatloo
ivatloo@gmail.com
true
1
University of Tabriz
University of Tabriz
University of Tabriz
LEAD_AUTHOR
M.
Nazari-Heris
mnazari94@ms.tabrizu.ac.ir
true
2
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
F.
Kalavani
f.kalavani93@ms.tabrizu.ac.ir
true
3
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
[1] S. Sorrell, "Reducing energy demand: A review of issues, challenges and approaches," Renewable Sustainable Energy Rev., vol. 47, pp. 74-82, 2015.
1
[2] A. Allouhi, Y. El Fouih, T. Kousksou, A. Jamil, Y. Zeraouli, and Y. Mourad, "Energy consumption and efficiency in buildings: current status and future trends," J. Cleaner Prod., vol. 109, pp. 118-130, 2015.
2
[3] Y. Sun, S. Wang, F. Xiao, and D. Gao, "Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: a review," Energy Convers. Manage., vol. 71, pp. 101-114, 2013.
3
[4] S. B. Sadineni and R. F. Boehm, "Measurements and simulations for peak electrical load reduction in cooling dominated climate," Energy, vol. 37, pp. 689-697, 2012.
4
[5] J. E. Braun, "Load control using building thermal mass," J. Solar Energy Eng., vol. 125, pp. 292-301, 2003.
5
[6] E. Dehnavi, H. Abdi, and F. Mohammadi, "Optimal emergency demand response program integrated with multi-objective dynamic economic emission dispatch problem," J. Oper. Autom. Power Eng., vol. 4, pp. 29-41, 2016.
6
[7] S. Derafshi Beigvand and H. Abdi, "Optimal Power Flow in the Smart Grid Using Direct Load Control Program," J. Oper. Autom. Power Eng., vol. 3, pp. 102-115, 2015.
7
[8] D. Zhou, C.-Y. Zhao, and Y. Tian, "Review on thermal energy storage with phase change materials (PCMs) in building applications," Appl. energy, vol. 92, pp. 593-605, 2012.
8
[9] I. Dincer, "On thermal energy storage systems and applications in buildings," Energy Build., vol. 34, pp. 377-388, 2002.
9
[10] S. Hasnain, "Review on sustainable thermal energy storage technologies, Part II: cool thermal storage," Energy Convers. Manage., vol. 39, pp. 1139-1153, 1998.
10
[11] G. Li, Y. Hwang, and R. Radermacher, "Review of cold storage materials for air conditioning application," Int. J. Refrig., vol. 35, pp. 2053-2077, 2012.
11
[12] J. E. Seem, "Adaptive demand limiting control using load shedding," HVAC&R Res., vol. 1, pp. 21-34, 1995.
12
[13] Y. Zhang, G. Zhou, K. Lin, Q. Zhang, and H. Di, "Application of latent heat thermal energy storage in buildings: State-of-the-art and outlook," Build. Environ., vol. 42, pp. 2197-2209, 2007.
13
[14] J. Zhao and N. Liu, "Exergy Analysis of Ice Storage Air-Condition System Operating Strategy," Proce. Int. Conf. Comput. Distrib. Control Intell. Environ. Monit., 2011.
14
[15] M. Zhang and Y. Gu, "Optimization of ice-storage air conditioning system With ASAGA," IEEE Workshop Adv. Res. Technol. Ind. Appl., 2014, pp. 1042-1046.
15
[16] H. Dasi, F. Xiaowei, and C. Wenjian, "A near-optimal operation strategy for ice storage air-conditioning systems," Proce. 3rd IEEE Conf. Ind. Electron. Appl., 2008, pp. 1287-1290.
16
[17] D. Arnold, "Dynamic Simulation of Encapsulated Ice Stores--Part 2: Model Development and Validation," ASHRAE Trans.-Am. Soc. Heating Refrig. Airconditioning Engin, vol. 100, pp. 1245-1256, 1994.
17
[18] C. Xueqing, C. Ying, and S. Yongkang, "Performance Enhancement Study of R410A Direct Evaporation Ice-Storage System Using Divided Storage Tank," Proce. Int. Conf. Comput. Distrib. Control Intell. Environ. Monit., 2011, pp. 1607-1610.
18
[19] M. Murphy, M. O’Mahony, and J. Upton, "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Appl. Energy, vol. 149, pp. 392-403, 2015.
19
[20] M. Navidbakhsh, A. Shirazi, and S. Sanaye, "Four E analysis and multi-objective optimization of an ice storage system incorporating PCM as the partial cold storage for air-conditioning applications," Appl. Therm. Eng., vol. 58, pp. 30-41, 2013.
20
[21] A. Shirazi, B. Najafi, M. Aminyavari, F. Rinaldi, and R. A. Taylor, "Thermal-economic-environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling," Energy, vol. 69, pp. 212-226, 2014.
21
[22] G. P. Henze, C. Felsmann, and G. Knabe, "Evaluation of optimal control for active and passive building thermal storage," Int. J. Therm. Sci., vol. 43, pp. 173-183, 2004.
22
[23] A. D. Giorgio, L. Pimpinella, and F. Liberati, "A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit," Proce. 20th Mediterr. Conf. Control Autom., 2012, 2012, pp. 1491-1498.
23
[24] W. Xu, M. Zhou, H. Wang, and H. Liu, "A load management optimization approach considering economic efficiency and load profile," Proce. China Int. Conf.Electricity Distrib., , 2014, pp. 907-911.
24
[25] M. M. Farid, A. M. Khudhair, S. A. K. Razack, and S. Al-Hallaj, "A review on phase change energy storage: materials and applications," Energy convers. manage., vol. 45, pp. 1597-1615, 2004.
25
[26] C. Chen, M. Kang, J. Hwang, and C. Huang, "Application of binary integer programming for load transfer of distribution systems," Proce. Int. Conf. Power Syst. Technol., 2000, pp. 305-310.
26
[27] A. M. Khudhair and M. M. Farid, "A review on energy conservation in building applications with thermal storage by latent heat using phase change materials," Energy Convers. Manage., vol. 45, pp. 263-275, 2004.
27
ORIGINAL_ARTICLE
A New Method of Distribution Marginal Price Calculation in Distribution Networks by Considering the Effect of Distributed Generations Location on Network Loss
The determination of practical and coherent policy to pin down the price in restructured distribution networks should be considered as a momentous topic. The present paper introduces a new method of distribution marginal price (DMP) calculation. The main aim of this paper is to evaluate the DMP for both producers and consumers separately. For this purpose, the first part of the procedure emphasizes a price by which the producers should sell their power. To meet this target, the share of each node plays a significant role in the total active loss of the network. The producers will make a substantial profit when their efficiency leads to decreasing the share of the node that is associated with the total loss. In the second part of the procedure, DMP is computed for the consumers. In this part, based on the distribution system operator’s decision about the obtained profit allocated to the consumers, their payment has been reduced. This method has been applied to the 33-Bus Distribution System. The results demonstrate the characteristic of the method which tends to encourage the distributed units to increase their output powers. This is the reason why the penetration of these units in the networks is an opportunity for consumers from an economic aspect in such a way that merchandising surplus (MS) becomes zero.
http://joape.uma.ac.ir/article_595_8538d332ece2606423115c8656af9e8a.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
171
180
10.22098/joape.2017.2169.1201
Distribution Marginal Price (DMP)
Distribution network
Power active loss
Distributed generation (DG)
Consumer
S.
