A New Structure of Buck-Boost Z-Source Converter Based on Z-H Converter

Document Type : Research paper


Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran


In this paper, a new structure for buck-boost Z-source converter based on Z-H topology is proposed. The proposed converter consists of two LC networks similar to the conventional Z-source and Z-H converters. One of the characteristics of the proposed structure is that, without any changing in its’ power circuit, it can be used in different conversions such as dc/dc, dc/ac and ac/ac. This unique characteristic of the proposed structure is similar to matrix converters. To use this structure in different conversions just control system should be changed. Other main advantages of the proposed converter are simpler topology, step-up and step-down capabilities and low ripple in voltage and current waveforms. Due to capabilities of the proposed converter mentioned above, it can be used in applications such as connect renewable energy sources to the grid, speed control of induction machines, electric vehicles and etc. In this paper, a complete analysis of the proposed converter in dc/dc conversion with details and mathematical equations is presented. Moreover, for the proposed topology, the ripple of inductors and capacitors is given. A suitable control method is presented, too. Also, the power losses and efficiency of the proposed converter are calculated. The correctness operation of the proposed converter is reconfirmed by the experimental results.


Main Subjects

[1]     N. Troy, E. Denny, M. O'Malley, “Base-load cycling on a system with significant wind penetration,” IEEE Trans. Power Syst., vol. 25, no. 2, pp. 1088-1097, 2010.
[2]     J. D. Maddaloni, A. M. Rowe, G. C. van Kooten, “Wind integration into various generation mixtures,” Renewable Energy, vol. 34, no. 3, pp. 807-814, 2009.
[3]     H. Khorramdel, B. Khorramdel, M. T. Khorrami, H. Rastegar, “A multi-objective economic load dispatch considering accessibility of wind power with here-and-now (HN) approach”, J. Oper. Autom. Power Eng., vol. 2 no. 1, pp. 49-59, 2014.
[4]     P. Siano, “Demand response and smart grids-A survey,” Renewable Sustainable Energy Rev., vol. 30, pp. 461-478, 2014.
[5]     H. Holttinen, A. Tuohy, M. Milligan, E. Lannoye, V. Silva, S. Muller, “The flexibility workout: managing variable resources and assessing the need for power system modification,” IEEE Power Energy Mag., vol. 11, no. 6, pp. 53-62, 2013. 
[6]     G. Papaefthymiou, K. Grave, K. Dragoon, “Flexibility options in electricity systems,” 2014. Report. Available at: http://www.ecofys.com/en/pub-lication/ flexibility-options-in-electricity-systems/.     
[7]     K. Afshar, A. S. 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.
[8]     K. Dietrich, J. M. Latorre, L. Olmos, A. Ramos, “Demand response in an isolated system with high wind integration,” IEEE Trans. Power Syst., vol. 27, no. 1, pp. 20-29, 2012.
[9]     A. Keane, A. Tuohy, P. Meibom, E. Denny, D. Flynn, A. Mullane, M.  O'Malley, “Demand side resource operation on the Irish power system with high wind power penetration,” Energy Policy, vol. 39, no. 5, pp. 2925-2934, 2011.
[10]   M. Parvani, M. Fotuhi-Firuzabad, “Integrating load reduction into wholesale energy market with application to wind power integration,” IEEE Syst. J., vol. 6, no. 1, pp. 35-45, 2012.
[11]   A. Yousefi, H. C. Iu, T. Fernand, H. Trinh, “An approach for wind power integration using demand side resources,” IEEE Trans. Sustainable Energy, vol. 4, no. 4, pp. 917-924, 2013.
[12]   H. Falsafi, A. Zakariazadeh, S. Jadid, “The role of demand response in single and multi-objective wind-thermal generation scheduling: A stochastic programm-ing,” Energy, vol. 64, pp. 853-867, 2014.
