Abstract
Micro-grid and standalone schemes are emerging as a viable mixed source of electricity due to interconnected costly central power plants and associated faults as well as brownouts and blackouts in additions to costly fuels. Micro-Grid (MG) is gaining very importance to avoid or decrease these problems. The objective of this paper is to design an optimal sizing and energy management scheme of an isolated MG. The MG is suggested to supply load located in El-shorouk Academy, Egypt between 30.119 latitudes and 31.605 longitudes. The components of the MG are selected and designed for achieving minimum Total Investment Cost (TIC) with CO2 emissions limitations. This is accomplished by a search and optimization MATLAB code used with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques. The use of Diesel Generators (DGs) is minimized by limiting the gaseous CO2 emissions as per targeted allowable amount. A comparison is accomplished for investigating the CO2 emissions constraints effects on the TIC in $/year and annual cost of energy in $/kWh. The obtained results verified and demonstrated that the designed MG configuration scheme is able to feed the energy entailed by the suggested load cost effectively and environmental friendly.
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References
Bansal, A. K., Kumar R., and Gupta, R. A., (2013).: Economic Analysis and Power Management of a Small Autonomous Hybrid Power System (SAHPS) Using Biogeography Based Optimization (BBO) Algorithm. IEEE Transactions on Smart Grid, 4 (1), 638-648.
Bilal, B., O., Sambou, V., Kebe, C. M. F., Ndiaye, P. A., and Ndongo, M., (2012).: Methodology to Size an Optimal Stand-Alone PV/wind/diesel/battery System Minimizing the Levelized cost of Energy and the CO2 Emissions. Energy Procedia14, 1636–1647.
Borowy, B. S., Salameh, Z. M., (1996).: Methodology for Optimally Sizing the Combination of a Battery Bank and PV Array in a Wind/PV Hybrid System. IEEE Transactions on Energy Conversions, 11 (2), 367-375.
Chedid, R., Rehman, S., (1997).: Unit sizing and control for hybrid wind-solar power systems. IEEE Transactions on Energy Conversion, 12, (1), 79-85.
El-Wakeel, A. S., El-Eyoun, A., Ellissy, K. M. and Abdel-hamed, A. M., (2015).: A Hybrid Bacterial Foraging-Particle Swarm Optimization Technique for Optimal 'Tuning of Proportional-Integral-Derivative Controller of a Permanent Magnet Brushless DC Motor. Electric Power Components and Systems, 43 (3), 309–319.
Govardhan M. D., and Roy, R., (2012).: Artificial Bee Colony based Optimal Management of Microgrid. 11th International Conference on Environment and Electrical Engineering (EEEIC).
Hassan, A., El-saadawi, M., Kandil M., and Saeed, M., (2015).: Economic Analysis of a Grid-Connected Hybrid Renewable System Supplying CIT Center at Mansoura University-Egypt. Journal of Electrical Engineering, 1-12.
Hassan, A., El-Saadawi, M., Kandil, M., and Saeed, M., (2016).: Modeling and optimization of a hybrid power system supplying RO water desalination plant considering CO2 emissions'', Desalination and Water Treatment, 57 (26) , 11972-11987.
Hassan, A., Saadawi, M., Kandil, M., and Saeed, M., (2015).: Modified particle swarm optimisation technique for optimal design of small renewable energy system supplying a specific load at Mansoura University. IET Renewable Power Generation, 9 (5), 474-483.
Hassanzadehfard, H., Tafreshi, S. M. M., Hakimi, S. M., (2011).: Optimal Sizing of an Islanded Micro-grid for an area in north-west Iran Using Particle Swarm Optimization Based on Reliability Concept. World Renewable Energy congress.
Huang, R., Wang, Y., Chu, C., Gadh, R., and Song, Y., (2014).: Optimal Configuration of Distributed Generation on Jeju Island Power Grid Using Genetic Algorithm: A Case Study. Journal of Communication Software and Systems, 10 (2), 135-144.
Kidmo, D. K., Danwe, R., Doka, S. Y., and Djongyang, N., (2015).: Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon. Revue des Energies Renouvelables, 18 (1), 105 – 125.
Mohamed, F. A., and Koivo, H. M., (2011).: Modelling and Environmental/Economic Power Dispatch of MicroGrid Using MultiObjective Genetic Algorithm Optimization. Fundamental and Advanced Topics in Wind Power, 20, DOI: 10.5772/19154.
Mohamed, F. A., and Koivo, N. H., (2007).: System Modelling and Online Optimal Management of MicroGrid with Battery Storage. International Conference on Renewable Energies and Power Quality (ICREPQ'07), Sevilla, Spain.
Noroozian, R., and Vahedi, H., (2010).: Optimal management of MicroGrid using Bacterial Foraging Algorithm'',18th Iranian Conference on Electrical Engineering,Isfahan, Iran.
Paudel, S., Shrestha, J. N., and Adhikari, M., (2011).: Optimization of Hybrid PV/Wind Power System for Remote Telecom Station. Tribhuvan University (TU), IEEE International Conference on Power and Energy Systems (ICPS).
Shi, W., Xie, X., Chu, C., and Gadh, R., (2014).: A Distributed Optimal Energy Management Strategy for Microgrids. IEEE SmartGridComm, Venice, Italy.
Solar Energy International, (2007).: Photovoltaic design and installation manual. New Society Publishers.
Tabatabaei, S. M., and Vahidi, B., (2013).: Optimum management of hybrid distributed generations in microgrid using bacterial foraging solution'', sCI.iNT.(lAHORE), 25 (3), 487-496.
Tito, M. S. R., Lie, T. T., and Anderson, T., (2015).: A Simple Sizing Optimization Method for Wind-Photovoltaic-Battery Hybrid Renewable Energy Systems", Proceedings of the 20th Electronics New Zealand Conference, Massey University, 8–12.
Wei, Z., (2007).: Simulation and optimum design of hybrid solar-wind and solar-wind-diesel power generation system. Ph. D Thesis, Hong Kong Polytechnic university.
Yang, H., Lu, L., Zhou, W., (2007).: A novel optimization sizing model for hybrid solar-wind energy power generation systems. Solar Energy, 81 (1), 76-84.
Yazdanpanah, M., (2014).: Modeling and sizing optimization of hybrid photovoltaic/wind power generation system. J. Ind. Eng. Int., 10, 1–14.
Zhang, D., (2013).: Optimal Design and Planning of Energy Microgrids. Ph. D Thesis, Department of Chemical Engineering University College London.
Zhou, W., Yang, H., Fang, Z., (2008).: Battery behavior prediction and battery working state analysis of a hybrid solar-wind power generation system. Renewable Energy, 33 (6), 1413-1423.
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