Electrical Engineering

Shaik Karimulla1 and K. Ravi This email address is being protected from spambots. You need JavaScript enabled to view it.1

1School of Electrical Engineering- SELECT, Vellore Institute of Technology, VIT-Vellore-632014, Tamil Nadu, India

 

Received: June 21, 2021
Accepted: July 25, 2021
Publication Date: September 30, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202206_25(3).0001  


ABSTRACT

Renewable energy sources are playing a more active essential part in electrical energy systems in the present and future as a source of energy and an expansion of grid supply. Solar and wind energy are the two sources with the greatest potential for global availability. Wind and solar energy have been investigated with the assistance of a battery energy storage device. The consideration renewable energy sources are due to the free of cost and more available in nature. This system will meet load demand using renewable energy sources. The Fire-fly Algorithm (FFA) is used in this research to minimise energy costs while meeting load demand. The sufficiency of FFA is linked with other metaheuristic methods in connection to performing estimation files, which remain to reduce the cost of energy and to increase the potential power supply. This method considers three different load profiles per year as autumn, winter, and summer seasons, with hourly load-based data used to demonstrate the differences between the three seasons. The results are carried out by using the HOMER (Hybrid Optimization of Multiple Energy Resources) software and MATLAB software. The results show the FFA has better performance than GA, PSO, and IPSO algorithm methods and it shows the comparison for minimization of the cost of energy. Hence, the proposed method shows it is best for minimization of cost with renewable energy sources.


Keywords: Solar energy, Wind energy, Battery storage system and Fire-Fly Algorithm (FFA)


