skip to main content

Techno-economic evaluation of optimization and rule-based energy management for grid-tied PV-BESS prosumers

1School of Electrical Engineering, Institute of Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand 30000, Thailand

2Energy Solution Business, Nuovo Plus Co., Ltd., Chatuchak, Bangkok 10900, Thailand., Thailand

3Getz Energy Co.,Ltd., Chatuchak, Bangkok 10900, Thailand., Thailand

Received: 21 Jan 2026; Published: 27 Sep 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2025 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

The purpose of this research is to examine the economic and technical feasibility of prosumer-based energy management systems (EMS) in a grid-tied photovoltaic (PV) system coupled with a lithium-iron phosphate battery system on a university campus. A prosumer of grid-tied PV-battery energy storage systems (BESS) prioritizes superior economic performance and the viability of energy management through optimization-based control (OBC). The EMS employs a novel techno-economic approach where PSO and GA algorithms are tuned simultaneously to maximize efficiency and profitability for the prosumer. The parameter sets of each PSO and GA were adjusted to verify the best parameter set for maximizing net profit. The OBC approach utilizes metaheuristic techniques, specifically Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), for dynamic BESS charging/discharging, while RBC (Rule-based control) focuses on minimizing net power imports and exports. The results from our analysis indicate that OBC strategies provide the highest net profit in trading, significantly outperforming RBC, even though RBC can reduce total electricity imports in the prosumer system. As a result of the dynamic charging/discharging patterns of OBC-PSO and OBC-GA, there is an increase in energy exports and better energy transfer, especially by using off-peak energy imports to sell later during on-peak hours. Quantitatively, OBC-PSO and OBC-GA achieved weekly net profit increases of 13.27% and 13.10%, respectively, compared to RBC. Furthermore, long-term economic analysis demonstrates that OBC-GA improves the Net Present Value (NPV) by 50% and reduces the Payback Time (PBT) by 1 year and 29 days compared to RBC. The impact of reducing BESS costs by 10% to 60% closely correlates with the further acceleration of PBT reduction rates for OBC strategies, as presented.

Keywords: Techno-economic; Energy management; Prosumer; Photovoltaic-battery system; Optimize algorithm
Funding: Institute of Research and Development; Suranaree University of Technology

Article Metrics:

