Optimal Design of Residential Microgrids with Regard to Fault Occurrence and Possibility of Power Outage

Subject Areas : Renewable energy

Mehrdad Movahedpour ¹ ( Department of Power Engineering, Faculty of Engineering, Yasooj Branch, Islamic Azad University, )
Sirus Mohammadi ² ( Department of Electrical Engineering, Gachsaran Branch, Islamic Azad University, Gachsaran, Iran )
Mohammadjavad Kiani ³ ( Assistant Professor - Department of Power Engineering, Faculty of Engineering, Branch, Islamic Azad University, Yasooj, Department of Power Engineering, Faculty of Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran )
Taher Niknam ⁴ ( Department of Electronic and Electrical Engineering, Shiraz University of Technology, Modares, Shiraz, Iran. )
Mahmoud Zadehbagheri ⁵ ( 5Assistant Professor Department of Power Engineering, Faculty of Engineering, Branch, Islamic Azad University, Yasooj, Department of Power Engineering, Faculty of Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran. )

Received: 2019-07-12 Accepted : 2019-10-17 Published : 2019-11-06

Keywords: Energy storage, residential grids, generation units, load response programs, colony optimization algorithm,

Abstract :

One of the issues which has attracted a lot of attention in the power grid in recent years is the emergence of microgrids. An optimized microgrid design includes choosing the best combination of the available options (distributed generation units, energy storage systems, and load response programs) to supply the microgrid so that the total costs of the microgrid development plan is minimized. In this article, a comprehensive modeling has been conducted for the problem of optimal design of residential microgrids considering the renewable distributed generation units, energy storage systems and controllable loads. This model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models for them has been chosen. In order to find the optimal solution, the problem of microgrid design is modeled as an optimization problem with the goal of minimizing the total costs of the microgrid development plan and the optimal response is determined via ant colony optimization algorithm.

References:

[1] Z. Wang, B. Chen, J. Wang, J. Kim, M. M. Begovic, “Robust optimization based optimal DG placement in microgrids”, IEEE Trans. on Smart Grid, Vol. 5, No. 5, pp. 2173 – 2182, Sep. 2014 (doi:10.1109/TSG.2014.2321748).

[2] S. Mizani, A. Yazdani, “Design and operation of a remote microgrid”, Proceeding of the IEEE/IECON, Porto, Portugal , Nov. 2009 (doi:10.1109/IECON.2009.5414925).

[3] J. P. Fossati, A. Galarza, A. Martín-Villate, L. Fontan, “A method for optimal sizing energy storage systems for microgrids”, Renewable Energy, Vol. 77,  pp. 539-549, May. 2015 (doi:10.1016/j.renene.2014.12.039).

[4] C. Smith, J. Burrows, E. Scheier, A. Young, J. Smith, T. Young, S. H. Gheewala, “Comparative life cycle assessment of a thai island's diesel/PV/wind hybrid microgrid”, Renewable Energy, Vol. 80, pp. 85-100, Aug. 2015 (doi:10.1016/j.renene.2015.01.003).

[5] W. W. Weaver; R. D. Robinett, G. G. Parker, D. G. Wilson, “Energy storage requirements of dc microgrids with high penetration renewables under droop control”, International Journal of Electrical Power and Energy Systems, Vol. 68, pp. 203–209, June 2015 (doi:10.1016/j.ijepes.2014.12.070).

[6] M. Lee, D. Soto, V. Modi, “Cost versus reliability sizing strategy for isolated photovoltaic micro-grids in the developing world”, Renewable Energy, Vol. 69, pp. 16-24, Sep. 2014 (doi:10.1016/j.renene.2014.03.019).

[7] A. Zidan, H.A. Gabbar, A. Eldessouky, “Optimal planning of combined heat and power systems within microgrids”, Energy, Vol. 93, pp. 235-244, Dec. 2015 (doi:10.1016/j.energy.2015.09.039).

