Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Moradinegade Dizqah, Arash
Languages: English
Types: Doctoral thesis
Subjects: H600
Due to substantial generation and demand fluctuations in stand-alone green micro-grids, energy management strategies (EMSs) are becoming essential for the power\ud sharing purpose and regulating the microgrids voltage. The classical EMSs track the maximum power points (MPPs) of wind and PV branches independently and rely on batteries, as slack terminals, to absorb any possible excess energy. However, in order to protect batteries from being overcharged by realizing the constant current-constant voltage (IU) charging regime as well as to consider the wind turbine operational constraints, more flexible multivariable and non-linear strategies, equipped with a power curtailment feature, are necessary to control microgrids.\ud \ud This dissertation work comprises developing an EMS that dynamically optimises the operation of stand-alone dc microgrids, consisting of wind, photovoltaic (PV), and\ud battery branches, and coordinately manage all energy flows in order to achieve four control objectives: i) regulating dc bus voltage level of microgrids; ii) proportional power sharing between generators as a local droop control realization; iii) charging batteries as close to IU regime as possible; and iv) tracking MPPs of wind and PV branches during their normal operations.\ud \ud Non-linear model predictive control (NMPC) strategies are inherently multivariable and handle constraints and delays. In this thesis, the above mentioned EMS is developed as a NMPC strategy to extract the optimal control signals, which are duty cycles of three DC-DC converters and pitch angle of a wind turbine.\ud \ud Due to bimodal operation and discontinuous differential states of batteries, microgrids belong to the class of hybrid dynamical systems of non-Filippov type. This\ud dissertation work involves a mathematical approximation of stand-alone dc microgrids as complementarity systems (CSs) of Filippov type. The proposed model is used to develop NMPC strategies and to simulate microgrids using Modelica.\ud \ud As part of the modelling efforts, this dissertation work also proposes a novel algorithm to identify an accurate equivalent electrical circuit of PV modules using both\ud standard test condition (STC) and nominal operating cell temperature (NOCT) information provided by manufacturers. Moreover, two separate stochastic models are presented for hourly wind speed and solar irradiance levels.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [9] T. Burton, N. Jenkins, D. Sharpe, and E. Bossanyi, Wind Energy Handbook. West Sussex, UK: John Wiley & Sons, 2 ed., 2011.
    • [10] A. Meharrar, M. Tioursi, M. Hatti, and A. B. Stambouli, “A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system,” Expert Systems with Applications, vol. 38, no. 6, pp. 7659 - 7664, 2011.
    • [11] C. T. Pan, and Y. L. Juan, “A Novel Sensorless MPPT Controller for a HighEfficiency Microscale Wind Power Generation System,” Energy Conversion, IEEE Transactions on, vol. 25, pp. 207 -216, march 2010.
    • [12] H. Li, and Z. Chen, “Overview of different wind generator systems and their comparisons,” Renewable Power Generation, vol. 2, no. 2, pp. 123-138, 2008.
    • [13] N. Mohan, T. M. Undeland, and W. P. Robbins, Power electronics: converters, applications, and design. New York: John Wiley & Sons, 2 ed., 1995.
    • [14] J. H. Su, J. J. Chen, and D. S. Wu, “Learning Feedback Controller Design of Switching Converters Via MATLABSIMULINK,” IEEE Transactions on Education, vol. 45, pp. 307-315, 2002.
    • [15] R. D. Middledbrook,and S. Cuk, “A General Unified Approach to Modelling Switching-Converter Power Stages,” in Proc. of IEEE Power Electronics Specialist conference, 1976.
    • [16] S. Mariethoz, S. Almer, M. Baja, A. G. Beccuti, D. Patino, A. Wernrud, J. Buisson, H. Cormerais, T. Geyer, H. Fujioka, U. T. Jonsson, C. Y. Kao, M. Morari, G. Papafotiou, A. Rantzer, P. Riedinger, “Comparison of Hybrid Control Techniques for Buck and Boost DC-DC converters,” IEEE Transactions on Control Systems Technology, vol. 18, pp. 1126-1145, 2010.
    • [29] M. Lazar, Model predictive control of hybrid systems: Stability and robustness. PhD thesis, Eindhoven University of Technology, Netherland, 2006.
