10.57647/mjee.2026.2001.02

Optimal Sizing and Cost Reduction of Grid Tied Photovoltaic Battery Systems Using Artificial Protozoa Optimization

PDF
  • Devesh Jaiswal*1,
  • Monika Mittal1,
  • Vikas Mittal2,
  1. Electrical Engineering Department, NIT KURUKSHETRA, Haryana, India
  2. Electronics and Communication Engineering Department, NIT KURUKSHETRA, Haryana, India

Received: 2024-10-14

Revised: 2025-02-26

Accepted: 2025-04-12

Published in Issue 2026-03-31

Copyright (c) 2026 Devesh Jaiswal, Monika Mittal, Vikas Mittal (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Jaiswal, D., Mittal, M., & Mittal, V. (2026). Optimal Sizing and Cost Reduction of Grid Tied Photovoltaic Battery Systems Using Artificial Protozoa Optimization. Majlesi Journal of Electrical Engineering, 20(1 (March 2026). https://doi.org/10.57647/mjee.2026.2001.02
Crossref
Scopus
Google Scholar
Europe PMC

PDF views: 39

Abstract

This work focuses on optimal sizing of rooftop photovoltaic (PV) systems and battery energy storage systems (BESS) for grid-connected houses (GCHs) to minimize electricity costs under flat and time-of-use (TOU) rate structures. The research employs Artificial Protozoa Optimization (APO) algorithm to create a cost-effective optimization model, considering grid constraints, solar and load profiles, component costs, and degradation effects. Deep analyses explore the impact of electricity rate variations and grid limitations on system sizing and expenses. A rule-based energy organization system is used to optimize power flow amongst PV, BESS, load, and grid connections. The study compares Particle Swarm Optimization (PSO) and Artificial Protozoa Optimization (APO) algorithms across different tariff scenarios: flat-flat, TOU-flat, flat-TOU, and TOU-TOU. APO consistently outperforms PSO, achieving lower net present cost (NPC) and cost of energy (COE) while optimizing system power output and storage capacity. In various pricing scenarios, the APO algorithm consistently outperformed PSO, achieving significantly lower Net NPC and COE with optimized PV and BESS configurations. APO proved more effective in minimizing costs across TOU-TOU, flat-flat, and TOU-flat scenarios These findings highlight APO’s superior performance in cost and efficiency optimization for grid-linked solar PV and BESS approaches.

Keywords

  • Battery energy storage system (BESS),
  • Grid-connected houses (GCHs),
  • Net present cost (NPC),
  • Cost of energy (COE),
  • Particle Swarm Optimization (PSO),
  • Artificial Protozoa Optimization (APO)
PDF

