Genetik Algoritma Kullanılarak Hibrit Yenilenebilir Enerji Kaynaklarının Maliyet Minimizasyonu

Öz Özet 1 : Bu yazıda güneş panelelleri, rüzgâr jeneratörü ve batarya içeren bir hibrit yenilenebilir enerji sistemi önerilmiştir. Bütün sistemin maliyet fonksiyonları belirlenmiştir. Her yenilenebilir enerji modülü (fotovoltaik, rüzgâr jeneratörü, akü) için güç-maliyet ilişkileri gösterilmiştir. Önerilen yenilenebilir enerji sisteminin toplam maliyetini en aza indirmek için genetik algoritma kullanılır. Genetik algoritma için hesaplamaları basitleştirmek için maliyet katsayısı tanımları yapılmıştır. Geleneksel hesaplama algoritmalarının yanı sıra, hesaplama zamanı ve hesaplama çabasını azaltmak için genetik algoritmanın olasılık yaklaşımı kullanılmıştır. Sonuç olarak; genetik algoritma, yenilenebilir enerji maliyet optimizasyonu problemlerinde hesaplama çabasını azalttığından, geleneksel hesaplama algoritmalarından daha uygun olduğu gösterilmiştir. Özet 2 : In this paper, a hybrid renewable energy system is proposed which includes PV, wind generator and batteries.  Cost functions of whole system is determined.  Power-cost relations for each renewable energy module (PV, wind generator, battery) are inspected.  Genetic algorithm is used to minimize the total cost of proposed renewable energy system. For genetic algorithm, cost coefficient definitions are made for simplifying calculations. Beside conventional search algorithms, genetic algorithm’s probabilistic approach is used for reducing calculation time and computation effort. In results, it is shown that genetic algorithm is more suitable than conventional search algorithms for reducing computation effort for renewable energy cost optimization problems.

Kaynakça

Y Shaahid, SM., Elhadidy, MA. (2003). Opportunities for utilization of standalone hybrid photovoltaic + diesel + battery) power systems in hot climates, Renew Energy, 28,1741–53.

Mellit, A., Soteris A., Kalogirou. (2008). Artificial intelligence techniques for photovoltaic applications: A review, Progress in Energy and Combustion Science, 34, 574–632

Billionnet, A., Costa, MC., Poirion, PL., (2016). Robust optimal sizing of a hybrid energy stand-alone system, European Journal of Operational Research, Volume 254, Issue 2, Pages 565-575.

Melanie, M., (1998). An Introduction to Genetic Algorithms, Cambridge, Massachusetts • London, England, First MIT Press paperback edition.

Chang, KH., (2016) A quantile-based simulation optimization model for sizing hybrid renewable energy systems, Simulation Modelling Practice And Theory, 66, 94-103.

Khare, R., Kumar, Y. (2016). A novel hybrid MOL-TLBO optimized techno-economic-socio analysis of renewable energy mix in island mode, Applied Soft Computing, 43,187-198.

Berrada, A., Loudiyi, K., Operation, sizing, and economic evaluation of storage for solar and wind power plants, Renewable & Sustainable Energy Reviews, 59, 1117-1129.

Colmenar-Santos, A., Reino-Rio, C., Borge-Diez, D., Collado-Fernandez, E. (2016). Distributed generation: A review of factors that can contribute most to achieve a scenario of DG units embedded in the new distribution networks, Renewable & Sustainable Energy Reviews, 59, 1130-1148.

Ferrari, ML., Rivarolo, M., Massardo, AF. (2016). Hydrogen production system from photovoltaic panels: experimental characterization and size optimization, Energy Conversion and Management, 116, 194-202.

Brka, A,, Al-Abdeli, YM., Kothapalli, G. (2016). Predictive power management strategies for stand-alone hydrogen systems: Operational impact, International Journal of Hydrogen Energy, Volume 41, Issue 16, pages 6685-6698.

Atia, R., Yamada, N. (2016). Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids, IEEE Transactions on Smart Grid, Volume 7, Issue 3, pages 1204-1213.

Singh, S., Kaushik, SC., (2016). Optimal sizing of grid integrated hybrid PV-biomass energy system using artificial bee colony algorithm, IET Renewable Power Generation, Volume 10, Issue 5, pages 642-650.

Siddaiah, R., Saini, RP. (2016) A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications, Renewable & Sustainable Energy Reviews, 58, 376-396.

Hosseinalizadeh, R., Shakouri, H., Amalnick, MS., Taghipour, P. (2016). Economic sizing of a hybrid (PV–WT–FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: Case study of Iran, Renewable and Sustainable Energy Reviews, Volume 54, Pages 139-150.

Eltamaly, AM., Mohamed, M. (2016). A novel software for design and optimization of hybrid power systems, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Volume 38, Issue 4, pages 1299-1315.

Mohamed, MA., Eltamaly, AM., Alolah, AI. (2015). Sizing and techno-economic analysis of stand-alone hybrid photovoltaic/wind/diesel/battery power generation systems, Journal Of Renewable And Sustainable Energy, Volume 7, Issue 6.

Sharafi, M., ElMekkawy, TY., Bibeau, EL. (2015). Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio, Renewable Energy, 83: 1026-1042.

Ogunjuyigbe, ASO., Ayodele, TR., Akinola, OA. (2016). Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building, Applied Energy, 171:153-171.

Upadhyay, S., Sharma, MP. (2016). Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India, Energy, 94,352-366.

Gonzalez, A., Riba, JR., Rius, A., Puig, R. (2015) Optimal sizing of a hybrid grid-connected photovoltaic and wind power system, Applied Energy, 154:752-762. Ko, MJ., Kim, YS., Chung, MH., Jeon, HC., (2015). Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm, Energies, Volume 8, Issue 4, pages 2924-2949.

Kaynak Göster

APA Karabacak, K , Telli, A . (2017). Genetik Algoritma Kullanılarak Hibrit Yenilenebilir Enerji Kaynaklarının Maliyet Minimizasyonu . Yalvaç Akademi Dergisi , 2 (2) , 41-54 .