Ghaemi
s.ghaemi93@ms.tabrizu.ac.ir
true
1
Department of Electrical and Computer Engineering, University of Tabriz
Department of Electrical and Computer Engineering, University of Tabriz
Department of Electrical and Computer Engineering, University of Tabriz
LEAD_AUTHOR
K.
Zare
true
2
Department of Electrical and Computer Engineering, University of Tabriz
Department of Electrical and Computer Engineering, University of Tabriz
Department of Electrical and Computer Engineering, University of Tabriz
AUTHOR
[1] S. G. Naik, D. Khatod and M. Sharma, “Optimal allocation of combined DG and capacitor for real power loss minimization in distribution networks,” Int. J. Electr. Power Energy Syst., vol. 53, pp. 967-973, 2014.
1
[2] E. N. Silva, A. B. Rodrigues and M. D. G. da Silva, “Stochastic assessment of the impact of photovoltaic distributed generation on the power quality indices of distribution networks,” Electr. Power Syst. Res., vol. 135, pp. 59-67, 2016.
2
[3] N. Mohandas, R. Balamurugan and L. Lakshmina-rasimman, “Optimal location and sizing of real power DG units to improve the voltage stability in the distribution system using ABC algorithm united with chaos,” Int. J. Electr. Power Energy Syst., vol. 66, pp.41-52, 2015.
3
[4] B. Mohammadi-Ivatloo, A. Mokari, H. Seyedi, and S. Ghasemzadeh “An improved under-frequency load shedding scheme in distribution networks with distributed generation,” J. Oper. Autom. Power Eng., vol. 2, no. 1, pp. 22-31, 2014.
4
[5] M. Al-Muhaini, G. T. Heydt, “Evaluating future power distribution system reliability including distributed generation,” IEEE Trans. Power Delivery, vol. 28, no. 4, pp. 2264-2272, 2013.
5
[6] G. Naik, S. Naik, D. K. Khatod, and M. P. Sharma, “Analytical approach for optimal siting and sizing of distributed generation in radial distribution networks,” IET Gener. Transm. Distrib., vol. 9, no. 3, pp. 209-220, 2015.
6
[7] F. S. Abu-Mouti, M. E. El-Hawary, “Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm,” IEEE Trans. Power Del. vol. 26, no. 4, pp. 2090-2101, 2011.
7
[8] D.Q. Hung, N. Mithulananthan, “Multiple distributed generator placement in primary distribution networks for loss reduction,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1700-1708, 2013.
8
[9] M. Esmaili, “Placement of minimum distributed generation units observing power losses and voltage stability with network constraints,” IET Gener. Transm. Distrib., vol. 7, no. 8, pp. 813-821, 2013.
9
[10] S. Ray, A. Bhattacharya and S. Bhattacharjee, “Optimal allocation of distributed generation and remote control switches for reliability enhancement of a radial distribution system using oppositional differential search algorithm,” IET J. Eng., vol. 1, no. 1, 2015.
10
[11] L. Wang, C. Singh, “Reliability-constrained optimum placement of reclosers and distributed generators in distribution networks using an ant colony system algorithm,” IEEE Trans. Syst., Man, Cybern., Part C (Appl. Rev.), vol. 38, no. 6, pp. 757-764, 2008.
11
[12] L. F. Ochoa, G. P. Harrison, “Minimizing energy losses: Optimal accommodation and smart operation of renewable distributed generation,” IEEE Trans. Power Syst., vol. 26, no. 1, pp. 198-205, 2011.
12
[13] H. Shayegi, M. Alilou “Application of multi objective HFAPSO algorithm for simultaneous placement of DG, capacitor and protective device in radial distribution network,” J. Oper. Autom. Power Eng., vol. 3, no. 2, pp. 131-146, 2015.
13
[14] P. M. Sotkiewicz, J. M. Vignolo, “Nodal pricing for distribution networks: efficient pricing for efficiency enhancing DG,” IEEE Trans. Power Syst., vol. 21, no. 2, pp. 1013, 2006.
14
[15] F. Li, R. Bo, “Congestion and price prediction under load variation,” IEEE Trans. Power Syst., vol. 24, no. 2, pp. 911-922, 2009.
15
[16] J. C. Peng, H. Jiang, G. Xu, A. Luo and C. Huang, “Independent marginal losses with application to locational marginal price calculation,” IET Gener. Transm. Distrib., vol. 3, no. 7, pp. 679-689, 2009.
16
[17] J. B. Cardell, “Marginal loss pricing for hours with transmission congestion,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1466–1474, 2007.
17
[18] Z. Hu, H. Cheng, Z. Yan and F. Li, “An iterative LMP calculation method considering loss distributions,” IEEE Trans. Power Syst., vol. 25, no. 3, pp. 1469-1477, 2010.
18
[19] E. Litvinov, “Design and operation of the locational marginal prices-based electricity markets”, IET J. Eng., vol. 4, no. 2, pp. 315-323, 2010.
19
[20] Y. P. Molina, R. B. Prada and O. R. Saavedra, “Complex losses allocation to generators and loads based on circuit theory and Aumann-Shapley method,” IEEE Trans. Power Syst., vol. 25, no. 4, pp. 1928-1936, 2010.
20
[21] J. S. Savier, D. Das, “Energy loss allocation in radial distribution systems: A comparison of practical algorithms,” IEEE Trans. Power Delivery, vol. 24, no. 1, pp. 260-267, 2009.
21
[22] M. Atanasovski, R. Taleski, “Power summation method for loss allocation in radial distribution networks with DG,” IEEE Trans. Power Syst., vol. 26, no. 4, pp. 2491-2499, 2011.
22
[23] Z. Ghofrani-Jahromi, Z. Mahmoodzadeh and M. Ehsan, “Distribution loss allocation for radial systems including DGs” IEEE Trans. Power Delivery, vol. 29, no. 1, pp. 72-80, 2014.
23
[24] K. M. Jagtap, D. K. Khatod, “Loss allocation in distribution network with distributed generations,” IET Gener. Transm. Distrib., vol. 9, no. 13, pp. 1628-1641, 2015.
24
[25] P. M. Sotkiewicz, J. M. Vignolo, “Towards a cost causation-based tariff for distribution networks with DG,” IEEE Trans. Power Syst., vol. 22, no. 3, pp. 1051-1060, 2007.
25
[26] R. K. Singh, S. K. Goswami, “Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue”, Int. J. Electr. Power Energy Syst., vol. 32, no. 6, pp. 637-644, 2010.
26
[27] K. Shaloudegi, N. Madinehi, S. H. Hosseinian, and H. A. Abyaneh, “A novel policy for locational marginal price calculation in distribution systems based on loss reduction allocation using game theory,” IEEE Trans. Power Syst., vol. 27, no. 2, pp. 811-820, 2012.
27
[28] E. A. Farsani, H. A. Abyaneh, M. Abedi, and S. H. Hosseinian, “A novel policy for LMP calculation in distribution networks based on loss and emission reduction allocation using nucleolus theory,” IEEE Trans. Power Syst., vol. 31, no. 1, pp. 143-152, 2016.
28
[29] E. Azad-Farsani, H. Askarian-Abyaneh, M. Abedi, and S. H. Hosseinian, “Stochastic locational marginal price calculation in distribution systems using game theory and point estimate method,” IET Gener. Transm. Distrib., vol. 9, no. 14, pp. 1811-1818, 2015.