    • [30] R. Goebel, R. G. Sanfelice, and A. R. Teel, “Hybrid dynamical systems,” Control Systems, IEEE, vol. 29, no. 2, pp. 28-93, 2009.
    • [31] W. P. M. H. Heemels, and B. Brogliato, “The Complementarity Class of Hybrid Dynamical Systems,” European Journal of Control, vol. 9, no. 2-3, pp. 322 - 360, 2003.
    • [32] A. F. Filippov, and F. M. Arscott, Differential Equations with Discontinuous Righthand Sides: Control Systems. Mathematics and its Applications, Kluwer Academic Publishers, 1988.
    • [33] W. S. Lin, and C. H. Zheng, “Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method,” Journal of Power Sources, vol. 196, no. 6, pp. 3280 - 3289, 2011.
    • [34] Y. Haitao, Z. Yulan, S. Zechang, and W. Gang, “Model-based power control strategy development of a fuel cell hybrid vehicle,” Journal of Power Sources, vol. 180, no. 2, pp. 821 - 829, 2008.
    • [35] S. J. Moura, D. S. Callaway, H. K. Fathy, and J. L. Stein, “Tradeoffs between battery energy capacity and stochastic optimal power management in plug-in hybrid electric vehicles,” Journal of Power Sources, vol. 195, no. 9, pp. 2979 - 2988, 2010.
    • [36] D. Feroldi, M. Serra, and J. Riera, “Energy Management Strategies based on efficiency map for Fuel Cell Hybrid Vehicles,” Journal of Power Sources, vol. 190, no. 2, pp. 387 - 401, 2009.
    • [37] A. Nosrat, and J. M. Pearce, “Dispatch strategy and model for hybrid photovoltaic and trigeneration power systems,” Applied Energy, vol. 88, no. 9, pp. 3270 - 3276, 2011.
    • [38] H. Beltran, E. Bilbao, E. Belenguer, I. Etxeberria-Otadui, and P. Rodriguez, “Evaluation of Storage Energy Requirements for Constant Production in PV Power Plants,” Industrial Electronics, IEEE Transactions on, vol. 60, no. 3, pp. 1225-1234, 2013.
    • [39] D. Ipsakis, S. Voutetakis, P. Seferlis, F. Stergiopoulos, and C. Elmasides, “Power management strategies for a stand-alone power system using renewable energy sources and hydrogen storage,” International Journal of Hydrogen Energy, vol. 34, no. 16, pp. 7081 - 7095, 2009.
    • [40] S. H. Karaki, R. B. Chedid, and R. Ramadan, “Probabilistic performance assessment of autonomous solar-wind energy conversion systems,” Energy Conversion, IEEE Transactions on, vol. 14, pp. 766 -772, sep 1999.
    • [41] M.Y. Sulaiman, W.M. H. Oo, M. A. Wahab, and A. Zakaria, “Application of beta distribution model to Malaysian sunshine data,” Renewable Energy, vol. 18, no. 4, pp. 573 - 579, 1999.
    • [42] A. Mellit, H. Mekki, A. Messai, and S.A. Kalogirou, “FPGA-based implementation of intelligent predictor for global solar irradiation, Part I: Theory and simulation,” Expert Systems with Applications, vol. 38, no. 3, pp. 2668 - 2685, 2011.
    • [43] A. Mefti, M. Y. Bouroubi, and A. Adane, “Generation of hourly solar radiation for inclined surfaces using monthly mean sunshine duration in Algeria,” Energy Conversion and Management, vol. 44, no. 19, pp. 3125 - 3141, 2003.
    • [44] S. S. Sanz, A. M. P. Bellido, E. G. O. Garcia, A. P. Figueras, L. Prieto, and F. Correoso, “Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks,” Neurocomputing, vol. 72, no. 4-6, pp. 1336 - 1341, 2009.
    • [45] W. G. Fruh, “Long-term wind resource and uncertainty estimation using wind records from Scotland as example,” Renewable Energy, vol. 50, no. 0, pp. 1014 - 1026, 2013.
    • [46] M. G. Villalva, J. R. Gazoli, and E. R. Filho, “Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays,” IEEE Transactions on Power Electronics, vol. 24, pp. 1198-1208, 2009.
