DOI: https://doi.org/10.61435/ijred.2026.62267
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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, 30000, Thailand

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

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

Received: 21 Jan 2026; Revised: 17 Sep 2026; Accepted: 25 Oct 2026; Published: 1 Nov 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 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.

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Abstract

This research investigates the techno-economic feasibility of an energy management system (EMS) for a grid-tied photovoltaic-battery energy storage system (PV-BESS) prosumer at a university campus. The study introduces an advanced Optimization-Based Control (OBC) framework, comparing Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) against conventional Rule-Based Control (RBC). A primary contribution is the systematic simultaneous parameter-tuning framework, utilizing a grid-search methodology with 30 independent simulations per configuration to ensure statistically reliable BESS scheduling. Results demonstrate that OBC-PSO and OBC-GA achieved annual net profit improvements of 13.50% and 11.72%, respectively, by effectively leveraging Time-of-Use (TOU) arbitrage. Furthermore, precise optimization significantly mitigated battery degradation, extending the replacement cycle from 8 to 10 years and drastically reducing long-term capital expenditure. A 25-year lifecycle analysis reveals that OBC-PSO attains a maximum Net Present Value (NPV) of 97,639 USD, representing a 98.57% improvement over RBC, while reducing the discounted payback period from 11.71 to 6.74 years. The Levelized Cost of Energy (LCOE) was also reduced from 0.085 to 0.078 USD/kWh. Sensitivity analysis confirms that the proposed tuning-centric OBC approach provides a robust financial buffer against electricity price volatility and BESS cost fluctuations. These findings underscore that rigorous algorithm configuration and initialization are essential for enhancing the commercial viability and reliability of grid-tied prosumer systems in modern smart cities.