29
[30] Z. Hu, H. Cheng, Z. Yan and F. Li, “An iterative LMP calculation method considering loss distributions,” IEEE Trans. Power Syst., vol. 25, no. 3, pp. 1469-1477, 2010.
30
[31] S. M. M. Larimi, M. R. Haghifam, M. Zangiabadi, and P. Taylor, “Value based pricing of distribution generations active power in distribution networks,” IET Gener. Transm. Distrib., vol. 9, no. 15, pp. 2117-2125, 2015.
31
ORIGINAL_ARTICLE
A Generalized Modular Multilevel Current Source Inverter
This paper proposes a novel topology of multilevel current source inverter which is suitable to apply in low/medium voltage. The proposed topology is capable of producing desirable bidirectional output current levels. Furthermore, it can employ symmetrical DC current sources as well as asymmetrical ones which is a significant advantage. Asymmetrical mode makes it possible to generate a great number of output levels by appropriate selection of DC current source magnitude, needless to make changes in the hardware of the inverter. As a result, various methods are presented to compute the magnitude of needed DC current sources. In comparison to the conventional H-Bridge inverter (CHB), the proposed inverter has lessened the number of required DC current sources, switches as well as related gate driver circuits. The reduced number of required components has leads to cost and volume advantages. In addition, the control layout has become simpler. Reduction of power loss as a result of reduced number of on-state switches is the other merit of the proposed inverter. To evaluate the efficiency of the proposed inverter, its simulation and experimental results are extracted including results of various methods of determining DC current source magnitude.
http://joape.uma.ac.ir/article_596_1e164ca840cc111b3bd2029a263ed9de.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
181
190
10.22098/joape.2017.3080.1254
Multilevel Current Source Inverters
Symmetric Inverter
Asymmetric Inverter
Reduced Number of Circuit Devices
Power Loss
E.
Seifi Najmi
e.seifi@azaruniv.edu
true
1
Electrical Engineering Deprtment of Azarbaijan Shahid Madani University
Electrical Engineering Deprtment of Azarbaijan Shahid Madani University
Electrical Engineering Deprtment of Azarbaijan Shahid Madani University
AUTHOR
A.
Ajami
ajami@azaruniv.edu
true
2
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
Electrical Engineering Dept. of Azarbaijan Shahid Madani University
LEAD_AUTHOR
A. H.
Rajaei
a.rajaei@sutech.ac.ir
true
3
Faculty of Electrical Engineering, Shiraz University of Technology
Faculty of Electrical Engineering, Shiraz University of Technology
Faculty of Electrical Engineering, Shiraz University of Technology
AUTHOR
[1] M. R. Banaei, M. R. Jannati Oskuee and H. Khounjahan, “Reconfiguration of semi-cascaded multilevel inverter to improve systems performance parameters,” IET Power Electron., vol. 7, no. 5, pp. 1106-1112, 2014.
1
[2] M. R. Banaei, M. R. Jannati Oskuee and F. Mohajel Kazemi, “Series H-bridge with stacked multi-cell inverter to quadruplicate voltage levels,” IET Power Electron., vol. 6, no. 5, pp. 878-884, 2013.
2
[3] M. R. Banaei, F. Mohajel Kazemi, M. R. Jannati Oskuee, “New mixture of hybrid stacked multi-cell with half-cascaded converter to increase voltage level,” IET Power Electron., vol. 6, no. 7, pp. 1406-1414, 2013.
3
[4] K. Sivakumar, D. Anandarup, R. Ramchand and C. Patel, “A hybrid multilevel inverter topology for an open-end winding induction-motor drive using two-level inverters in series with a capacitor-fed H-bridge cell,” IEEE Trans. Ind. Electrons, vol. 57, no. 11, pp. 3707-3714, 2010.
4
[5] J. Rodriguez, J. Lai, and F. Z. Peng, “Multilevel inverters: A survey of topologies, controls and applications,” IEEE Trans. Ind. Electron., vol. 49, no. 4, pp. 724-738, 2002.
5
[6] M. Farhadi Kangarlu, E. Babaei and F. Blaabjerg, “An LCL-filtered single-phase multilevel inverter for grid integration of PV systems,” J. Oper. Autom. Power Engin., vol. 4, no. 1, pp. 54-65, 2016.
6
[7] A. Ajami, M. R. Jannati Oskuee, A. Mokhberdoran, H. Shokri, “Selective harmonic elimination method for wide range of modulation indexes in multilevel inverters using ICA,” J. Cent. South Univ., vol. 21, no. 4, pp. 1329-1338, 2014.
7
[8] A. Ajami, M. R. Jannati Oskuee and A. Mokhberdoran, “Implementation of novel technique for selective harmonic elimination in multilevel inverters based on ICA,” Adv. Power Electron., vol. 2013, pp.1- 10, 2013.
8
[9] S. Laali, E. Babaei and M. B. B. Sharifian, “Reduction the number of power electronic devices of a cascaded multilevel inverter based on new general topology,” J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 81-90, 2014.
9
[10] Y. Zhang and J. V. Milanovic, “Global voltage sag mitigation with FACTS based devices,” IEEE Trans. Power Deliv., vol. 25, no. 4, pp. 2842-2850, 2010.
10
[11] A. Llaria, O. Curea, J. Jiménez, and H. Camblong, “Survey on micro-grids: unplanned islanding and related inverter control techniques,” Renew. Energy, vol. 36, no. 8, pp. 2052-2061, 2011.
11
[12] R. T. H. Li, H. S. Chung, and T. K. M. Chan, “An active modulation technique for single-phase grid connected CSI,” IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1373-1380, 2007.
12
[13] A. R. Beig and V. T. Ranganathan, "A novel CSI-fed induction motor drive." IEEE Trans. Power Electron., vol. 21, no. 4, pp. 1073-1082, 2006.
13
[14] T. Noguchi and Suroso, “Review of novel multilevel current-source inverters with h-bridge and common-emitter based topologies,” Proc. IEEE Energy Convers. Congr. Expos., 2010, pp. 4006-4011.
14
[15] R. E. Torres-Olguin, A. Garces, M. Molinas and T. Undemand, “Integration of offshore wind farm using a hybrid HVDC transmission composed by the PWM current-source converter and line-commutated converter,” IEEE Trans. Energy Convers., vol. 28, no.1, pp. 125-134, 2013.
15
[16] V.Vekhande, N. Kothari and B. G. Fernandes, “Switching state vector selection strategies for paralleled multilevel current-fed inverter under unequal DC-link currents condition,” IEEE Trans. Power Electron., vol. 30, no. 4, pp. 1998-2009, 2015.
16
[17] P. Cossutta, M. P. Aguirre, A. Cao, S. Raffo, and M. I. Vaua, “Single stage fuel cell to grid interface with multilevel current-source inverters,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5256-5264, 2015.
17
[18] S. Kwak, and H. A. Toliyat, “Multilevel converter topology using two types of current-source inverters,” IEEE Trans. Ind. Appl., vol. 42, no. 6, pp. 1558-1564, 2006.
18
[19] B. P. McGrath, and D. G. Holmes, “Natural current balancing of multi-cell current source inverter,” IEEE Trans. Power Electron., vol. 23, no. 3, pp. 1239-1246, 2008.