[13]   E. Heydarian-Forushani, M.P. Moghaddam, M.K. Sheikh-El-Eslami, M. Shafie-khah, J.P.S. Catalao, “A stochastic framework for the grid integration of wind power using flexible load approach,” Energy Convers. Manage., vol. 88, pp. 985-998, 2014.
[14]   CAES dispatch modeling. Available online: http://www.smartgrid.gov/sites/default/files/doc/files/Exh%2013.13%20Energy%20Market%20Report%20CES%20Part%203.pdf.
[15]   T. Das, V. Krishnan, J. D. McCalley, “Assessing the benefits and economics of bulk energy storage technologies in the power grid,” Appl. Energy, vol. 139, no. 1, pp. 104-118, 2015.
[16]   D. Pozo, J. Contreras, EE. Sauma, “Unit commitment with ideal and generic energy storage units,” IEEE Trans.  Power Syst., vol. 29, no. 6, pp. 2974-2984, 2014.
[17]   M. Shafie-khah, M. P. Moghaddam, M. K. Sheikh-El-Eslami, J. P. S. Catalao, “Optimised performance of a plug-in electric vehicle aggregator in energy and reserve markets”, Energy Convers. Manage., vol. 97, pp. 393-408, 2015.
[18]   A. El-Zonkoly, “Intelligent energy management of optimally located renewable energy systems incorporating PHEV”, Energy Convers. Manage., vol. 84, pp. 427-435, 2014.
[19]   P. Pinson, H. Madsen, “Benefits and challenges of electrical demand response: A critical review,” Renewable Sustainable Energy Rev., vol. 39, pp. 686-699, 2014.
[20]   J. Aghaei, M. I. Alizadeh, “Demand response in smart electricity grids equipped with renewable energy sources: A review,” Renewable Sustainable Energy Rev., vol. 18, pp. 64-72, 2013.
[21]   M. Y. Suberu, M. W. Mustafa, N. Bashir, “Energy storage systems for renewable energy power sector integration and mitigation of intermittency,” Renewable Sustainable Energy Rev., vol. 35, pp. 499-514, 2014.
[22]   H. Zhao, Q. Wu, S. Hu, H. Xu, C. N. Rasmussen, “Review of energy storage system for wind power integration support,” Appl. Energy, vol. 137, pp. 545-553, 2015.
[23]   H. A. Aalami, M. P. Moghaddam, G. R. Yousefi, “Modeling and prioritizing demand response programs in power markets,” Electr. Power Syst. Res., vol. 80, no. 4, pp. 426-435, 2010.
[24]   H. A. Aalami, M. P. Moghaddam, G. R. Yousefi, “Demand response modelling considering interruptib-le/curtailable loads and capacity market programs,” Appl. Energy, vol. 87, no. 1, pp. 243-250, 2010.  
[25]   H. A. Aalami, M. P. Moghaddam, G. R. Yousefi, “Evaluation of nonlinear models for time-based rates demand response programs,” Int. J. Electric. Power Energy Syst., vol. 65, pp. 282-290, 2015.
[26]   M. Nikzad, B. Mozafari, M. Bashirvand, S. Solaymani, A. M. Ranjbar, “Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index”, Energy, vol. 41, no. 1, pp. 541-548, 2012.
[27]   A. Abdollahi, M. P. Moghaddam, M. Rashidinejad, M. K. Sheikh-El-Eslami, “Investigation of economic and environmental-driven demand response measures incorporating UC,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 12-25, 2012.
[28]   M. Parvania, M. Fotuhi-Firuzabad, M. Shahidehpour, “Assessing impact of demand response in emission-constrained environments,” Proc. of the IEEE Power and Energy Society General Meeting, pp. 1-6, 2011.
[29]   The IEEE reliability test system-1996. IEEE Trans. Power Syst., vol. 14, pp. 1010-1020, 1999.
[30]   H. A. Aalami, S. Nojavan, “Energy storage system and demand response program effects on stochastic energy procurement of large consumers considering renewable generation,” IET Gener. Transm. Distrib., vol. 10, no. 1, pp. 107-114, 2016.
Volume 4, Issue 2
December 2016
Pages 117-131
  • Receive Date: 15 May 2015
  • Revise Date: 05 April 2016
  • Accept Date: 25 December 2016
  • First Publish Date: 25 December 2016