    • [47] M. K. Deshmukh, and S. S. Deshmukh, “Modelling of Hybrid Renewable Energy System,” Renewable And Sustainable Energy Reviews, vol. 12, pp. 235- 249, 2008.
    • [48] J. Mart´ınez, and A. Medina, “A State Space Model for the Dynamic Operation Representation of Small-Scale Wind-Photovoltaic Hybrid Systems,” Journal of Renewable Energy, vol. 35, pp. 1159-1168, 2010.
    • [49] M. Ceraolo, “New dynamical models of lead-acid batteries,” Power Systems, IEEE Transactions on, vol. 15, pp. 1184 -1190, nov 2000.
    • [50] A. Gupta, R.P. Saini, and M.P. Sharma, “Modelling of hybrid energy system: Part II: Combined dispatch strategies and solution algorithm,” Renewable Energy, vol. 36, no. 2, pp. 466 - 473, 2011.
    • [51] X. Hu, S. Li, and H. Peng, “A comparative study of equivalent circuit models for Li-ion batteries,” Journal of Power Sources, vol. 198, no. 0, pp. 359 - 367, 2012.
    • [52] K. Mamadou, T. M. P. Nguyen, E. L. Potteau, C. Glaize, and J. Alzieu, “New accelerated charge methods using early destratification applied on flooded lead acid batteries,” Journal of Power Sources, vol. 196, no. 8, pp. 3981 - 3987, 2011.
    • [53] F. J. Lin, M. S. Huang, P. Y. Yeh, H. C. Tsai, and C. H. Kuan, “DSP-Based Probabilistic Fuzzy Neural Network Control for LiIon Battery Charger,” Power Electronics, IEEE Transactions on, vol. 27, pp. 3782 -3794, aug. 2012.
    • [54] R. Carter, A. Cruden, and P. J. Hall, “Optimizing for Efficiency or Battery Life in a Battery/Supercapacitor Electric Vehicle,” Vehicular Technology, IEEE Transactions on, vol. 61, pp. 1526 -1533, may 2012.
    • [55] F. Locment, M. Sechilariu, and I. Houssamo, “DC Load and Batteries Control Limitations for Photovoltaic Systems Experimental Validation,” Power Electronics, IEEE Transactions on, vol. 27, pp. 4030 -4038, sept. 2012.
    • [56] S. Grillo, M. Marinelli, S. Massucco, and F. Silvestro, “Optimal Management Strategy of a Battery-Based Storage System to Improve Renewable Energy Integration in Distribution Networks,” Smart Grid, IEEE Transactions on, vol. 3, pp. 950 -958, june 2012.
    • [57] F. Valenciaga, and P. F. Puleston, “Supervisor control for a stand-alone hybrid generation system using wind and photovoltaic energy,” Energy Conversion, IEEE Transactions on, vol. 20, pp. 398 - 405, june 2005.
    • [59] X. Liu, and W. Xu, “Economic Load Dispatch Constrained by Wind Power Availability: A Here-and-Now Approach,” Sustainable Energy, IEEE Transactions on, vol. 1, pp. 2 -9, april 2010.
    • [60] M. Khalid, and A.V. Savkin, “A model predictive control approach to the problem of wind power smoothing Aˆ with controlled battery storage,” Renewable Energy, vol. 35, no. 7, pp. 1520 - 1526, 2010.
    • [61] W. H. Kwon, and S. H. Han, “Optimal Controls on Finite and Infinite Horizons: A Review,” in Receding Horizon Control, Advanced Textbooks in Control and Signal Processing, pp. 17-82, Springer London, 2005.
    • [62] L. Gru¨ne, and J. Pannek, Nonlinear Model Predictive Control: Theory and Algorithms. Communications and control engineering, Springer, 2011.
    • [63] R. Findeisen, and F. Allgo¨wer, “An Introduction to Nonlinear Model Predictive,” in Control, 21st Benelux Meeting on Systems and Control, Veidhoven, pp. 1- 23, 2002.
    • [67] E. F. Camacho, and C. Bordons, Model Predictive Control. Advanced Textbooks in Control and Signal Processing Series, SpringerVerlag GmbH, 2004.