19
[20] Z. H. Bai, and Z. C. Zhang, “Conformation of multilevel current source converter topologies using the duality principle,” IEEE Trans. Power Electron., vol. 23, no. 5, pp. 2260-2267, 2008.
20
[21] E. S. Najmi and A. Ajami, "Modular symmetric and asymmetric reduced count switch multilevel current source inverter," IET Power Electron., vol. 9, no. 1, pp. 51-61, 2016.
21
[22] A. Nami, L. Jiaqi, F. Dijkhuizen, and G. D. Demetriades, “Modular Multilevel Converters for HVDC Applications: Review on converter cells and functionalities, ” IEEE Trans. Power Electron., vol. 30, no. 1, pp. 18-36, 2015.
22
[23] A. Ajami, M. R. Jannati Oskuee, M. T. Khosroshahi and A. Mokhberdoran, “Cascade multi-cell multilevel converter with reduced number of switches,” IET Power Electron., vol. 7, no. 3, pp. 552-558, 2014.
23
[24] Data sheet of IGBT ‘BUP314’, Available at: www.datasheetcatalog.com.
24
ORIGINAL_ARTICLE
Adaptive Observer-Based Decentralized Scheme for Robust Nonlinear Power Flow Control Using HPFC
This paper investigates the robust decentralized nonlinear control of power flow in a power system using a new configuration of UPFC. This structure comprises two shunt converters and one series capacitor called as hybrid power flow controller (HPFC). A controller is designed via control Lyapunov function (CLF) and adaptive observer to surmount the problems of stability such as tracking desired references, robustness against uncertainties, rejecting the disturbances, and remote data estimation. The suggested control scheme is decentralized using adaptive observer to estimate the non-local varying parameters of the system. Stability of the closed loop system is proved mathematically using Lyapunov stability theorem. Performance of the proposed finite-time controller (FT-C) is compared to another suggested exponentially convergent nonlinear controller (ECN-C) and a conventional PI controller (PI-C). Settling time of the state variables are diminished to a known little time by FT-C in comparison with ECN-C and PI-C. Simulation results are given to validate the proposed controllers. Effects of model uncertainties such as parameter variation in the transmission line and the converters are studied and properly compensated by the proposed controllers. The impact of the control gain and the communication time-delay is shown using the Bode diagram analysis.
http://joape.uma.ac.ir/article_597_a62fd740a9b3cd4c1344d016b10948ee.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
191
203
10.22098/joape.2017.3007.1251
Decentralized Control Lyapunov function
flexible AC transmission systems
Hybrid power flow controller
nonlinear control systems
robust control
A.
Mohammadpour Shotorbani
a.m.shotorbani@tabrizu.ac.ir
true
1
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
LEAD_AUTHOR
S.
Ghassem Zadeh
g_zadeh@tabrizu.ac.ir
true
2
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
AUTHOR
B.
Mohammadi-ivatloo
bmohammadi@tabrizu.ac.ir
true
3
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
AUTHOR
S. H.
Hosseini
hosseini@tabrizu.ac.ir
true
4
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
Department of Electrical and Computer Engineering, University of Tabriz, Tabriz,Iran
AUTHOR
L.
Wang
liwei.wang@ubc.ca
true
5
School of Engineering, University of British Columbia
School of Engineering, University of British Columbia
School of Engineering, University of British Columbia
AUTHOR
[1] S. Kamel, F. Juradoa, and J. A. P. Lopes, “Comparison of various UPFC models for power flow control” Electr. Power Syst. Res., vol. 121, pp. 243-251, 2015.
1
[2] G. S. Ilango, C. Nagamani, A. V. S. S. R. Sai, and D. Aravindan, “Control algorithms for control of real and reactive power flows and power oscillation damping using UPFC” Electr. Power Syst. Res., vol. 79, pp. 595-605, 2009.
2
[3] L. Gyugyi, “A unified power flow control concept for flexible AC transmission systems” IEEE Proc. C Gener. Transm. Distrib., vol. 139, no. 4, pp. 323-331, 1992.
3
[4] S. A. Al-Mawsawi, “Comparing and evaluating the voltage regulation of a UPFC and STATCOM” Int. J. Electr. Power Energy Syst., vol. 25, pp. 735-740, 2003.
4
[5] E. Gholipour and S. Saadate, “Improving of transient stability of power systems using UPFC” IEEE Trans. Power Delivery, vol. 20, no. 2, pp. 1677-1682, 2005.
5
[6] A. M. Shotorbani, A. Ajami, M. P. Aghababa, and S. H. Hosseini, “Direct Lyapunov theory-based method for power oscillation damping by robust finite-time control of unified power flow controller” IET Gener. Transm. Distrib., vol. 7, pp. 691-699, 2013.
6
[7] J. Guo, M. L. Crow, and J. Sarangapani, “An improved UPFC control for oscillation damping” IEEE Trans. Power Syst., vol. 14, pp. 288-296, 2009.
7
[8] N. Bigdeli, E. Ghanbaryan, and K. Afshar, “Low frequency oscillations suppression via CPSO based damping controller” J. Oper. Autom. Power Eng., vol. 1, pp. 22-32, 2013.
8
[9] M. R. Esmaili, A. Khodabakhshian, and M. Bornapour, “A new coordinated design of UPFC controller and PSS for improvment of power system stability using CPCE algorithm” Proc. IEEE Electr. Power Energy Conf., Ottawa, pp. 1-6, 2016.
9
[10] L. Wang, H.W. Li, and C.T. Wu, “Stability analysis of an integrated offshore wind and seashore wave farm fed to a power grid using a unified power flow controller” IEEE Trans. Power Syst., vol. 28, pp. 2211-2221, 2013.
10
[11] W.M. Lin, K.H. Lu, and T.C. Ou, “Design of a novel intelligent damping controller for unified power flow controller in power system connected offshore power applications” IET Gener. Transm. Distrib., vol. 9, pp. 1708-1717, 2015.
11
[12] A. Mohanty, S. Patra, and P. K. Ray, “Robust fuzzy-sliding mode based UPFC controller for transient stability analysis in autonomous winddiesel-PV hybrid system” IET Gener. Transm. Distrib., vol. 10, pp. 1248-1257, 2016.
12
[13] M. Firouzi, G. B. Gharehpetian, and B. Mozafari, “Power-flow control and short-circuit current limitation of wind farms using unified interphase power controller” IEEE Trans. Power Delivery, vol. 32, pp. 32-71, 2017.
13
[14] M. A. Sayed and T. Takeshita, “Line loss minimization in isolated substations and multiple loop distribution systems using the UPFC” IEEE Trans. Power Electron., vol. 29, pp. 5813-5822, 2014.
14
[15] A. R. Ghahnavieh, M. Fotuhi-Firuzabad, and M. Othman, “Optimal unified power flow controller application to enhance total transfer capability” IET Gener. Transm. Distrib., vol. 9, pp. 358-368, 2015.
15
[16] J. Z. Bebic, P. W. Lehn, and M. R. Iravani, “The hybrid power flow controller a new concept for flexible AC transmission” Proc. IEEE Power Eng. Soc. Gen. Meeting, pp. 1-6, 2006.
