DESIGNING TECHNO-ECONOMIC OFF-GRID PHOTOVOLTAIC SYSTEM USING AN IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM

Authors

  • Seth Bedu Rockson Department of Electrical Engineering, Koforidua Technical University, Koforidua-Ghana https://orcid.org/0000-0003-0126-1045
  • Madihah Md Rasid School of Electrical Engneering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor ,Malaysia https://orcid.org/0000-0002-1947-1174
  • Mohd Shafiq Anuar Project Lebuhraya Usahasama Berhad, Malaysia
  • Siti Maherah Hussin School of Electrical Engneering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor ,Malaysia
  • Norzanah Rosmin School of Electrical Engneering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor ,Malaysia
  • Norjulia Mohamad Nordin School of Electrical Engneering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor ,Malaysia https://orcid.org/0000-0002-0160-0345
  • Michael Gyan Department of Physics university of Education Winneba, Ghana https://orcid.org/0000-0001-6337-2205

DOI:

https://doi.org/10.11113/jurnalteknologi.v85.18334

Keywords:

Off-Grid flow, Optimization, Photovoltaic, Battery, Levelized cost of Energy

Abstract

Conventional power generation is one of the main contributors to the phenomenon of the greenhouse effect. This has led to a diversification of electricity sources including environmentally friendly energy sources such as solar energy. Off-grid PV systems have gained some traction due to their cost-effectiveness for rural communities. However, the intermittent nature of solar is the main challenge to developing the off-grid PV system. Moreover, the high capital cost of PV systems as well as the storage batteries becomes the main concern for all PV users. Thus, this study aims to optimize the size of the PV system and battery simultaneously and design a cost-effective off-grid photovoltaic system considering various aspects such as battery power, solar irradiance, and PV panel selection while ensuring system reliability. The proposed system was optimized using improved Differential Evolution (DE) and its effectiveness was tested by comparing the results with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The Improved DE algorithm provides the highest average cost savings compared to other algorithms, which is $500 per year. It is recommended that this method is very useful in the optimization of off-grid PV systems, considering other uncertainties that affect PV system performance.

References

C. Mekontso, A. Abdulkarim, I. S. Madugu, and O. Ibrahim. 2019. Review of Optimization Techniques for Sizing Renewable Energy Systems. Computer Engineering and Applications Journal. 8(1): 12-30. Doi: 10.18495/comengapp.v8i1.285.

International Energy Agency. 2018. Global Energy & CO2 Status Report. March. 1-15. [Online]. Available: http://www.iea.org/publications/freepublications/publication/GECO2017.pdf%0Ahttps://www.iea.org/publications/freepublications/publication/GECO2017.pdf.

M. Isa, C. W. Tan, and A. H. M. Yatim. 2018. A Proposition of a Standalone Photovoltaic System for Educational Building in Malaysia. Int. J. Renew. Energy Resour. 8(1): 1-6.

A. Askarzadeh and A. Askarzadeh. 2017. Optimisation of Solar and Wind Energy Systems : A Survey. 0750. Doi: 10.1080/01430750.2016.1155493.

E. T. El Shenawy, A. H. Hegazy, and M. Abdellatef. 2017. Design and Optimization of Stand-alone PV System for Egyptian Rural Communities. 12(20): 10433-10446.

M. Hafner, S. Tagliapietra, and L. de Strasse. 2018. Energy Investments for Africa’s Energy. Springer Briefs in Energy. Doi: 10.1007/978-3-319-92219-5.

V. Dabra, K. K. Paliwal, P. Sharma, and N. Kumar. 2017. Optimization of Photovoltaic Power System: A Comparative Study. Prot. Control Mod. Power Syst. 2(1): Doi: 10.1186/s41601-017-0036-2.

M. Kamran et al. 2018. Designing and Optimization of Stand-alone Hybrid Renewable Energy System for Rural Areas of Punjab, Pakistan. Int. J. Renew. Energy Res. 8(4): 2385-2397.

S. Kamali. 2016. Feasibility Analysis of Standalone Photovoltaic Electrification System in a Residential Building in Cyprus. Renew. Sustain. Energy Rev. 65: 1279-1284. Doi: 10.1016/j.rser.2016.07.018.

F. Huneke, J. Henkel, J. A. B. González, and G. Erdmann. 2012. Optimisation of Hybrid Off-grid Energy Systems by Linear Programming. Energy. Sustain. Soc. 2(1): 1-19. Doi: 10.1186/2192-0567-2-7.

E. T. El Shenawy, A. H. Hegazy, and M. Abdellatef. 2017. Design and Optimization of Stand-alone PV System for Egyptian Rural Communities. Int. J. Appl. Eng. Res. 12(20): 10433-10446.