    • [68] L.T. Biegler, and V.M. Zavala, “Large-scale nonlinear programming using IPOPT: An integrating framework for enterprise-wide dynamic optimization,” Computers & Chemical Engineering, vol. 33, no. 3, pp. 575 - 582, 2009.
    • [69] M. Mahmood, and P. Mhaskar, “Lyapunov-based model predictive control of stochastic nonlinear systems,” Automatica, vol. 48, no. 9, pp. 2271 - 2276, 2012.
    • [70] M. Diehl, R. Amrit, and J. B. Rawlings, “A Lyapunov Function for Economic Optimizing Model Predictive Control,” Automatic Control, IEEE Transactions on, vol. 56, no. 3, pp. 703-707, 2011.
    • [71] M. Heidarinejad, J. Liu, and P. D. Christofides, “Economic model predictive control of switched nonlinear systems,” Systems & Control Letters, vol. 62, no. 1, pp. 77 - 84, 2013.
    • [72] S. Boyd,L. Vandenberghe, Convex Optimization. New York: Cambridge University Press, 2004.
    • [73] B. Lincoln, and A. Rantzer, “Relaxing dynamic programming,” Automatic Control, IEEE Transactions on, vol. 51, no. 8, pp. 1249-1260, 2006.
    • [74] N. Kim, S. Cha, and H. Peng, “Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle,” Control Systems Technology, IEEE Transactions on, vol. 19, no. 5, pp. 1279-1287, 2011.
    • [75] C. Kirches, L. Wirsching, H.G. Bock, and J.P. Schlo¨der, “Efficient direct multiple shooting for nonlinear model predictive control on long horizons,” Journal of Process Control, vol. 22, no. 3, pp. 540 - 550, 2012.
    • [76] R. A. Bartlett, A. Wachter, and L. T. Biegler, “Active set vs. interior point strategies for model predictive control,” in American Control Conference, 2000. Proceedings of the 2000, vol. 6, pp. 4229-4233 vol.6, 2000.
    • [77] J. Andersson, J. A˚kesson, and M. Diehl, “Dynamic optimization with CasADi,” in Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, pp. 681-686, 2012.
    • [78] R. Neidinger, “Introduction to Automatic Differentiation and MATLAB ObjectOriented Programming,” SIAM Review, vol. 52, no. 3, pp. 545-563, 2010.
    • [79] A. Verma, “An introduction to automatic differentiation,” Current Science, vol. 78, pp. 804-807, Apr. 2000.
    • [80] A. Griewank, and A. Walther, “Introduction to Automatic Differentiation,” PAMM, vol. 2, no. 1, pp. 45-49, 2003.
    • [81] J. Andersson, J. A˚kesson, and Moritz Diehl, “CasADi - A Symbolic Package for Automatic Differentiation and Optimal Control,” in Recent Advances in Algorithmic Differentiation (S. Forth, P. Hovland, E. Phipps, J. Utke, and A. Walther, eds.), vol. 87 of Lecture Notes in Computational Science and Engineering, pp. 297-307, Springer Berlin Heidelberg, 2012.
    • [82] B. Houska, H. J. Ferreau, and M. Diehl, “An auto-generated real-time iteration algorithm for nonlinear fMPCg in the microsecond range,” Automatica, vol. 47, no. 10, pp. 2279 - 2285, 2011.
    • [88] J. J. Justo, F. Mwasilu, J. Lee, and J. W. Jung, “AC-microgrids versus DCmicrogrids with distributed energy resources: A review,” Renewable and Sustainable Energy Reviews, vol. 24, no. 0, pp. 387 - 405, 2013.
    • [89] S. Schuler, D. Schlipf, P. W. Cheng, and F. Allgo¨wer, “`1-Optimal Control of Large Wind Turbines,” Control Systems Technology, IEEE Transactions on, vol. 21, no. 4, pp. 1079-1089, 2013.
    • [90] W. Qi, J. Liu, and P. D. Christofides, “Distributed Supervisory Predictive Control of Distributed Wind and Solar Energy Systems,” Control Systems Technology, IEEE Transactions on, vol. 21, no. 2, pp. 504-512, 2013.
    • [91] L. Gkatzikis, I. Koutsopoulos, T. Salonidis, “The Role of Aggregators in Smart Grid Demand Response Markets,” IEEE Journal on Selected Areas in Communications, vol. to apper, 2013.