16
[17] A. K. Sadigh, M. T. Hagh, and M. Sabahi, “Unified power flow controller based on two shunt converters and a series capacitor” Electr. Power Syst. Res., vol. 80, pp. 1511-1519, 2010
17
[18] A. Shukla, A. Ghosh, and A. Joshi, “Static shunt and series compensation of an SMIB system using flying capacitor multilevel inverter, ” IEEE Trans. Power Delivery, vol. 20, pp. 2613-2622, 2005.
18
[19] D. Soto and T. C. Green, “A comparison of high-power converter topologies for the implementation of FACTS controllers, ” IEEE Trans. Indus. Electron., vol. 49, pp. 1072-1080, 2002.
19
[20] S. Yang, D. Gunasekaran, Y. Liu, U. Karki, and F. Z. Peng, “Application of transformer-less UPFC for interconnecting synchronous AC grids, ” Proc. IEEE Energy Convers. Congr. Expos., Montreal, 2015, pp. 1-6.
20
[21] F. Z. Peng, Y. Liu, S. Yang, S. Zhang, D. Gunasekaran, and U. Karki, “Transformer-less unified power-flow controller using the cascade multilevel inverter, ”IEEE Trans. Power Electron., vol. 31, pp. 5461-5472, 2016.
21
[22] S. Yang, Y. Liu, X. Wang, D. Gunasekaran, U. Karki, and F. Z. Peng, “Modulation and control of transformerless UPFC, ” IEEE Trans. Power Electron., vol. 31, pp. 1050-1063, 2016.
22
[23] Y. Liu, S. Yang, X. Wang, D. Gunasekaran, U. Karki, and F. Z. Peng, “Application of transformer-less upfc for interconnecting two synchronous AC grids with large phase difference” IEEE Trans. Power Electron., vol. 31, pp. 6092-6103, 2016.
23
[24] A. M. Shotorbani, A. Ajami, S. G. Zadeh, M. P. Aghababa, and B. Mahboubi, “Robust terminal sliding mode power flow controller using unified power flow controller with adaptive observer and local measurem-ent, ” IET Gener. Transm. Distrib., vol. 8, pp. 1712-1723, 2014.
24
[25] A. Ajami, A. M. Shotorbani, and M. P. Aagababa, “Application of the direct Lyapunov method for robust finite-time power flow control with a unified power flow controller, ” IET Gener. Transm. Distrib., vol. 6, pp. 822-830, 2012.
25
[26] T. T. Ma, “P-Q decoupled control schemes using fuzzy neural networks for the unified power flow controller, ” Int. J. Electr. Power Energy Syst., vol. 29, pp. 748-758, 2007.
26
[27] J. Monteiro, J. F. Silva, S. F. Pinto, and J. Palma, “Linear and sliding-mode control design for matrix converter-based unified power flow controllers, ” IEEE Trans. Power Electron., vol. 29, pp. 3357-3367, 2014.
27
[28] M. J. Kumar, S. S. Dash, A. S. P. Immanuvel, and R. Prasanna, “Comparison of FBLC (feed-back linearization) and PI-controller for UPFC to enhance transient stability, ” Proc. Int. Conf. Comput. Commun. Electr. Technol., 2011, pp. 1-6.
28
[29] B. Lu and B. T. Ooi, “Unified power flow controller (UPFC) under nonlinear control, ” Proc. PCC-Osaka Power Convers. Conf., 2002, pp. 1-6.
29
[30] G. S. Ilango, C. Nagamani, and D. Aravindan, “Independent control of real and reactive power flows using UPFC based on adaptive back stepping, ” Proc. IEEE Reg. 10 Conf., Hyderabad, 2008, pp. 1-6.
30
[31] S. Mehraeen, J. Sarangapani, and M. L. Crow, “Novel dynamic representation and control of power systems with FACTS devices, ” IEEE Trans. Power Syst., vol. 25, pp. 1542-1554, 2010.
31
[32] H. Alasooly and M. Redha, “Optimal control of UPFC for load flow control and voltage flicker elimination and current harmonics elimination, ” Comput. Math. Appl., vol. 60, pp. 926-943, 2010.
32
[33] A. Rajabi-Ghahnavieh, M. Fotuhi-Firuzabad, and M. Othman, “Optimal unified power flow controller application to enhance total transfer capability, ” IET Gener. Transm. Distrib., vol. 9, pp. 358-368, 2015.
33
[34] S. A. Taher, S. Akbari, A. Abdolalipour, and R. Hematti, “Design of robust decentralized control for UPFC controller based on structured singular value, ” Am. J. Appl. Sci., vol. 5, no. 10, pp. 1269-1280, 2008.
34
[35] F. Shalchi, H. Shayeghi, and H. A. Shayanfar, “Robust control design for UPFC to improve damping of oscillation in distribution system by H2 method, ” Proc. 16th Conf. Electr. Power Distrib. Networks, Bandar Abbas, 2011.
35
[36] M. M. Farsangi, Y. H. Song, W. L. Fang, and X. F. Wang, “Robust FACTS control design using the H/sub/spl infin loop-shaping method," IEE Proc. Gener. Transm. Distrib., vol. 149, pp. 352-358, 2002.
36
[37] M. Januszewski, J. Machowski, and J. W. Bialek, “Application of the direct Lyapunov method to improve damping of power swings by control of UPFC, ” IEE Proc. Gener. Transm. Distrib., pp. 252-260, 2004.
37
[38] S. G. Nersesov, W. M. Haddad, and Q. Hui, “Finite-time stabilization of nonlinear dynamical systems via control vector Lyapunov functions, ” J. Franklin Inst., vol. 345, pp. 819-837, 2008.
38
[39] S. Yu, X. Yu, B. Shirinzadeh, and Z. Man, “Continuous finite-time control for robotic manipulators with terminal sliding mode, ” Autom., vol. 41, pp. 1957-1964, 2005.
39
[40] A. G. L. H. Huerta, J.M. Canedo, “Robust multi-machine power systems control via high order sliding modes, ” Electr. Power Syst. Res., vol. 81, pp. 1602-1609, 2011.
40
[41] H. Huerta, A. G. Loukianov, and J. M.Cañedo, “Multimachine power-system control: integral-sm approach, ” IEEE Trans. Ind. Electron., vol. 56, pp. 2229-2236, 2009.
41
[42] V. I. Utkin, J. Guldner, and J. Shi, Sliding Mode Control in Electromechanical Systems. London: Taylor & Francis, 1999.
42
[43] G. Besancon, J. D. Leon–Morales, and O. Huerta–Guevara, “On adaptive observers for state affine systems, ”Int. J. Control, vol. 79, pp. 581-591, 2006.
43
[44] H. Saadat, Power System Analysis, Second Edition ed.: McHraw-Hill, 2002.
44
[45] W. C. Schultz and V. C. Rideout, “Control system performance measures: Past, present, and future” IRE Trans. Autom. Control, vol. AC-6, pp. 22-35, 1961.
45
[46] R. Kazemzadeh, M. Moazen, R. Ajabi-Farshbaf, and M. Vatanpour, “STATCOM optimal allocation in transmission grids considering contingency analysis in OPF using BF-PSO algorithm, ” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 1-11, 2013.