M. B. Eteiba, S. Barakat, M. M. Samy, and W. I. Wahba. 2018. Optimization of an Off-grid PV/Biomass Hybrid System with Different Battery Technologies. Sustain. Cities Soc. 40: 713-727.

R. U. Islam, M. S. Hossain, and K. Andersson. 2020. A Learning Mechanism for Brbes using Enhanced Belief Rule-Based Adaptive Differential Evolution. 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 2020. 1-10.

P. Ouyang and V. Pano. 2015. Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning. Algorithms. 8(3): 697-711. Doi: 10.3390/a8030697.

S. Gao, K. Wang, S. Tao, T. Jin, H. Dai, and J. Cheng. 2021. A State-of-the-art Differential Evolution Algorithm for Parameter Estimation of Solar Photovoltaic Models. Energy Convers. Manag. 230: 113784. Doi: 10.1016/j.enconman.2020.113784.

Z. Huang. 2013. An Improved Differential Evolution Algorithm based on Statistical Log-linear Model. Sensors and Transducers. 159(11): 277-281.

Y. Ma and Y. Bai. 2020. A Multi-population Differential Evolution with Best-random Mutation Strategy for Large-scale Global Optimization. Appl. Intell. 1-17.

T. Sum-Im. 2009. A Novel Differential Evolution Algorithmic Approach to Transmission Expansion Planning.

X. Zhong and P. Cheng. 2020. An Improved Differential Evolution Algorithm Based on Dualstrategy. Math. Probl. Eng. Doi: 10.1155/2020/9767282.

R. A. Khanum, M. A. Jan, N. M. Tairan, and W. K. Mashwani. 2016. Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method. J. Optim. 1-14. Doi: 10.1155/2016/3260940.

M. B. Eteiba, S. Barakat, M. M. Samy, and W. I. Wahba. 2018. Optimization of an Off-grid PV/Biomass Hybrid System with Different Battery Technologies. Sustainable Cities and Society. 40: 713-727. Doi: 10.1016/j.scs.2018.01.012.

T. M. N. T. Mansur, N. H. Baharudin, and R. Ali. 2018. Optimal Sizing and Economic Analysis of Self-consumed Solar PV System for a Fully DC Residential House. 2017 IEEE Int. Conf. Smart Instrumentation, Meas. Appl. ICSIMA 2017. 2017(November): 1-5. Doi: 10.1109/ICSIMA.2017.8312006.

W. M. Sarhan, A. N. Alkhateeb, K. D. Omran, and F. H. Hussein. 2006. Effect of Temperature on the Efficiency of the Thermal Cell. Asian J. Chem. 18(2): 982-990.

S. Asumadu-Sarkodie and P. A. Owusu. 2016. A Review of Ghana’s Solar Energy Potential. Aims Energy. 4(5): 675-696.

P. Rajendran and H. Smith. 2016. Modelling of Solar Irradiance and Daylight Duration for Solar-powered UAV sizing. Energy Exploration & Exploitation. 34(2): 235-243. Doi: 10.1177/0144598716629874.

Seyed Abbas Mousavi Maleki, H. Hizam, and Chandima Gomes. 2017. Estimation of Hourly, Daily and Monthly Global Solar Radiation on Inclined Surfaces: Models Re-Visited. Energies. 10(1): 134. Doi: 10.3390/en10010134.

C. A. Gueymard. 2014. A Review of Validation Methodologies and Statistical Performance Indicators for Modeled Solar Radiation Data: Towards a Better Bankability of Solar Projects. Renew. Sustain. Energy Rev. 39: 1024-1034.

S. Mousavi Maleki, H. Hizam, and C. Gomes. 2017. Estimation of Hourly, Daily and Monthly Global Solar Radiation on Inclined Surfaces: Models Re-Visited. Energies. 10(1): 134. Doi: 10.3390/en10010134.

T. A. Olukan and M. Emziane. 2014. A comparative Analysis of PV Module Temperature Models. Energy Procedia. 62: 694-703. Doi: 10.1016/j.egypro.2014.12.433.stab.pdf.

Martin Murnane and Adel Ghazel. A Closer Look at State of Charge ( SOC ) and State of Health ( SOH ) Estimation Techniques for Batteries. Analog Devices.

P. Boonluk, S. Khunkitti, P. Fuangfoo, and A. Siritaratiwat. 2021. Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand. Energies. 14(5): Doi: 10.3390/en14051458.

Diouf, B., Avis, C. 2019. The Potential of Li-ion Batteries in ECOWAS Solar Home Systems. J. Energy Storage. 22: 295-301. Doi:10.1016/j.est.2019.02.021.