REFERENCES

  1. [1] I. Syed, (2017) “Near-optimal standalone hybrid PV/WE system sizing method" Solar Energy 157: 727–734. DOI:10.1016/j.solener.2017.08.085.
  2. [2] F. H. Fahmy, N. M. Ahmed, and H. M. Farghally. “Optimization of renewable energy power system for small scale brackish reverse osmosis desalination unit and a tourism motel in Egypt”. In: The International Conference on Electrical Engineering. 8. 8th International Conference on Electrical Engineering ICEENG 2012. Military Technical College. 2012, 1–14.
  3. [3] A. Khiareddine, C. Ben Salah, D. Rekioua, and M. Mimouni, (2018) “Sizing methodology for hybrid photovoltaic /wind/ hydrogen/battery integrated to energy management strategy for pumping system" Energy 153:743–762. DOI: 10.1016/j.energy.2018.04.073.
  4. [4] S. Sanajaoba Singh and E. Fernandez, (2018) “Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system" Energy 143: 719–731.DOI: 10.1016/j.energy.2017.11.053.
  5. [5] W. Ma, X. Xue, and G. Liu, (2018) “Techno-economic evaluation for hybrid renewable energy system: Application and merits" Energy 159: 385–409. DOI: 10.1016/j.energy.2018.06.101.
  6. [6] G. Kasilingam, J. Pasupuleti, C. Bharatiraja, and Y. Adedayo, (2019) “Power system stabilizer optimization using BBO algorithm for a better damping of rotor oscillations owing to small disturbances" FME Transactions 47(1): 166–176. DOI: 10.5937/fmet1901166K.
  7. [7] N. Ghorbani, A. Kasaeian, A. Toopshekan, L. Bahrami, and A. Maghami, (2018) “Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability" Energy 154: 581–591. DOI: 10.1016/j.energy.2017.12.057.
  8. [8] A. Forough and R. Roshandel, (2018) “Lifetime optimization framework for a hybrid renewable energy system based on receding horizon optimization" Energy 150:617–630. DOI: 10.1016/j.energy.2018.02.158.
  9. [9] A. Mas’Ud, (2017) “The application of homer optimization software to investigate the prospects of hybrid renewable energy system in rural communities of sokoto in Nigeria" International Journal of Electrical and Computer Engineering 7(2): 596–603. DOI: 10.11591/ijece.v7i2.pp596-603.
  10. [10] S. S. Singh, E. Fernandez, et al. “PSO optimized PV-wind-battery system for satisfying the electrical needs of a remote area”. In: 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI). IEEE. 2017, 632–637.
  11. [11] N. Ahmed, H. Farghally, and F. Fahmy, (2017) “Optimal sizing and economical analysis of PV-wind hybrid power system for water irrigation using genetic algorithm" International Journal of Electrical and Computer Engineering 7(4): 1797–1814. DOI: 10.11591/ijece.v7i4.pp1797-1814.
  12. [12] M. Muralikrishna and V. Lakshminarayana, (2008) “Hybrid (solar and wind) energy systems for rural electrification" ARPN Journal of Engineering and Applied Sciences 3(5): 50–58.
  13. [13] S. Karimulla and K. Ravi. “A Review on Importance of Smart Grid in Electrical Power System”. In: cited By 2. 2019, 22–27. DOI: 10.1109/ICCPEIC45300.2019.9082355.
  14. [14] A. Duman and Ö. Güler, (2018) “Techno-economic analysis of off-grid PV/wind/fuel cell hybrid system combinations with a comparison of regularly and seasonally occupied households" Sustainable Cities and Society 42:107–126. DOI: 10.1016/j.scs.2018.06.029.
  15. [15] P. Sankaramurthy, B. Chokkalingam, S. Padmanaban, Z. Leonowicz, and Y. Adedayo, (2019) “Rescheduling of generators with pumped hydro storage units to relieve congestion incorporating flower pollination optimization" Energies 12(8): DOI: 10.3390/en12081477.
  16. [16] S. Twaha and M. Ramli, (2018) “A review of optimization approaches for hybrid distributed energy generation systems: Off-grid and grid-connected systems" Sustainable Cities and Society 41: 320–331. DOI: 10.1016/j.scs.2018.05.027.
  17. [17] D. Savio, V. Juliet, B. Chokkalingam, S. Padmanaban, J. Holm-Nielsen, and F. Blaabjerg, (2019) “Photovoltaic integrated hybrid microgrid structured electric vehicle charging station and its energy management approach" Energies 12(1): DOI: 10.3390/en12010168.
  18. [18] Y. Yang, S. Bremner, C. Menictas, and M. Kay, (2018) “Battery energy storage system size determination in renewable energy systems: A review" Renewable and Sustainable Energy Reviews 91: 109–125. DOI: 10.1016/j.rser.2018.03.047.
  19. [19] Information on National institute of solar energy, government of India. https://nise.res.in/.
  20. [20] Information on National institute of wind energy, government of India. https://niwe.res.in/.
  21. [21] S. Joseph and E. Jasmin, (2021) “Demand response program for smart grid through real time pricing and home energy management system" International Journal of Electrical and Computer Engineering 11(5): 4558–4567. DOI: 10.11591/ijece.v11i5.pp4558-4567.
  22. [22] N. Kalaiarasi, S. Dash, S. Paramasivam, and C. Bharatiraja, (2021) “Investigation on ANFIS aided MPPT technique for PV fed ZSI topologies in standalone applications" Journal of Applied Science and Engineering (Taiwan) 24(2): 261–269. DOI: 10.6180/jase.202104_24(2).0015.
  23. [23] N. Saad, A. El-Sattar, and A.-A. Mansour, (2018) “A novel control strategy for grid connected hybrid renewable energy systems using improved particle swarm optimization" Ain Shams Engineering Journal 9(4): 2195–2214. DOI: 10.1016/j.asej.2017.03.009.
  24. [24] C. Bharatiraja, S. Jeevananthan, and R. Latha, (2014) “FPGA based practical implementation of NPC-MLI with SVPWM for an autonomous operation PV system with capacitor balancing" International Journal of Electrical Power and Energy Systems 61: 489–509. DOI: 10.1016/j.ijepes.2014.03.066.
  25. [25] S. Sanajaoba and E. Fernandez, (2016) “Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System" Renewable Energy 96: 1–10. DOI: 10.1016/j.renene.2016.04.069.
  26. [26] S. Karimulla and K. Ravi, (2021) “Solving multi objective power flow problem using enhanced sine cosine algorithm" Ain Shams Engineering Journal: DOI: 10.1016/j.asej.2021.02.037.
  27. [27] X.-S. Yang. “Firefly algorithms for multimodal optimization”. In: International symposium on stochastic algorithms. Springer. 2009, 169–178.
  28. [28] X.-S. Yang, (2010) “Firefly algorithm, stochastic test functions and design optimization" International Journal of Bio-Inspired Computation 2(2): 78–84. DOI: 10.1504/IJBIC.2010.032124.
  29. [29] X.-S. Yang and Y.-X. Zhao. “Firefly algorithm and flower pollination algorithm”. In: Nature-Inspired Computation and Swarm Intelligence. Elsevier, 2020, 35–48.
  30. [30] S. Karimulla and K. Ravi, (2021) “Optimal Measuring and Energy Planning of Smart Grid Considering the Battery Lifetime by Using FFA" Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(6): 2324–2337.
  31. [31] L. Zhang, L. Liu, X.-S. Yang, and Y. Dai, (2016) “A novel hybrid firefly algorithm for global optimization" PLoS ONE 11(9): DOI: 10 . 1371 / journal . pone .0163230.
  32. [32] A. Ritthipakdee, A. Thammano, N. Premasathian, and D. Jitkongchuen, (2017) “Firefly Mating Algorithm for Continuous Optimization Problems" Computational Intelligence and Neuroscience 2017: DOI:10.1155/2017/8034573.
  33. [33] A. Kanase-Patil, R. Saini, and M. Sharma, (2011) “Development of IREOM model based on seasonally varying load profile for hilly remote areas of Uttarakhand state in India" Energy 36(9): 5690–5702. DOI: 10.1016/j .energy.2011.06.057.
  34. [34] S. Upadhyay and M. Sharma, (2016) “Selection of a suitable energy management strategy for a hybrid energy system in a remote rural area of India" Energy 94: 352–366. DOI: 10.1016/j.energy.2015.10.134.
  35. [35] R. Dufo-López and J. Bernal-Agustín, (2015) “Technoeconomic analysis of grid-connected battery storage" Energy Conversion and Management 91: 394–404. DOI:10.1016/j.enconman.2014.12.038.
  36. [36] A. Ogunjuyigbe, T. Ayodele, and O. Akinola, (2016) “Optimal allocation and sizing of PV/Wind/Splitdiesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building" Applied Energy 171: 153–171. DOI: 10.1016/j.apenergy.2016.03.051.
  37. [37] D. Guangqian, K. Bekhrad, P. Azarikhah, and A. Maleki, (2018) “A hybrid algorithm based optimization on modeling of grid independent biodiesel-based hybrid solar/wind systems" Renewable Energy 122: 551–560.DOI: 10.1016/j.renene.2018.02.021.
  38. [38] F. Fodhil, A. Hamidat, and O. Nadjemi, (2019) “Potential, optimization and sensitivity analysis of photovoltaicdiesel- battery hybrid energy system for rural electrification in Algeria" Energy 169: 613–624. DOI: 10.1016/j.energy.2018.12.049.