References

  1. “Ministry of New and Renewable Energy, Solar grid.” Available from: https://mnre.gov.in/solar-grid-connected
  2. “Fraunhofer Institute for Solar Energy Systems ISE.” Available from: https://www.ise.fraunhofer. de/en.html
  3. Khenissi I, Fakhfakh MA, Sellami R, and Neji R. “A new approach for optimal sizing of a grid connected PV system using PSO and GA algorithms: Case of Tunisia.” Appl. Artif. Intell. 2021; 35:1930–51. doi: 10.1080/08839514.2021.1995233
  4. Garip S and Ozdemir S. “Optimization of PV and battery energy storage size in grid-connected microgrid.” Appl. Sci. 2022; 12:8247. doi: 10. 3390/app12168247
  5. Regis N, Muriithi CM, and Ngoo L. “Optimal battery sizing of a grid-connected residential photovoltaic system for cost minimization using PSO algorithm.” Eng. Technol. Appl. Sci. Res. 2019; 9:4905–11. doi: 10.48084/etasr.3094
  6. Yi H and Yang X. “A metaheuristic algorithm based on simulated annealing for optimal sizing and techno-economic analysis of PV systems with multi-type of battery energy storage.” Sus-tain. Energy Technol. Assess. 2022; 53:102724. doi: 10.1016/j.seta.2022.102724
  7. Grisales-Norena LF, Ocampo-Toro JA, Montoya-Giraldo OD, Montano J, and Hernandez JC. “Optimal operation of battery storage systems in standalone and grid-connected DC microgrids using parallel metaheuristic optimization algo-rithms.” J. Energy Storage 2023; 65:107240. doi: 10.1016/j.est.2023.107240
  8. Fathima H and Palanisamy K. “Optimized sizing, selection, and economic analysis of battery en-ergy storage for grid-connected wind-PV hybrid system.” Model. Simul. Eng. 2015; 2015:16. doi: 10.1155/2015/713530
  9. Nimma KS, Al-Falahi MD, Nguyen HD, Jayas-inghe SDG, Mahmoud TS, and Negnevitsky M. “Grey wolf optimization-based optimum energy-management and battery-sizing method for grid-connected microgrids.” Energies 2018; 11:847. doi: 10.3390/en11040847
  10. Fares D, Fathi M, and Mekhilef S. “Performance evaluation of metaheuristic techniques for opti-mal sizing of a stand-alone hybrid PV/wind/bat-tery system.” Appl. Energy 2022; 305:117823. doi: 10.1016/j.apenergy.2021.117823
  11. Zhao L, Jerbi H, Abbassi R, Liu B, Latifi M, and Nakamura H. “Sizing renewable energy systems with energy storage systems based microgrids for cost minimization using hybrid shuffled frog-leaping and pattern search algorithm.” Sustain. Cities Soc. 2021; 73:103124. doi: 10.1016/j.scs.2021.103124
  12. Nayak CK and Nayak MR. “Optimal battery en-ergy storage sizing for grid connected PV sys-tem using IHSA.” Proc. Int. Conf. Signal Process., Commun., Power Embedded Syst. (SCOPES) 2016:121–7. doi: 10.1109/SCOPES.2016.7955654
  13. Vaka SSKR and Matam SK. “Optimal sizing and management of battery energy storage systems in microgrids for operating cost minimization.” Electr. Power Compon. Syst. 2021; 49:1319–32. doi: 10.1080/15325008.2022.2061641
  14. Xu Y, Huang S, Wang Z, Ren Y, Xie Z, Guo J, and Zhu Z. “Optimization based on tabu search al-gorithm for optimal sizing of hybrid PV/energy storage system: Effects of tabu search param-eters.” Sustain. Energy Technol. Assess. 2022; 53:102662. doi: 10.1016/j.seta.2022.102662
  15. Kamble R, Karve GM, Chakradeo A, and Vaidya
  16. G. “Optimal sizing of battery energy storage system in microgrid by using particle swarm optimization technique.” J. Integr. Sci. Technol. 2018; 6:6–12. Available from: https://pubs.iscience. in/journal/index.php/jist/article/viewFile/785/426
  17. Hadi HA, Kassem A, Amoud H, Nadweh S, and Ghazaly NM. “Using Grey Wolf Optimization Algorithm and Whale Optimization Algorithm for Optimal Sizing of Grid-Connected Bifacial PV Systems.” J. Robot. Control 2024; 5:733–45. doi: 18196/jrc.v5i3.21777