R. Dufo-López, T. Cortés-Arcos, J. S. Artal-Sevil, and J. L. Bernal-Agustín. 2021. Comparison of Lead-acid and Li-ion Batteries Lifetime Prediction Models in stand-Alone Photovoltaic Systems. Appl. Sci. 11(3): 1-16. Doi: 10.3390/app11031099.

Z. Othman, S. I. Sulaiman, I. Musirin, A. M. Omar, and S. Shaari. 2017. Optimal Sizing Stand Alone Photovoltaic System using Evolutionary Programming. ACM Int. Conf. Proceeding Ser. Part F1278. 302-306. Doi: 10.1145/3057039.3057057.

U. S. E. I. Administration. 2020. Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2016. Us Eia Lcoe. February: 1-20.

S. Y. Yuen and X. Zhang. 2015. On Composing an Algorithm Portfolio. Memetic Comput. 7(3): 203-214.

Bp Solar. 2009. Solar Energizer Series Owners Manual, Part number 2627.0116 – 0609R7. .

M. Rasid, J. Murata, and H. Takano. 2017. Fossil Fuel Cost Saving Maximization: Optimal Allocation and Sizing of Renewable-energy Distributed Generation Units Considering Uncertainty via Clonal Differential Evolution. Appl. Therm. Eng. 114: 1424-1432. Doi: 10.1016/j.applthermaleng.2016.10.030.

M. A. A. M. S. Khalid. 2018. Seven-parameter PV Model Estimation using Differential Evolution. Electr. Eng. 100(2): 971-981. Doi: 10.1007/s00202-017-0542-2.

B. Kazimipour, X. Li, and A. K. Qin. 2014. Effects of Population Initialization on Differential Evolution for Large Scale Optimization. 2014 IEEE Congress on Evolutionary Computation (CEC). 2404-2411. Doi: 10.1109/CEC.2014.6900624.

I. Al Hamrouni, A. Khairuddin, and M. Salem. 2013. Application of Differential Evolution Algorithm in Transmission Expansion Planning. Appl. Mech. Mater. 394: 314-320. Doi: 10.4028/www.scientific.net/AMM.394.314.

Y. Wu, W. Lee, and C. Chien. 2011. Modified the Performance of Differential Evolution Algorithm with Dual Evolution Strategy. International Journal of Innovative Computing, Information & Control: IJICIC. 8(4).

S. Mandal and K. K. Mandal. 2020. Optimal Energy Management of Microgrids under Environmental Constraints using Chaos Enhanced Differential Evolution. Renew. Energy Focus. 34: 129-141. Doi: 10.1016/j.ref.2020.05.002.

M. Centeno-telleria, E. Zulueta, U. Fernandez-gamiz, and D. Teso-fz-betoño. 2021. Differential Evolution Optimal Parameters Tuning with Artificial Neural Network. Mathematics. 9(4): 4271-20.

M. Fei, S. Ma, X. Li, X. Sun, L. Jia, and Z. Su. 2017. Advanced Computational Methods in Life System Modeling and Simulation. International Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Nan. 761. Springer.

T. Eltaeib and A. Mahmood. 2018. Differential Evolution: A Survey and Analysis. Appl. Sci. 8(10): Doi: 10.3390/app8101945.

G. Hailu and A. S. Fung. 2019. Optimum Tilt Angle and Orientation of Photovoltaic Thermal System for Application in Greater Toronto Area, Canada. Sustain. 11(22). Doi: 10.3390/su11226443.

Y. Tian and Y. Zhang. 2022. A Comprehensive Survey on Regularization Strategies in Machine Learning. Inf. Fusion. 80: 146-166.

D. Akinyele. 2017. Battery Storage Technologies for Electrical Applications : Impact in Stand-Alone Photovoltaic Systems. Energies. 10(11). Doi: 10.3390/en10111760.

J. Dias, H. Rocha, B. Ferreira, and M. do C. Lopes. 2014. A Genetic Algorithm with Neural Network Fitness Function Evaluation for IMRT Beam Angle Optimization. Cent. Eur. J. Oper. Res. 22(3): 431-455.

Downloads

Published

2023-06-25

Issue

Section

Science and Engineering

How to Cite

DESIGNING TECHNO-ECONOMIC OFF-GRID PHOTOVOLTAIC SYSTEM USING AN IMPROVED DIFFERENTIAL EVOLUTION ALGORITHM. (2023). Jurnal Teknologi, 85(4), 153-165. https://doi.org/10.11113/jurnalteknologi.v85.18334