CFP last date
20 January 2025
Reseach Article

Cost Benefit Analysis of Self-Optimized Hybrid Solar-Wind-Hydro Electrical Energy Supply as compared to HOMER Optimization

by Amevi Acakpovi, Essel Ben Hagan, Mathias Bennet Michael
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 18
Year of Publication: 2015
Authors: Amevi Acakpovi, Essel Ben Hagan, Mathias Bennet Michael
10.5120/20081-2133

Amevi Acakpovi, Essel Ben Hagan, Mathias Bennet Michael . Cost Benefit Analysis of Self-Optimized Hybrid Solar-Wind-Hydro Electrical Energy Supply as compared to HOMER Optimization. International Journal of Computer Applications. 114, 18 ( March 2015), 32-38. DOI=10.5120/20081-2133

@article{ 10.5120/20081-2133,
author = { Amevi Acakpovi, Essel Ben Hagan, Mathias Bennet Michael },
title = { Cost Benefit Analysis of Self-Optimized Hybrid Solar-Wind-Hydro Electrical Energy Supply as compared to HOMER Optimization },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 18 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 32-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number18/20081-2133/ },
doi = { 10.5120/20081-2133 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:11.034272+05:30
%A Amevi Acakpovi
%A Essel Ben Hagan
%A Mathias Bennet Michael
%T Cost Benefit Analysis of Self-Optimized Hybrid Solar-Wind-Hydro Electrical Energy Supply as compared to HOMER Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 18
%P 32-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The purpose of this paper is to evaluate the cost benefit of a self-optimized solar-wind-hydro hybrid energy supply and to compare the outcome with a similar optimization done with the HOMER software. In reality HOMER optimization software has long been used for hybrid system optimization and many do consider it as the reference software for any optimization related to hybrid energy systems. However, due to some few lack of flexibility in the setting-up of constraints and also the ignorance of the true optimization approaches used by the HOMER, it has become necessary to develop self-optimized algorithms based on rigorous mathematical models. One of these self-optimized models, developed in a previous study, was presented in this paper and was tested with data collected at Accra, Ghana. Results show that the cost of electricity proposed by the HOMER, 0. 307$/kWh, is slightly lower than the one obtained through the self-optimized method, 0. 442$/kWh. Moreover looking at the dynamism of selecting different sources to achieve the optimization at a lower rate for the user, more credit is given to the developed method than the HOMER because the self-optimization method gives more priority to the wind turbine than the solar plant due to the higher electricity cost of solar (0. 64$/kWh). It was however observed that the HOMER software does the opposite in terms of priority. Moreover the probability of unmet load is lower with the self-optimized method than the HOMER result which consists of a big contribution because it is a major quality measure for hybrid systems to always satisfy the load request.