46
ORIGINAL_ARTICLE
Optimal Sizing of Energy Storage System in A Renewable-Based Microgrid Under Flexible Demand Side Management Considering Reliability and Uncertainties
Utilization of energy storage system (ESS) in microgrids has turned to be necessary in recent years and now with the improvement of storage technologies, system operators are looking for an exact modeling and calculation for optimal sizing of ESS. In the proposed paper, optimal size of ESS is determined in a microgrid considering demand response program (DRP) and reliability criterion. Both larger and small-scale ESSs have their own problems. A large-scale ESS reduces microgrid operating cost but it includes higher investment costs while a small-scale ESS has less investment cost. The main goal of the proposed paper is find optimal size of ESS in which microgrid investment cost as well as operating cost are minimized. Since the renewable units may not have stable production and also because of the outages that conventional units may have, ESS is utilized and then a reliability index called reliability criterion is obtained. Furthermore, effects of reliability criterion and DRP on optimal sizing of ESS are evaluated. A mixed-integer programing (MIP) is used to model the proposed stochastic ESS optimal sizing problem in a microgrid and GAMS optimization software is used to solve it. Five study cases are studied and the results are presented for comparison.
http://joape.uma.ac.ir/article_598_eb8e9a2dba34502c24a2daf8a4ebe939.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
205
214
10.22098/joape.2017.3356.1268
Energy storage system
Renewable-based microgrid
Reliability criterion
Demand response program
M.
Majidi
majidmajidi95@ms.tabrizu.ac.ir
true
1
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
AUTHOR
S.
Nojavan
sayyad.nojavan@tabrizu.ac.ir
true
2
Faculty of Electrical and Computer Engineering, University of Tabriz
Faculty of Electrical and Computer Engineering, University of Tabriz
Faculty of Electrical and Computer Engineering, University of Tabriz
LEAD_AUTHOR
[1] X. Zhang, J. Bao, R. Wang, C. Zheng, and M. Skyllas-Kazacos, Dissipativity based distributed economic model predictive control for residential microgrids with renewable energy generation and battery energy storage," Renewable Energy, vol. 100, pp. 18-34,2017.
1
[2] M. Khalid, A. Ahmadi, A. V. Savkin, and V. G. Agelidis, "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renew. Energy, vol. 97, pp. 646-655, 2016.
2
[3] R. Mallol-Poyato, S. Salcedo-Sanz, S. Jiménez-Fernández, and P. Díaz-Villar, "Optimal discharge scheduling of energy storage systems in MicroGrids based on hyper-heuristics," Renewable Energy, vol. 83, pp. 13-24, 2015.
3
[4] S. Bahramirad, W. Reder, and A. Khodaei, "Reliability-constrained optimal sizing of energy storage system in a microgrid," IEEE Trans. Smart Grid, vol. 3, pp. 2056-2062, 2012.
4
[5] X. Wang, D. M. Vilathgamuwa, and S. Choi, "Determination of battery storage capacity in energy buffer for wind farm," IEEE Trans. Energy Convers., vol. 23, pp. 868-878, 2008.
5
[6] S.-J. Chiang, K. Chang, and C. Yen, "Residential photovoltaic energy storage system," IEEE Trans. Ind. Electron., vol. 45, pp. 385-394, 1998.
6
[7] C. Venu, Y. Riffonneau, S. Bacha, and Y. Baghzouz, "Battery storage system sizing in distribution feeders with distributed photovoltaic systems," Proc. of the IEEE Power Technol., Bucharest, 2009, pp. 1-5.
7
[8] A. S. Awad, T. H. El-Fouly, and M. M. Salama, "Optimal ESS allocation and load shedding for improving distribution system reliability," IEEE Trans. Smart Grid, vol. 5, pp. 2339-2349, 2014.
8
[9] M. Zidar, P. S. Georgilakis, N. D. Hatziargyriou, T. Capuder, and D. Škrlec, "Review of energy storage allocation in power distribution networks: applications, methods and future research," IET Gener. Transm. Distrib., vol. 10, pp. 645-652, 2016.
9
[10] L. Bridier, D. Hernández-Torres, M. David, and P. Lauret, "A heuristic approach for optimal sizing of ESS coupled with intermittent renewable sources systems," Renew. Energy, vol. 91, pp. 155-165, 2016.
10
[11] F. Fallahi, M. Nick, G. H. Riahy, S. H. Hosseinian, and A. Doroudi, "The value of energy storage in optimal non-firm wind capacity connection to power systems," Renew. Energy, vol. 64, pp. 34-42, 2014.
11
[12] J. P. Fossati, A. Galarza, A. Martín-Villate, and L. Fontán, "A method for optimal sizing energy storage systems for microgrids," Renew. Energy, vol. 77, pp. 539-549, 2015.
12
[13]M. Nick, R. Cherkaoui, and M. Paolone, "Optimal siting and sizing of distributed energy storage systems via alternating direction method of multipliers," Int. J. Electr. Power Energy Syst., vol. 72, pp. 33-39, 2015.
13
[14] M. Motalleb, E. Reihani, and R. Ghorbani, "Optimal placement and sizing of the storage supporting transmission and distribution networks," Renew. Energy, vol. 94, pp. 651-659, 2016.
14
[15] F. M. Vieira, P. S. Moura, and A. T. de Almeida, "Energy storage system for self-consumption of photovoltaic energy in residential zero energy buildings," Renew. Energy, vol. 103, pp. 308-320, 2017.
15
[16] A. H. Alami, K. Aokal, J. Abed, and M. Alhemyari, "Low pressure, modular compressed air energy storage (CAES) system for wind energy storage applications," Renew. Energy,vol. 106, pp. 201-211, 2017.
16
[17] M. Cheng, S. S. Sami, and J. Wu, "Benefits of using virtual energy storage system for power system frequency response," Appl. Energy, vol. 194, pp. 376-385, 2017.
17
[18] E. Heydarian-Forushani and H. Aalami, "Multi objective scheduling of utility-scale energy storages and demand response programs portfolio for grid integration of wind power," J. Oper. Autom. Power Eng., vol. 4, no. 2, pp. 104-116, 2016.
18
[19] M. Allahnoori, S. Kazemi, H. Abdi, and R. Keyhani, "Reliability assessment of distribution systems in presence of microgrids considering uncertainty in generation and load demand," J. Oper. Autom. Power Eng., vol. 2, no. 2, pp. 113-120, 2014.
19
[20] R. Ghanizadeh, M. Ebadian, and G. B. Gharehpetian, "Control of inverter-interfaced distributed generation units for voltage and current harmonics compensation in grid-connected microgrids," J. Oper. Autom. Power Eng., vol. 4, no.1, pp. 66-82, 2016.
20
[21] R. Billinton and R. N. Allan, Reliability of Power Systems, 2nd ed. New York: Plenum, 1996.
21
[22] L. Wu, M. Shahidehpour, and T. Li, "Stochastic security-constrained unit commitment," IEEE Trans. Power Syst., vol. 22, pp. 800-811, 2007.
22
[23] M. Ross, R. Hidalgo, C. Abbey, and G. Joós, "Analysis of energy storage sizing and technologies," Proc. IEEE Electr. Power Energy Conf., 2010, pp. 1-6.
23
[24] M. R. Patel, Wind and Solar Power Systems. Boca Raton, FL: CRC, 1999.
24
[25] C. Justus, W. Hargraves, A. Mikhail, and D. Graber, "Methods for estimating wind speed frequency distributions," J. appl. meteorol., vol. 17, pp. 350-353, 1978.