  1. Abdul Basit, N. A. I., Rosmin, N., Mustaamal, A. H., Hussin, S. M., & Said, D. M. (2022). A simple energy management system for a micro grid system using rule-based algorithm. In Control, instrumentation and mechatronics: theory and practice (pp. 273-284). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-3923-5_24
  2. Adenuga OT, Krishnamurthy S. Economic Power Dispatch of a Grid-Tied Photovoltaic-Based Energy Management System: Co-Optimization Approach. Mathematics. 2023; 11(15):3266. https://doi.org/10.3390/math11153266
  3. Barchi, G., Pierro, M., & Moser, D. (2019). Predictive Energy Control Strategy for Peak Shaving and Shifting Using BESS and PV Generation Applied to the Retail Sector. Electronics, 8(5), 526. https://doi.org/10.3390/electronics8050526
  4. Behzadi, A., & Sadrizadeh, S. (2023). Grid-tied solar and biomass hybridization for multi-family houses in Sweden: An optimal rule-based control framework through machine learning approach. Renewable energy, 218, 119230. https://doi.org/10.1016/j.renene.2023.119230
  5. Branker, K., Pathak, M. J. M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and sustainable energy reviews, 15(9), 4470-4482. https://doi.org/10.1016/j.rser.2011.07.104
  6. Çınar H, Kandemir I. Active Energy Management Based on Meta-Heuristic Algorithms of Fuel Cell/Battery/Supercapacitor Energy Storage System for Aircraft. Aerospace. 2021; 8(3):85. https://doi.org/10.3390/aerospace8030085
  7. Cole, W., Frazier, A. W., & Augustine, C. (2021). Cost projections for utility-scale battery storage: 2021 update (No. NREL/TP-6A20-79236). National Renewable Energy Lab.(NREL), Golden, CO (United States). https://doi.org/10.2172/1786976
  8. Fadil, H. E., Idrissi, Z. E., Intidam, A., Rachid, A., Koundi, M., & Bouanou, T. (2020). Nonlinear control and energy management of the hybrid fuel cell and battery power system. International Journal of Modelling, Identification and Control, 36(2), 89-103. https://doi.org/10.1504/IJMIC.2020.116196
  9. Hadjipaschalis, I., Poullikkas, A., & Efthimiou, V. (2009). Overview of current and future energy storage technologies for electric power applications. Renewable and sustainable energy reviews, 13(6-7), 1513-1522. https://doi.org/10.1016/j.rser.2008.09.028
  10. Han, B., Zahraoui, Y., Mubin, M., Mekhilef, S., Seyedmahmoudian, M., & Stojcevski, A. (2023). Home energy management systems: A review of the concept, architecture, and scheduling strategies. Ieee Access, 11, 19999-20025. https://doi.org/10.1109/ACCESS.2023.3248502
  11. Jain, M., Saihjpal, V., Singh, N., & Singh, S. B. (2022). An Overview of Variants and Advancements of PSO Algorithm. Applied Sciences, 12(17), 8392. https://doi.org/10.3390/app12178392
  12. Karim, F., Ahmed, T., & Rasif, M. H. I. (2025). Techno-economic assessment of utility-scale solar PV systems in south-eastern Bangladesh: Insights from empirical data and simulation modeling. Energy Conversion and Management: X, 101374. https://doi.org/10.1016/j.ecmx.2025.101374
  13. Khan, A., Bressel, M., Davigny, A., Abbes, D., & Ould Bouamama, B. (2025). Comprehensive Review of Hybrid Energy Systems: Challenges, Applications, and Optimization Strategies. Energies, 18(10), 2612. https://doi.org/10.3390/en18102612
  14. Khan, A., Hizam, H., bin Abdul Wahab, N. I., & Lutfi Othman, M. (2020). Optimal power flow using hybrid firefly and particle swarm optimization algorithm. Plos one, 15(8), e0235668. https://doi.org/10.1371/journal.pone.0235668
  15. Khan, S. U. D., Wazeer, I., Almutairi, Z., & Alanazi, M. (2022). Techno-economic analysis of solar photovoltaic powered electrical energy storage (EES) system. Alexandria Engineering Journal, 61(9), 6739-6753. https://doi.org/10.1016/j.aej.2021.12.025
  16. Kumar, R. P., & Karthikeyan, G. (2024). A multi-objective optimization solution for distributed generation energy management in microgrids with hybrid energy sources and battery storage system. Journal of Energy Storage, 75, 109702. https://doi.org/10.1016/j.est.2023.109702
  17. Luo, L., Abdulkareem, S. S., Rezvani, A., Miveh, M. R., Samad, S., Aljojo, N., & Pazhoohesh, M. (2020). Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. Journal of Energy Storage, 28, 101306. https://doi.org/10.1016/j.est.2020.101306
  18. Mohamed, A. A. R., Best, R. J., Liu, X., & Morrow, D. J. (2021). A comprehensive robust techno-economic analysis and sizing tool for the small-scale PV and BESS. IEEE transactions on energy conversion, 37(1), 560-572. https://doi.org/10.1109/TEC.2021.3107103
  19. Mquqwana MA, Krishnamurthy S. Particle Swarm Optimization for an Optimal Hybrid Renewable Energy Microgrid System under Uncertainty. Energies. 2024; 17(2):422. https://doi.org/10.3390/en17020422
  20. Njoka, G. M., Mogaka, L., & Wangai, A. (2025). Enhancing grid stability and resilience through BESS optimal placement and sizing in VRES-dominated systems. Energy Reports, 13, 1764-1779. https://doi.org/10.1016/j.egyr.2025.01.028