[8] P. Moutis, S. Skarvelis-Kazakos, M. Brucoli, “Decision tree aided planning and energy balancing of planned community microgrids”, Applied Energy, Vol. 161, pp. 197–205, Jan. 2016 (doi:/10.1016/j.apenergy.2015.10.002).

[9] S. Li, H. He,    Y. Chen, M. Huang, C. Hu, “Optimization between the PV and the retired EV battery for the residential microgrid application”, Energy Procedia, Vol. 75, pp. 1138-1146, Aug. 2015 (doi:10.1016/j.egypro.2015.07.537).

[10] T.M. Priya, V. Sanjana, B. Gohila, R. Lavanya, A. Anbazhagan, M. Veerasundaram, “Design and analysis of a sustainable LV residential microgrid”, Procedia Technology, Vol. 21, pp. 139-146, 2015 (doi:10.1016/j.protcy.2015.10.081).

[11] S. Kahrobaee, S. Asgarpoor, W. Qiao, “Optimum sizing of distributed generation and storage capacity in smart households”, IEEE Trans. on Smart Grid, Vol. 4, No. 4, pp. 1791–1801, Dec. 2013 (doi:10.1109/TSG.2013.2278783).

[12] A. Arabali, M. Ghofrani, M. Etezadi-Amoli, M. S. Fadali, “Stochastic performance assessment and sizing for a hybrid power system of solar/wind/energy storage”, IEEE Trans. on Sustain. Energy, Vol. 5, No. 2, pp. 363–371, April 2014 (doi:10.1109/TSTE.2013.2288083).

[13] L. Göransson, S. Karlsson, F. Johnsson, “Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system”, Energy Policy, Vol. 38, No. 10, pp. 5482–5492, Oct. 2010 (doi:10.1016/j.enpol.2010.04.001).

[14] Q. Zhang, T. Tezuka, K. N. Ishihara, B. C. Mclellan, “Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai area, Japan,” Renew. Energy, Vol. 44, pp. 99–108, Aug. 2012 (doi:10.1016/j.renene.2012.01.003).

[15] N. Juul, P. Meibom, “Optimal configuration of an integrated power and transport system”, Energy, Vol. 36, No. 5, pp. 3523–3530, May 2011 (doi:10.1016/j.energy.2011.03.058).

[16] C. K. Ekman, “On the synergy between large electric vehicle fleet and high wind penetration—An analysis of the Danish case”, Reneable. Energy, Vol. 36, No. 2, pp. 546–553, Feb. 2011 (doi:10.1016/j.renene.2010.08.001).

[17] A. Botterud, Z. Zhou, J. Wang, J. Sumaili, H. Keko, J. Mendes, R. J. Bessa, V. Miranda, “Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: A case study of Illinois”, IEEE Trans. on Sustainable Energy, Vol. 4, No. 1, pp. 250-261, Jan. 2013 (doi:10.1109/TSTE.2012.2215631).

[18] Y. Guo, M. Pan, Y. Fang, P. P. Khargonekar, “Decentralized coordination of energy utilization for residential households in the smart grid”, IEEE Trans. on Smart Grid, Vol. 4, No. 3, pp. 1341–1350, Sep. 2013 (doi:10.1109/TSG.2013.2268581).

[19] N. Kunwar, K. Yash, R. Kumar, “Area-load based pricing in DSM through ANN and heuristic scheduling”, IEEE Trans. on Smart Grid, Vol. 4, No. 3, pp. 1275–1281, Sep. 2013 (doi:10.1109/TSG.2013.2262059).

[20] E. Matallanas, M. Castillo-Cagigal, A. Gutiérrez, F. Monasterio-Huelin, E.Caamaño-Martín, D.Masa, J. Jiménez-Leube, “Neural network controller for active demand-side management with PV energy in the residential sector”, Applied Energy, Vol. 91, No. 1, pp. 90–97, Mar. 2012 (doi:10.1016/j.apenergy.2011.09.004).

[21] A.-H. Mohsenian-Rad and A. Leon-Garcia, “Optimal residential load control with price prediction in real-time electricity pricing environments”, IEEE Trans. on Smart Grid, Vol. 1, No. 2, pp. 120–133, Sep. 2010 (doi:10.1109/TSG.2010.2055903).