25
[26] J. Seguro and T. Lambert, "Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis," J. Wind Eng. Ind. Aerodyn., vol. 85, pp. 75-84, 2000.
26
[27] S. Kamalinia, M. Shahidehpour, and A. Khodaei, "Security-constrained expansion planning of fast-response units for wind integration," Electr. Power Syst. Res., vol. 81, pp. 107-116, 2011.
27
[28] A. Nourai, "Installation of the first Distributed Energy Storage System (DESS) at American Electric Power (AEP)," Sandia National Laboratories, 2007.
28
[29] S. Nojavan, B. Mohammadi-Ivatloo, and K. Zare, "Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties," IET Gener. Transm. Distrib., vol. 9, pp. 328-338, 2015.
29
[30] N. Growe-Kuska, H. Heitsch, and W. Romisch, "Scenario reduction and scenario tree construction for power management problems," Proc. IEEE Power Technol. conf., Bologna, 2003, pp. 1-7.
30
[31]http://www.gams.com/help/index.jsp?topic=%2Fgams.d%2Fsolvers%2Findex.html
31
ORIGINAL_ARTICLE
Dynamic Analysis and Optimal Design of FLPSS for Power Network Connected Solid Oxide Fuel Cell Using of PSO
This paper studies the theory and modeling manner of solid oxide fuel cell (SOFC) into power network and its effect on small signal stability. The paper demonstrates the fundamental module, mathematical analysis and small signal modeling of the SOFC connected to single machine infinite bus (SMIB) system. The basic contribution of the study is to attenuate the low frequency oscillations by optimal stabilizers in the presence of SOFC. To optimize the performance of system, fuzzy logic-based power system stabilizer (FLPSS) is exploited and designed by particle swarm optimization (PSO) technique. To ensure the effectiveness of the proposed optimal stabilizers, the simulation process takes in three scenarios of operating conditions. The effectiveness of proposed PSO based FLPSS on the oscillations in the power system, including SOFC is extensively demonstrated through time-domain simulations and by comparing FLPSS with the results of other stabilizers approaches.
http://joape.uma.ac.ir/article_599_ee9feef47a74309f1031bdbee5d647ec.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
215
225
10.22098/joape.2017.3563.1282
Solid Oxide Fuel Cell
Fuzzy Logic based PSS
Small Signal Model
Particle Swarm Optimization
H.
Shahsavari
hosin.shahsavari@gmail.com
true
1
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
AUTHOR
A.
Safari
asafari1650@yahoo.com
true
2
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
LEAD_AUTHOR
J.
Salehi
j.salehi@azaruniv.ac.ir
true
3
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
Department of Electrical Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
AUTHOR
[1] J. Larminie and A. Dicks., Fuel Cell System Explained, 2nd. New York, Wiley, 2002.
1
[2] M. Farooque and H. C. Maru., “Fuel cells-the clean and efficient power generators,” Proc.IEEE, vol. 89, no.12, pp. 1819-1829, 2001.
2
[3] B. Raton, Fuel Cell Technology Handbook., FL: CRC, 2002.
3
[4] P. Thounthong, B. Davat, S. Rael, P. Sethakul., “Fuel cell high-power applications,” IEEE Ind. Electron. Mag., vol. 3, no. 1: pp. 32-46, 2009.
4
[5] Y. H. Li, S. Rajakaruna and S. S. Choi., “Control of a solid oxide fuel cell power plant in a grid-connected system,” IEEE Trans. Energy Coners., vol. 22, no. 2, pp. 405-413, 2007.
5
[6] J. Padulle’s, G. W. Ault, and J. R. McDonald., “An integrated SOFC plant dynamic model for power systems simulation,” J. Power Sources, vol. 86, pp. 495-500, 2000.
6
[7] K. Sedghisigarchi and A. Feliachi., “Dynamic and transient analysis of power distribution systems with fuel cells–part I: fuel-cell dynamic model,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 423-428, 2004.
7
[8] K. Sedghisigarchi and A. Feliachi., “Dynamic and transient analysis of power distribution systems with fuel cells–part II: control and stability enhancement,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 429-434, 2004.
8
[9] S. Das, D. Das and A. Patra., “Operation of solid oxide fuel cell based distributed generation,” Energy Procedia, vol. 31; pp. 439-447, 2009.
9
[10] E. M. Fleming and I. A. Hiskens., “Dynamics of a microgrid supplied by solid oxide fuel cells in bulk power system dynamics and control-VII.” IEEE Revitalizing Oper. Reliab., vol. 19, pp. 1-10, 2007.
10
[11] H. Wang and G. Li., “Dynamic performance of microturbine and fuel cell in a microgrid,” Proc. Int. Conf. Mechatron. Sci. Electr. Eng. Comput., pp. 122-125, 2011.
11
[12] W. Du, H. F. Wang, X.F. Zhang and L.Y. Xiao., “Effect of grid-connected solid oxide fuel cell power generation on power systems small-signal stability,” IET Renew. Power Gener., vol. 6, no.1, pp. 24-37, 2012.
12
[13] C. J. Hatziadoniu, A. A. Lobo, F. Pourboghrat and M. Daneshdoost., “A simplified dynamic model of grid-connected fuel-cell generators,” IEEE Trans. Power Delivery, vol. 17, no. 2, pp. 467-473, 2002.
13
[14] P. Kunder, Power system stability and control, McGraw-Hill, 1994.
14
[15] X. Yang and A. Feliachi., “Stabilization of inter-area oscillation modes through excitation systems,” IEEE Trans. Power Syst., vol. 9, no.1, pp.494-502, 1994.
15
[16] M. Klein, G. J. Roger and P. Kundur., “A fundamental study of inter-area oscillations in power systems,” IEEE Trans. Power Syst., vol. 6, no. 3, pp. 914-921, 1991.
16
[17] A. M. El-Zonkoly, A. A. Khalil and N. M. Ahmied., “Optimal tuning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization,” Expert Syst. Appl. ,vol. 36, no. 2, pp. 2097-2106, 2009.
17
[18] K. Mazlumi, M. Darabian and M. Azari., “Adaptive fuzzy synergetic PSS design to damp power system oscillations,” J. Oper. Autom. Power Eng., vol. 1, no. 1, pp. 43-53, 2013.
18
[19] Y. N. Yu, Electric power system dynamics, Academic Press, Inc., 111 FIFTH AVE., NEW YORK, NY 10003, USA, 1983.
19
[20] M. R. Banaei., “Multi-Stage DC-AC Converter Based on new DC-DC converter for energy conversion,” J. Oper. Autom. Power Eng., vol. 4, no. 1, pp. 42-53, 2016.
20
[21] CIGRE technical report: Modeling of power electronics equipment (FACTS) in load flow and stability programs, CIGRE T F 38-01-08, 1998.
21
[22] J. Kennedy and R. Eberhart., “Particle swarm optimization,” Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 1942-1948, 1995.
22
[23] A. Jalilvand, A. Safari and R. Aghmasheh, “Design of state feedback stabilizer for multi-machine power system using PSO algorithm,” Proc. IEEE Int. Conf. Multitopic, 2008, pp. 1-6.
23
[24] M. Rahmati, R. Effatnejad and A. Safari, “Comprehensive learning particle swarm optimization for multi-objective optimal power flow,” Indian J. Sci. Technol., vol. 7, no. 3, pp. 262-270, 2014.