  18. Jasim AM, Jasim BH, and Bures V. “A novel grid-connected microgrid energy management system with optimal sizing using hybrid grey wolf and cuckoo search optimization algorithm.” Front. Energy Res. 2022; 10:960141. doi: 10 . 3389/fenrg.2022.960141
  19. Diab AAZ, El-Rifaie AM, Zaky MM, and Tolba MA. “Optimal sizing of stand-alone micro-grids based on recent metaheuristic algorithms.” Mathematics 2022; 10:140. doi: 10 . 3390 / math10010140
  20. Salman UT, Al-Ismail FS, and Khalid M. “Optimal sizing of battery energy storage for grid-connected and isolated wind-penetrated mi-crogrid.” IEEE Access 2020; 8:91129–38. doi: 10.1109/ACCESS.2020.2992654
  21. Mahmoud TS, Ahmed BS, and Hassan MY. “The role of intelligent generation control algorithms in optimizing battery energy storage systems size in microgrids: A case study from Western Aus-tralia.” Energy Convers. Manag. 2019; 196:1335–52. doi: 10.1016/j.enconman.2019.06.045
  22. Sun X, He H, and Ma L. “Harmony search meta-heuristic algorithm based on the optimal sizing of wind-battery hybrid micro-grid power system with different battery technologies.” J. Energy Storage 2024; 75:109582. doi: 10.1016/j.est.2023. 109582
  23. Diab AAZ, Sultan HM, Mohamed IS, Kuznetsov ON, and Do TD. “Application of different op-timization algorithms for optimal sizing of PV/wind/diesel/battery storage stand-alone hy-brid microgrid.” IEEE Access 2019; 7:119223–45. doi: 10.1109/ACCESS.2019.2936656
  24. Zhang Y, Ma T, and Yang H. “A review on ca-pacity sizing and operation strategy of grid-connected photovoltaic battery systems.” Energy Built Environ. 2024; 5:500–16. doi: 10.1016/j. enbenv.2023.04.001
  25. Kumar J and Kumar N. “Optimal scheduling of grid connected solar photovoltaic and battery storage system considering degradation cost of battery.” Iran. J. Sci. Technol. Trans. Electr. Eng. 2022; 46:1175–88. doi: 10.1007/s40998- 022-
  26. 00529-x
  27. Boghdady TA, Bakhoum SR, and Sayed MM. “Microgrid Optimal Sizing and Economic Op-eration for Rural Area in Assiut (Egypt) Using Meta-Heuristic Optimization Techniques.” Proc. Int. Telecommun. Conf. (ITC-Egypt) 2022 :1–7. doi: 10.1109/ITC-Egypt55520.2022.9855733
  28. Khezri R, Mahmoudi A, and Aki H. “Optimal planning of solar photovoltaic and battery stor-age systems for grid-connected residential sec-tor: Review, challenges and new perspectives.” Renew. Sustain. Energy Rev. 2022; 153:111763. doi: 10.1016/j.rser.2021.111763
  29. Li Y, Vilathgamuwa DM, Quevedo DE, Lee CF, and Zou C. “Ensemble nonlinear model predic-tive control for residential solar battery energy management.” IEEE Trans. Control Syst. Technol. 2023. doi: 10.1109/TCST.2023.3291540
  30. Li Y, Vilathgamuwa DM, Farrell TW, Tran NT, and Teague J. “Nonlinear model predictive control of photovoltaic-battery system for short-term power dispatch.” Proc. Annu. Conf. IEEE Ind. Electron. Soc. (IECON) 2018 :1884–9. doi: 10. 1109/IECON.2018.8591326
  31. Mohamed MA, Shadoul M, Yousef H, Al-Abri R, and Sultan HM. “Multi-agent based optimal sizing of hybrid renewable energy systems and their significance in sustainable energy devel-opment.” Energy Rep. 2024; 12:4830–53. doi: 10.1016/j.egyr.2024.10.051
  32. Monteiro A et al. “Development of a tool for sizing and technical–financial analysis of energy-storage systems using batteries.” Energy Technol. 2024; 12:2301638. doi: 10.1002/ente.202301638
  33. Jaiswal D, Mittal M, and Mittal V. “An optimiza-tion approach to enhance performance of a solar PV system based on performance assess-ment of an existing 5 MW on-grid PV system.”
  34. J. Harbin Eng. Univ. 2023; 44. Available from: https://harbinengineeringjournal.com/index.php/ journal/article/download/628/473/1091
  35. Wang X, Snasel V, Mirjalili S, Pan JS, Kong L, and Shehadeh HA. “Artificial Protozoa Optimizer (APO): A novel bio-inspired metaheuristic al-gorithm for engineering optimization.” Knowl. Based Syst. 2024 :111737. doi: 10.1016/j.knosys. 2024.111737