Optimal Planning and Implementation of Remote Areas Energy Provision’s System Considering Uncertainty

Document Type : -

Authors

1 Imam Hossein Comprehensive University

2 University of Tabriz

Abstract

In this paper, the optimal planning of renewable energy systems including the wind turbine photovoltaic solar panels and energy storage systems has been done for a sample garrison considering the weather condition’s uncertainty. The target case study is Tatavar1 garrison which has been located in Kermanshah province of Iran. Utilizing renewable based energy system for providing the energy demand of areas which are far from main grid is a suitable and practical suggestion. Consideration of uncertainty of energy production of these systems during the optimization studies affords more comprehensive and more practical results. The simulation results show that the using of renewable based energy systems caused considerable reduction of energy cost. Simulation results indicate that for case study of Tatavar 1, using the renewable energy system decrease the consumption of gasoil fuel about 87% and total cost up to 81% during the 5 years of optimization period, beside the eliminating the whole of load shedding hours. 

Keywords

References

[1]     Bahramara, S.; Parsa Moghaddam M.; Haghifam, M. R. “Optimal Planning of Hybrid Renewable Energy Systems Using HOMER: A Review”; Renew. Sust. Energ. Rev.  2016, 62, 609-620.
[2]     Chang, K.; Grace, L. “Optimal Design of Hybrid Renewable Energy Systems Using Simulation Optimization”; Simul. Model. Pract. Theor. 2015, 52, 40-51.
[3]     Ramli, M. A.; Hiendro, A.; Sedraoui, K.; Twaha, S. “Optimal Sizing of Grid-Connected Photovoltaic Energy System in Saudi Arabia”; Renew. Energ. 2015, 75, 489-495.
[4]     Che, L.; Zhang, X.; Shahidehpour, M.; Alabdulwahab, A.; Abusorrah, A. “Optimal Interconnection Planning of Community Microgrids with Renewable Energy Sources”; IEEE Trans. Smart Grid 2017, 8.3, 1054-1063.
[5]     Jung, J.; Villaran, M. “Optimal Planning and Design of Hybrid Renewable Energy Systems for Microgrids”; Renew. Sust. Energ. Rev. 2017, 75, 180-191.
[6]     Mohammadi, M.; Ghasempour, R.; Astaraei, F. R.; Ahmadi, E.; Aligholian, A.; Toopshekan, A. “Optimal Planning of Renewable Energy Resource for a Residential House Considering Economic and Reliability Criteria”; Int. J. Electr. Power Energy Syst. 2018, 96, 261-273.
[7]     Liu, Y.; Yu, S.; Zhu, Y.; Wang, D.; Liu, J. “Modeling, Planning, Application and Management of Energy Systems for Isolated Areas: A Review”; Renew. Sust. Energ. Rev. 2018, 82, 460-470.
[8]     Sadeghi, H; Abodollahi, A.; Mohammadian, M.; Rashidinejad, M. “Evaluating The Effects of Renewable Energy Resources from Passive Defence and Social Welfare perspectives in the Context of Expansion Planning”; Adv. Defence Sci. Technol. 2015, 6,71-86.
[9]     Khanzade, M. H.; Nabati Rad, M. “Energy Supply of Sensitive Areas as an Inverter-Based Microgrid During Stiff Power System Black Out”; Adv. Defence Sci. Technol. 2017, 8, 85-95.
[10]  Zaman, F.; Elsayed, S. M.; Ray, T.; Sarker, R. A. “Evolutionary Algorithms for Power Generation Planning with Uncertain Renewable Energy”; Energy 2016, 112, 408-419.
[11]  Zeng, B.; Zhang, J.; Yang, X.; Wang, J.; Dong, J.; Zhang, Y. “Integrated Planning for Transition to Low-Carbon Distribution System with Renewable Energy Generation and Demand Response”; IEEE Trans. Power Syst. 2014, 29, 1153-1165.
[12]  Soroudi, A.; Aien, M.; Ehsan, M. “A Probabilistic Modeling of Photo Voltaic Modulesand Wind Power Generation Impact on Distribution Networks”; IEEE Sys. J. 2012, 6, 254–259.
[13]  Ghaffarpour, R.; Jam, A.; Ranjbar, A. “Optimal Mix of Distributed Generation Allocation to Improve the Security of Energy Supply in Defensive Sites Using Principles of Passive Defence”; Adv. Defence Sci. Technol. 2016, 7, 19-32
Vahid-Pakdel, M. J.; Nojavan, S.; Mohammadi-Ivatloo, B.; Zare, K; “Stochastic Optimization of Energy Hub Operation with Consideration of Thermal Energy Market and Demand Rsponse”; Energy Convers. Manage. 2017, 145, 117-128

Files

History

  • Receive Date: 02 June 2018
  • Revise Date: 22 October 2019
  • Accept Date: 02 December 2018
  • Publish Date: 23 September 2019

Share

How to cite

Statistics