    • [92] J. J. Soon, and K. S. Low, “Photovoltaic Model Identification Using Particle Swarm Optimization With Inverse Barrier Constraints,” IEEE Transactions on Power Electronics, vol. 27, pp. 3975-3983, 2012.
    • [98] G. W. Paltridge, and C. M. R. Platt, “Radiative Processes in Meteorology and Climatology,” Developments in Atmospheric Science, vol. 5, 1976.
    • [99] U. M. Office, “MIDAS Land Surface Stations data (1853-current).” NCAS British Atmospheric Data Centre, 2006-2013.
    • [100] J. L. Agorreta, M. Borrega, J. Lopez, and L. Marroyo, “Modeling and Control of N-Paralleled Grid-Connected Inverters With LCL Fitler Coupled Due to Grid Impedance in PV Plants,” IEEE Transactions on Power Electronics, vol. 26, no. 3, pp. 770-785, 2011.
    • [101] P. E. Kakosimos, and A. G. Kladas, “Implementation of Photovoltaic Array MPPT Through Fixed Step Predictive Control Technique,” Renewable Energy, vol. 36, pp. 2508-2514, 2011.
    • [108] K. Ishaque, Z. Salam, and H. Taheri, “Simple, Fast and Accurate Two-Diode Model for Photovoltaic Modules,” Journal of Solar Energy Materials and Solar Cells, vol. 95, pp. 586-594, 2011.
    • [109] G. Walker, “Evaluating MPPT Converter Topologies Using a Matlab PV Model,” Journal of Electrical and Electronics Engineering, vol. 21, pp. 45-55, 2001.
    • [110] A. N. Celik, and N. Acikgoz, “Modelling and Experimental Verification of the Operating Current of Mono-Crystalline Photovoltaic Modules Using Four- and Five-Parameter Models,” Journal of Applied Energy, vol. 84, pp. 1-15, 2007.
    • [111] D. S. H. Chan, and J. C. H. Phang, “Analytical Methods for the Extraction of Solar-Cell Single- and Double-Diode Model Parameters from I-V Charactristics,” IEEE Transactions on Electron Devices, vol. ED-34, pp. 286-293, 1987.
    • [112] W. De Soto, S. A. Klein, and W. A. Beckman, “Improvement and Validation of a Model for Photovoltaic Array Performance,” Journal of Solar Energy, vol. 80, pp. 78-88, 2006.
    • [113] D. Sera, R. Teodorecu, and P. Rodriguez, “PV Panel Model Based on Datasheet Values,” in Proc. IEEE Int. Symp. Electronic, pp. 2392-2396, 2007.
    • [114] A. Chaterjee, A. Keyhani, and D. Kapoor, “Identification of Photovoltaic Source Models,” IEEE Transactions on Energy Conversion, vol. 80, pp. 78-88, 2011.
    • [115] R. Kardi, H. Andrei, J. P. Gaubert, T. Ivanovici, G. Champenois, and P. Andrei, “Modeling of the Photovoltaic Cell Circuit Parameters for Optimum Connection Model and Real-Time Enulator with Partial Shadow Conditions,” Journal of Energy, vol. 42, pp. 57-67, 2012.
    • [116] M. Zagrouba, A. Sellami, M. Bouaicha, and M. Ksouri, “Identification of PV Solar Cells and Modules Parameters Using the Genetic Algorithms: Application to Maximum Power Extraction,” Journal of Solar Energy, vol. 84, pp. 860-866, 2010.
    • [117] F. M. Petcut, and T. L. Dragomir, “Solar Cell Parameter Identification Using Genetic Algorithms,” Journal of Control Engineering and Applied Informatics, vol. 12, pp. 30-37, 2010.
    • [148] H. Kakigano, Y. Miura, and T. Ise, “Distribution Voltage Control for DC Microgrids Using Fuzzy Control and Gain-Scheduling Technique,” Power Electronics, IEEE Transactions on, vol. 28, no. 5, pp. 2246-2258, 2013.
    • [149] P. C. Loh, and F. Blaabjerg, “Autonomous control of distributed storages in microgrids,” in Power Electronics and ECCE Asia (ICPE ECCE), 2011 IEEE 8th International Conference on, pp. 536-542, 2011.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article