References
  1. HOMER Energy, http://www. homerenergy. com/ software. html, Accessed Feb 2015.
  2. Motaz Amer, A. Namaane and N. K. M'Sirdi (2013). Optimization of Hybrid Renewable Energy Systems (HRES) Using PSO for Cost Reduction, The Mediterranean Green Energy Forum 2013, MGEF-13, Elsevier-Science Direct, Energy Procedia 42 (2013) 318 – 327.
  3. Bansal, A. K. , Gupta R. A. , Kumar, R. , (2010). Optimization of hybrid PV/Wind Energy System using Meta Particle Swarm Optimization (MPSO). IEEE, India Internaltional Conference on Power Electronics (IICPE), pp 1-7
  4. G. Naveen Ram , J. Devi Shree2 , A. Kiruthiga (2013). COST OPTIMIZATION OF STAND ALONE HYBRID POWER GENERATION SYSTEM USING PSO, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 8
  5. Saeid Lotfi Trazouei, Farid Lotfi Tarazouei**, Mohammad Ghiamy (2013). Optimal Design of a Hybrid Solar -Wind-Diesel Power System for Rural Electrification Using Imperialist Competitive Algorithm, INTERNATIONAL JOURNAL of RENEWABLE ENERGY RESEARCH, Vol. 03, No. 2.
  6. DEEPALI SHARMA, PRERNA GAUR, and A. P. MITTAL2 (2014). Comparative Analysis of Hybrid GAPSO Optimization Technique With GA and PSO Methods for Cost Optimization of an Off-Grid Hybrid Energy System, Taylor and Francis Group, Energy Technology & Policy (2014) , ISSN: 2331-7000, pp. 106–114
  7. Lhassane Idoumghar, Mahmoud Melkemi, René Schott, and Maha Idrissi Aouad (2011), Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems, Applied Computational Intelligence and Soft Computing, Volume 2011 (2011), Article ID 138078, 12 pages, DOI:10. 1155/2011/138078
  8. Banu Y. Ekren & Orhan Ekren. (2009). Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Econpapers. Applied Energy. Vol. 86, issue 9, 1387-1394p.
  9. Zhou Wei. (2008). Simulation and optimum design of hybrid solar-wind and solar-wind-diesel power generation systems. Hong Kong Polytechnic University.
  10. S. Ashok. (2007). Optimised model for community-based hybrid energy system. ScienceDirect, Elsevier, Renewable Energy 32. 1155-1164p.
  11. Acakpovi, A. , Hagan, E. B. , Fifatin, F. X. 2015. Cost Optimization of an Electrical Energy Supply from a Hybrid Solar, Wind and Hydropower Plant. International Journal of Computer Applications (IJCA).
  12. Acakpovi, A. , Hagan, E. B. 2013. Novel Photovoltaic Module Modeling using Matlab/Simulink, International Journal of Computer Applications (IJCA) Vol. 83, No. 16, pp 27-32.
  13. Villalva, M. , G. , Gazoli, J. R. , and Filho. E. R. 2009. Comprehensive Approach of Modeling and Simulation of a Photovoltaic Arrays. IEEE Transaction on Power Electronics. Vol. 24. No. 5. pp. 1198-1208.
  14. Ramos-Paja, C. A. , Perez, E. , Montoya, D. G. , Carrejo, C. E. , Simon-Muela, A. , Alonso, C. 2010. Modelling of Full Photovoltaic Systems applied to Advanced Control Strategies. Columbia: Universidad Nacional de Columbia
  15. Tsai, H. L. , Tu, C. S. , and Su, Y. J. 2008. Development of Generalized Phottovoltaic Model Using MATLAB/SIMULINK. Proceedings on the world congress on Engineering and Computer Science. WCECS, ISBN: 978-988-98671-0-2, 6p.
  16. Khajuria, S. , and Kaur, J. 2012. Implementation of Pitch Control of Wind Turbine using Simulink (Matlab). International Journal of Advanced Research in computer Engineering and technology (IJARCET), vol. 1, ISSN: 2278-1323.
  17. Abbas, F. A. R. , Abdulsada, M. A. 2010. Simulation of Wind-Turbine Speed Control by MATLAB. International Journal of Computer and Electrical Engineering. Vol. 2, No. 5, 1793-8163p.
  18. Acakpovi, A. , Hagan, E. B. 2014. A Wind Turbine System Model Using a Doubly-Fed Induction Generator (DFIG). International Journal of Computer Applications (IJCA). Vol. 90, No. 15, pp 6-11.
  19. Fuchs, E. F. , Masoum, M. A. S. 2011. Power Conversion of Renewable Energy Systems. Springer. ISBN 978-1-4419-7978-0.
  20. Hernandez, G. A. M. , Mansoor, S. P. , Jones, D. L. 2012. Modelling and Controlling Hydropower plants. Springer. DOI 10. 1007/978-1-4471-2291-312.
  21. Naghizadeh, R. A. , Jazebi, S. , Vahidi, B. 2012. Modelling Hydro Power Plants and Tuning Hydro Governors as an Educational Guideline. International Review on Modelling and Simulations (I. RE. MO. S), Vol. 5, No. 4.
Index Terms

Computer Science
Information Sciences

Keywords

Solar Energy Wind Energy Hydro Energy Cost optimization Matlab Simulation HOMER optimization