Optimized Planning for Hybrid Micro-grid in Grid-connected Mode

Ashkan Nadimi, Farid Adabi

Abstract


The emergence of the Distributed Generation (DG) units along with their application in the distribution system level has led to the establishment of microgrids. microgrids are a part of the distribution network in which, in addition to the loads, there are microsources operating in two modes, i.e. grid-connected mode and island AKA standalone mode. In the grid-connected mode, the load is supplied through local DG units, and if necessary, power is exchanged with the upstream grid, concerned as well.  Considering the development of DG units technology in recent years which in turn, has led to the expansion of microgrid concept  , microgrid planning meaning determining the capacities of local DG units assumes an extraordinary significance, taking the technical, economic and environmental considerations into account. This research tries to study the optimized planning for a grid-connected hybrid microgrid. The case study was conducted in Razi University in Kermanshah, Iran. Due to the stochastic behavior of renewable energies based DG units, the uncertainties about the amount of generated power from these resources are considered using appropriate probability density functions. The problem is a Mixed Integer Non-linear Program (MINLP), to be solved by means of GAMS software. In order to bring about optimized results, microgrid simulation is processed in assorted scenarios, both in probabilistic and deterministic modes. In the end, the results are compared to those resulted from Homer Energy software. 


Keywords


Optimized Planning, Hybrid Microgrid, Probability Functions, Uncertainty, Distributed Generation

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References


Balaguer IJ, Qin Lei, Shuitao Yang, Supatti U, Fang Zheng Peng.“Control for gridconnected and intentional islanding operations of distributed power generation. IEEE Trans Ind Electron;58(1):147–57. 2011.

Paul Sheaffer, “Interconnection of Distributed Generation to Utility Systems, Recommendations for Technical Require“, Procedures and Agreements,sep 2011.

W. El-Khattam, K. Bhattacharya, Y. Hegazy, and M. Salama, “Optimal investment planning for distributed generation in a competitive electricity market,†IEEE Trans. Power Syst., vol. 19, pp. 1674–1684, Aug. 2004.

Ipakchi A, AlbuyehF.Grid of the Future: Are We Ready to Transition to a Smart Grid.

IEEE Power and Energy Magazine, 52- 62; 2009.

] Mohamed FA, Koivo HN. System modelling and online optimal management of micro-grid using mesh adaptive direct search. Int J Electr Power Energy System ;32:398–407. 2010.

Lingfeng Wang and Chanan Singh, “PSO Based Multi-Criteria Optimum Design of A Grid- Connected Hybrid Power System With Multiple Renewable Sources of Energyâ€, Proceedings of the 2007 IEEE Swarm Intelligence Symposium SIS 2007.

Kellogg W, Nehrir MH, Venkataramanan G, Gerez V; “Optimal unit sizing for a hybrid wind/photovoltaic generating system“; Electric Power Systems Research 39:35-81996.

Kaabeche A, Belhamel M, Ibtiouen R; “Sizing optimization of grid independent hybrid photovoltaic/wind power generation system“; Energy;36:1214-22, 2011.

Borowy BS, Salameh ZM; “ Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system“; IEEE Transactions on Energy Conversion;11:367-75, 1996.

Ekren O, Ekren BY; “Size optimization of a PV/wind hybrid energy conversion system with battery storage using simulated annealing“; Applied Energy;87:592-8, 2010.

Hakimi SM, Moghaddas-Tafreshi SM; “Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran“;Renewable Energy;34:1855-62و2009.

Seeling-Hochmuth GC; “A combined optimisation concept for the design and operation strategy of hybrid-PV energy systems“; Solar Energy;61:77-87, 1997.

T. Senjyu, K. Shimabukuro, K. Uezato, and T. Funabashi, “A fast technique for unit commitment problem by extended priority list,†IEEE Trans. Power Syst., vol. 18, no. 2, pp. 882–888, May 2003.

J. M. Arroyo and A. J. Conejo, “A parallel repair genetic algorithm to solve the unit commitment problem,†IEEE Trans. Power Syst., vol. 17, pp. 1216–1224, Nov. 2002.

N.P. Padhy, “Unit commitment - a bibliographical surveyâ€, IEEE Trans. Power Syst. vol. 6, no. 3, pp. 1196-1205, 2004.

J. Medina, V. H. Quintana, and A. J. Conejo, “A clipping-off interior point technique for medium-term hydro-thermal coordination,†IEEE Trans. Power Syst., vol. 14, no. 1, pp. 266–273. 1999.

Zong Woo Geem*.“Size optimization for a hybrid photovoltaic–wind energy systemâ€. Electrical Power and Energy Systems ,448-451, 2012.

M. Sadeghi1 , M. Kalantar2; “Allocation of Solar Units to Reduce Annual Costs of the Distribution Systemâ€; IEEE Trans, vol. 978, no.1, pp.2803-2809, 2013.

Ango Sobu Non-Member, IEEE and Guohong Wu Member, IEEE; “ optimal operation planning method for isolated micro grid considering and load demand “ ; IEE pes isgt asia 1569536541;2012 .

A.T.D. Perera, D.M.I.J. Wickremasinghe, D.V.S. Mahindarathna, R.A. Attalage , K.K.C.K. Perera, E.M. Bartholameuz, “Sensitivity of internal combustion generator capacity in standalone hybrid energy systems “, Energy 39, 403-411,2012.

Dekker J, Nthontho M, Chowdhury S, Chowdhury S.P. “ Economic analysis of PV/diesel hybrid systems in different climate zones of South Africa“.Electrical Power and Energy Systems 2012.

Data available online at: http://eosweb.larc.nasa.gov/cgi-bin/sse/

J. Carta, P. Ramirez and S. Velazquez: “A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islandsâ€, Renewable and Sustainable Energy Reviews, 13:933 -955, 2009.

Kaldellis JK, Zafirakis D, Kondili E. “Optimum sizing of photovoltaic-energy storage systems for autonomous small islands“. Int J Electr Power Energy System ;32:24–36; 2010.

Data available online at: http:// www.rollsbattery.com.

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DOI (PDF): https://doi.org/10.20508/ijrer.v6i2.3586.g6810

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