24
[25] A. Safari, N. Rezaei, “Towards an extended power system stability: An optimized GCSC-based inter-area damping controller proposal,” Int. J. Electr. Power Energy Syst., vol. 56, pp. 316-324, 2014.
25
ORIGINAL_ARTICLE
Optimal Operation Management of Grid-connected Microgrid Using Multi-Objective Group Search Optimization Algorithm
Utilizing distributed generations (DGs) near load points has introduced the concept of microgrid. However, stochastic nature of wind and solar power generation as well as electricity load makes it necessary to utilize an energy management system (EMS) to manage hourly power of microgrid and optimally supply the demand. As a result, this paper utilizes demand response program (DRP) and battery to tackle this difficulty. To do so, an incentive-based DRP has been utilized and the effects of applying DRP on microgrid EMS problem have been studied. The objective functions of microgrid EMS problem include the total cost and emission. These metrics are combined in a multi-objective formulation and solved by the proposed multi-objective group search optimization (MOGSO) algorithm. After obtaining Pareto fronts, the best compromise solution is determined by using fuzzy decision making (FDM) technique. Studies have been employed on a test microgrid composed of a wind turbine, photovoltaic, fuel cell, micro turbine and battery while it is connected to the upper-grid. Simulation results approve the efficiency of the proposed method in hourly operation management of microgrid components.
http://joape.uma.ac.ir/article_600_3f66114ecbc619b957fc1abd541f0f17.pdf
2017-12-01T11:23:20
2018-08-18T11:23:20
227
239
10.22098/joape.2017.3659.1290
Microgrid
Demand response program
MOGSO
Fuzzy decision making
Wind turbine
H.
Shayeghi
hshayeghi@gmail.com
true
1
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.
Electrical Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran.
LEAD_AUTHOR
E.
Shahryari
elnaz.shahryari@yahoo.com
true
2
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
AUTHOR
[1] C. Yin, H. Wu, F. Locment, and M. Sechilariu, “Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor,” Energy Convers. Manage., vol. 132, pp. 14-27, 2017.
1
[2] E. Hossain, E. Kabalci, R. Bayindir, and R. Perez, “Microgrid testbeds around the world: State of art,” Energy Convers. Manage., vol. 86, pp. 132-153, 2014.
2
[3] A. Deihimi, B. Keshavarz Zahed, and R. Iravani, “An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm,” Energy, vol. 106, pp. 482-509, 2016.
3
[4] E. E. Sfikas, Y. A. Katsigiannis, and P. S. Georgilakis, “Simultaneous capacity optimization of distributed generation and storage in medium voltage microgrids,” Int. J. Electr. Power Energy Syst., vol. 67, pp. 101-113, 2015.
4
[5] M. Marzband, F. Azarinejadian, M. Savaghebi, and J. M. Guerrero, “An optimal energy management system for islanded microgrids based on multiperiod artificial bee colony combined with markov chain,” IEEE Syst. J., vol. PP, pp. 1-11, 2015.
5
[6] T. Wu, Q. Yang, Z. Bao, and W. Yan, “Coordinated energy dispatching in microgrid with wind power generation and plug-in electric vehicles,” IEEE Trans. Smart Grid, vol. 4, pp. 1453-1463, 2013.
6
[7] J. Garcia-Gonzalez, R. M. R. d. l. Muela, L. M. Santos, and A. M. Gonzalez, “stochastic joint optimization of wind generation and pumped-storage units in an electricity market,” IEEE Trans. Power Syst., vol. 23, pp. 460-468, 2008.
7
[8] L. Guo, W. Liu, X. Li, Y. Liu, B. Jiao, W. Wang, “Energy management system for stand-alone wind-powered-desalination microgrid,” IEEE Trans. Smart Grid, vol. 7, pp. 1079-1087, 2016.
8
[9] M. Motevasel and A. R. Seifi, “Expert energy management of a micro-grid considering wind energy uncertainty,” Energy Convers. Manage., vol. 83, pp. 58-72, 2014.
9
[10] C.S. Karavas, G. Kyriakarakos, K. G. Arvanitis, and G. Papadakis, “A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous poly generation microgrids,” Energy Convers. Manage., vol. 103, pp. 166-179, 2015.
10
[11] S. A. Alavi, A. Ahmadian, and M. Aliakbar-Golkar, “Optimal probabilistic energy management in a typical micro-grid based-on robust optimization and point estimate method,” Energy Convers. Manage., vol. 95, pp. 314-325, 2015.
11
[12] M. A. Fotouhi Ghazvini, J. Soares, N. Horta, R. Neves, R. Castro, and Z. Vale, “A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers,” Appl. Energy, vol. 151, pp. 102-118, 2015.
12
[13] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Demand response modeling considering Interruptible/ Curtailable loads and capacity market programs,” Appl. Energy, vol. 87, pp. 243-250, 2010.
13
[14] L. Wang, Q. Li, R. Ding, M. Sun, and G. Wang, “Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach,” Energy, vol. 130, pp. 1-14, 2017.
14
[15] M. Mazidi, A. Zakariazadeh, S. Jadid, and P. Siano, “Integrated scheduling of renewable generation and demand response programs in a microgrid,” Energy Convers. Manage., vol. 86, pp. 1118-1127, 2014.
15
[16] G. R. Aghajani, H. A. Shayanfar, and H. Shayeghi, “Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management,” Energy Convers. Manage., vol. 106, pp. 308-321, 2015.
16
[17] R. Wang, P. Wang, G. Xiao, and S. Gong, “Power demand and supply management in microgrids with uncertainties of renewable energies,” Int. J. Electr. Power Energy Syst., vol. 63, pp. 260-269, 2014.
17
[18] H. Safamehr and A. Rahimi-Kian, “A cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program,” Energy, vol. 91, pp. 283-293, 2015.
18
[19] Y. Simmhan, S. Aman, A. Kumbhare, R. Liu, S. Stevens, Q. Zhou, “Cloud-based software platform for big data analytics in smart grids,” Comput. Sci. Eng., vol. 15, pp. 38-47, 2013.
19
[20] W. Alharbi and K. Raahemifar, “Probabilistic coordination of microgrid energy resources operation considering uncertainties,” Electr. Power Syst. Res., vol. 128, pp. 1-10, 2015.
20
[21] M. Marzband, M. Ghadimi, A. Sumper, and J. L. Domínguez-García, “Experimental validation of a real-time energy management system using multi-period gravitational search algorithm for microgrids in islanded mode,” Appl. Energy, vol. 128, pp. 164-174, 2014.
21
[22] B. Zhao, M. Xue, X. Zhang, C. Wang, and J. Zhao, “An MAS based energy management system for a stand-alone microgrid at high altitude,” Appl. Energy, vol. 143, pp. 251-261, 2015.
22
[23] S. He, Q. H. Wu, and J. R. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior,” IEEE Trans. Evol. Comput., vol. 13, pp. 973-990, 2009.
23
[24] M. Moradi-Dalvand, B. Mohammadi-Ivatloo, A. Najafi, and A. Rabiee, “Continuous quick group search optimizer for solving non-convex economic dispatch problems,” Electr. Power Syst. Res., vol. 93, pp. 93-105, 2012.
24
[25] H. A. Aalami, M. P. Moghaddam, and G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” Electr. Power Syst. Res., vol. 80, pp. 426-435, 2010.
25