  21. Palizban, O., & Kauhaniemi, K. (2016). Energy storage systems in modern grids—Matrix of technologies and applications. Journal of Energy Storage, 6, 248-259. https://doi.org/10.1016/j.est.2016.02.001
  22. Pan, X., Khezri, R., Mahmoudi, A., Yazdani, A., & Shafiullah, G. (2021). Energy Management Systems for Grid-Connected Houses with Solar PV and Battery by Considering Flat and Time-of-Use Electricity Rates. Energies, 14(16), 5028. https://doi.org/10.3390/en14165028
  23. Parrado, C., Girard, A., Simon, F., & Fuentealba, E. (2016). 2050 LCOE (Levelized Cost of Energy) projection for a hybrid PV (photovoltaic)-CSP (concentrated solar power) plant in the Atacama Desert, Chile. Energy, 94, 422-430. https://doi.org/10.1016/j.energy.2015.11.015
  24. Parrado, C., Girard, A., Simon, F., & Fuentealba, E. (2016). 2050 LCOE (Levelized Cost of Energy) projection for a hybrid PV (photovoltaic)-CSP (concentrated solar power) plant in the Atacama Desert, Chile. Energy, 94, 422-430. https://doi.org/10.1016/j.energy.2015.11.015
  25. Patrizi, N., LaTouf, S. K., Tsiropoulou, E. E., & Papavassiliou, S. (2022). Prosumer-centric self-sustained smart grid systems. IEEE Systems Journal, 16(4), 6042-6053. https://doi.org/10.1109/JSYST.2022.3156877
  26. Pinto, G. X. A., Naspolini, H. F., & Rüther, R. (2024). Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook. Renewable Energy, 225, 120252. https://doi.org/10.1016/j.renene.2024.120252
  27. Pippia, T., Sijs, J., & De Schutter, B. (2019). A single-level rule-based model predictive control approach for energy management of grid-connected microgrids. IEEE Transactions on Control Systems Technology, 28(6), 2364-2376. https://doi.org/10.1109/TCST.2019.2945023
  28. Rasoulzadeh-akhijahani, A., & Mohammadi-ivatloo, B. (2015). Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization. International Journal of Electrical Power & Energy Systems, 67, 350-367. https://doi.org/10.1016/j.ijepes.2014.12.011
  29. Recalde, A., Cajo, R., Velasquez, W., & Alvarez-Alvarado, M. S. (2024). Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review. Energies, 17(13), 3059. https://doi.org/10.3390/en17133059
  30. Shadaei, M., & Samet, H. (2020, June). Using non-linear controller in peer-to-peer energy trading between distributed generations. In 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe) (pp. 1-5). IEEE. https://doi.org/10.1109/EEEIC/ICPSEurope49358.2020.9160508
  31. Tanjavooru, V. T., Pant, P., Hamacher, T., & Hesse, H. (2025). Multi-Objective Nonlinear Power Split Control For BESS With Real-Time Simulation Feedback. arXiv preprint arXiv:2507.04800. https://doi.org/10.48550/arXiv.2507.04800
  32. Vartiainen E, Masson G, Breyer C. The true competitiveness of Solar PV, A European case study. European Technology & Innovation Platform (ETIP); 2017 Available at: http://www.etip-pv.eu/fileadmin/Documents/ETIP_PV_Publications_2017-2018/LCOE_Report_March_2017.pdf
  33. Wang, X., Mao, X., & Khodaei, H. (2021). A multi-objective home energy management system based on internet of things and optimization algorithms. Journal of Building Engineering, 33, 101603. https://doi.org/10.1016/j.jobe.2020.101603
  34. Wu, Y., Wu, Y., & Liu, X. (2019). Couple-based particle swarm optimization for short-term hydrothermal scheduling. Applied Soft Computing, 74, 440-450. https://doi.org/10.1016/j.asoc.2018.10.041
  35. Wynn, S. L. L., Boonraksa, T., Boonraksa, P., Pinthurat, W., & Marungsri, B. (2023). Decentralized Energy Management System in Microgrid Considering Uncertainty and Demand Response. Electronics, 12(1), 237. https://doi.org/10.3390/electronics12010237
  36. Yao, L., Yang, B., Cui, H., Zhuang, J., Ye, J., & Xue, J. (2016). Challenges and progresses of energy storage technology and its application in power systems. Journal of Modern Power Systems and Clean Energy, 4(4), 519-528. https://doi.org/10.1007/s40565-016-0248-x
  37. Zafar, R., Mahmood, A., Razzaq, S., Ali, W., Naeem, U., & Shehzad, K. (2018). Prosumer based energy management and sharing in smart grid. Renewable and Sustainable Energy Reviews, 82, 1675-1684. https://doi.org/10.1016/j.rser.2017.07.018
  38. Zakeri, B., & Syri, S. (2015). Electrical energy storage systems: A comparative life cycle cost analysis. Renew. Sustain. Energy Rev, 42(2015), 569-596. https://doi.org/10.1016/j.rser.2014.10.011
  39. Zhang, C., Qiu, J., Yang, Y., & Zhao, J. (2021). Trading-oriented battery energy storage planning for distribution market. International Journal of Electrical Power & Energy Systems, 129, 106848. https://doi.org/10.1016/j.ijepes.2021.106848
  40. Zhou, X., Xue, S., Du, H., & Ma, Z. (2023). Optimization of building demand flexibility using reinforcement learning and rule-based expert systems. Applied Energy, 350, 121792. https://doi.org/10.1016/j.apenergy.2023.121792

Last update:

No citation recorded.

Last update: 2026-10-15 00:23:46

No citation recorded.