[22] A.-H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, A. Leon-Garcia, “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid,” IEEE Trans.on  Smart Grid, Vol. 1, No. 3, pp. 320–331, Dec. 2010 (doi:10.1109/TSG.2010.2089069).

[23] A. Molderink, V. Bakker, M. G. C. Bosman, J. L. Hurink, G. J. M. Smit, “Management and control of domestic smart grid technology”, IEEE Trans. Smart Grid, Vol. 1, No. 2, pp. 109–119, Sep. 2010 (doi:10.1109/TSG.2010.2055904).

[24] M. Vasirani, R. Kota, R. L. G. Cavalcante, S. Ossowski, N. R. Jennings, “An agent-based approach to virtual power plants of wind power generators and electric vehicles”, IEEE Trans.on  Smart Grid, Vol. 4, No. 3, pp. 1314–1322, Sep. 2013 (doi:10.1109/TSG.2013.2259270).

[25] R. Atia, N. Yamada, “Sizing and analysis of renewable energy and battery systems in residential microgrids”, IEEE Trans. on Smart Grid, Vol. 7, No. 3, pp. 1204-1213, May 2016 (doi:10.1109/TSG.2016.2519541).

[26] S. Mohammadi, S. Soleymani, B. Mozafari, “Scenario-based stochastic operation management of microgrid including wind, photovoltaic, micro-turbine, fuel cell and energy storage devices”, Electrical Power and Energy Systems, Vol. 54, pp. 525-535, Jan. 2014 (doi:10.1016/j.ijepes.2013.08.004).

[27] E. Hajipour, M. Bozorg, M. Fotuhi-Firuzabad, “Stochastic capacity expansion planning of remote microgrids with wind farms and energy storage”, IEEE Trans. on Sustainable Energy, Vol. 6, No. 2, pp. 491-498, Apr. 2015 (doi:10.1109/TSTE.2014.2376356).

[28] W. Sheng, K.Y. Liu, X. Meng, X. Ye, Y. Liu, “Research and practice on typical modes and optimal allocation method for PV-Wind-ES in Microgrid”, Electric Power Systems Research, Vol. 120, pp. 242-255, March 2015 (doi:10.1016/j.epsr.2014.02.011).

[29] A. Kavousi-Fard, A. Khodaei, “Efficient integration of plug-in electric vehicles via reconfigurable microgrids”, Energy, Vol. 111, pp. 653-663, Sep. 2016 (doi:10.1016/j.energy.2016.06.018).

[30] Chan S. Park, “Fundamentals of Engineering Economics”, Pearson Education Inc., 2004.

[31] S. Pazouki, A. Mohsenzadeh, S. Ardalan, M. R. Haghifam, “Optimal place, size, and operation of combined heat and power in multi carrier energy networks considering network reliability, power loss, and voltage profile”, IET Generation, Transmission and Distribution, Vol. 10, No. 7, pp. 1615-1621, May. 2016 (doi:10.1049/iet-gtd.2015.0888).

[32] J. Jannati, D. Nazarpour, “Optimal energy management of the smart parking lot under demand response program in the presence of the electrolyser and fuel cell as hydrogen storage system”, Energy Conversion and Management, Vol. 138, pp. 659-669, April 2017 (doi:10.1016/j.enconman.2017.02.030).

[33] Y. Riffonneau, S. Bacha, F. Barruel, S. Ploix, “Optimal power flow management for grid connected PV systems with batteries”, IEEE Trans. on Sustainable Energy, Vol. 2, No. 3, pp. 309–320, July 2011 (doi: 10.1109/TSTE.2011.2114901).

[34] T. Liao, T. Stützle, M. A. M. Oca, M. Dorigo, "A unified ant colony optimization algorithm for continuous optimization", European Journal of Operational Research, Vol. 234, No. 3, pp. 597-609, May 2014 (doi: 10.1016/j.ejor.2013.10.024).