Latest Articles

Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian Approach
Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian ApproachVolume 28, Issue 2 (In Press)

Read more: ...

NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering Reliability
NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering ReliabilityVolume 28, Issue 2 (In Press)

Read more: ...

Predicting Demand for Emergency Ambulance Services: A Comparative Approach
Predicting Demand for Emergency Ambulance Services: A Comparative ApproachVolume 27, Issue 10

Read more: ...

Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education Management
Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education ManagementVolume 28, Issue 2 (In Press)

Read more: ...

River bank erosion prediction using multivariable linear regression
River bank erosion prediction using multivariable linear regressionVolume 27, Issue 12 (In Press)

Read more: ...

Optimization of Ultrasound-Assisted Pectin Extraction from Durian Rind
Optimization of Ultrasound-Assisted Pectin Extraction from Durian RindVolume 28, Issue 2 (In Press)

Read more: ...

Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTM
Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTMVolume 28, Issue 2 (In Press)

Read more: ...

Response Prediction Analysis of RC Frame Structures Using Support Vector Machine Algorithm
Response Prediction Analysis of RC Frame Structures Using Support Vector Machine AlgorithmVolume 28, Issue 2 (In Press)

Read more: ...

Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb core
Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb coreVolume 28, Issue 2 (In Press)

Read more: ...