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

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Section: Original Research Article
Language : EN
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  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. 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
  4. 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
  5. 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
  6. de Lima, T. D., Faria, P., & Vale, Z. (2026). Optimal scheduling of home energy systems considering battery aging and CO2 emissions. Applied Energy, 409, 127433. https://doi.org/10.1016/j.apenergy.2026.127433
  7. Do, L. T., Nguyen, T. N., Tran, Q. T., & Duong, K. T. (2025). Economic analysis of solar power plant and battery energy storage: Case study of Binh Phuoc province, Vietnam. Cleaner Engineering and Technology, 26, 100937. https://doi.org/10.1016/j.clet.2025.100937
  8. Elmotech. (2023, January 17). Thai BESS introduction & Southeast Asia market entry strategy. https://www.elmotech.co.th/en/blog-posts/thai-bess-introduction-southeast-asia-market-entry-strategy
  9. 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
  10. Gbadega, P. A., & Balogun, O. A. (2025). Transactive energy management for efficient scheduling and storage utilization in a grid-connected renewable energy-based microgrid. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 11, 100914. https://doi.org/10.1016/j.prime.2025.100914
  11. Ghadim, H. V., Haas, J., Breyer, C., Gils, H. C., Read, E. G., Xiao, M., & Peer, R. (2025). Are we too pessimistic? Cost projections for solar photovoltaics, wind power, and batteries are over-estimating actual costs globally. Applied Energy, 390, 125856. https://doi.org/10.1016/j.apenergy.2025.125856
  12. 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
  13. Hafeez, G., Ullah, S., Islam, M., Khan, F. A., Alsafran, A. S., Alghamdi, B., & Kraiem, H. (2025). An improved meta-heuristic algorithm for energy optimization in smart grids considering photovoltaic, storage battery, and demand response. Journal of Energy Storage, 119, 115929. https://doi.org/10.1016/j.est.2025.115929
  14. Hasan, S., Zeyad, M., Ahmed, S. M., & Anubhove, M. S. T. (2023). Optimization and planning of renewable energy sources based microgrid for a residential complex. Environmental Progress & Sustainable Energy, 42(5), e14124. https://doi.org/10.1002/ep.14124
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Korab, R., Połomski, M., Smołka, M., & Naczyński, T. (2026). Impact of distributed battery energy storage controlled by optimization-based home energy management systems implementing various objective functions on the voltage profiles in the low-voltage network with a high saturation of prosumer photovoltaic micro-installations. Energy Conversion and Management, 353, 121148. https://doi.org/10.1016/j.enconman.2026.121148
  21. 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
  22. Lee, J. O., & Kim, Y. S. (2022). Novel battery degradation cost formulation for optimal scheduling of battery energy storage systems. International Journal of Electrical Power & Energy Systems, 137, 107795. https://doi.org/10.1016/j.ijepes.2021.107795
  23. 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
  24. Masoumi‐Anaraki, M., Hooshmand, R. A., & Kabiri‐Renani, Y. (2025). Probabilistic Pricing for Collaborative Demand‐Side Management With Coordinated Operation of Energy Storage Systems for Optimal Peak Load Control in Smart Grids. IET Generation, Transmission & Distribution, 19(1), e70084. https://doi.org/10.1049/gtd2.70084
  25. 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
  26. Mouna, E. Q., Abbou, A., Laamim, M., Id-Khajine, L., & Rochd, A. (2025). Comparative analysis of GA and PSO algorithms for optimal cost management in on-grid microgrid energy systems with PV-battery integration. Global Energy Interconnection. https://doi.org/10.1016/j.gloei.2025.05.003
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. Purohit, I., Bose, S. K., & Atla, C. R. (2026). Techno-economics of grid connected Battery Energy Storage System (BESS) in power distribution: Case study of distribution company in India. Energy for Sustainable Development, 92, 101934. https://doi.org/10.1016/j.esd.2026.101934
  35. Rana, A., & Gróf, G. (2024). Prosumer potential assessment and techno-economic feasibility analysis of rural electrification. Energy Conversion and Management: X, 22, 100542. https://doi.org/10.1016/j.ecmx.2024.100542
  36. 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
  37. 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
  38. 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
  39. Sharma, P., Saini, K. K., Mathur, H. D., & Mishra, P. (2024). Improved energy management strategy for prosumer buildings with renewable energy sources and battery energy storage systems. Journal of Modern Power Systems and Clean Energy, 12(2), 381-392. https://doi.org/10.35833/MPCE.2023.000761
  40. Subramanian, V., & Bereznychenko, V. (2025). Cognitive fuzzy logic-integrated energy management for self-sustaining hybrid renewable microgrids. Scientific Reports, 15(1), 1-17. https://doi.org/10.1038/s41598-025-94077-z
  41. Vadi, S., Bildirici, M., & Kaplan, O. (2025). Performance Comparison of Metaheuristic and Hybrid Algorithms Used for Energy Cost Minimization in a Solar–Wind–Battery Microgrid. Sustainability, 17(19), 8849. https://doi.org/10.3390/su17198849
  42. Wang, J., Lyu, C., Bai, Y., Yang, K., Song, Z., & Meng, J. (2024). Optimal scheduling strategy for hybrid energy storage systems of battery and flywheel combined multi-stress battery degradation model. Journal of Energy Storage, 99, 113208. https://doi.org/10.1016/j.est.2024.113208
  43. 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
  44. 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
  45. 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
  46. 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
  47. Yousri, D., Farag, H. E., Zeineldin, H., & El-Saadany, E. F. (2023). Integrated model for optimal energy management and demand response of microgrids considering hybrid hydrogen-battery storage systems. Energy Conversion and Management, 280, 116809. https://doi.org/10.1016/j.enconman.2023.116809
  48. 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
  49. 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
  50. 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
  51. Zhou, H., Bi, J., Lv, Y., Zhao, W., Zhang, H., & Du, T. (2025). Techno-economic analysis of solar photovoltaic systems integrated with battery energy storage for university campuses. Journal of Energy Storage, 129, 117360. https://doi.org/10.1016/j.est.2025.117360
  52. 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

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