SavedAdd your journal

Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 16

Multi-objective optimization of operational cost and net emission for a microgrid

Bir mikro şebeke için işletme maliyeti ve net emisyonun çok amaçlı optimizasyonu

Hamza Yapici(Necmettin Erbakan Üniversitesi)0000-0003-0687-2953
Remzi İnan(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ TEKNOLOJİ FAKÜLTESİ)0000-0003-1717-3875
Pages
—
Publication Date
2026-04-13
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is observed that the multi-objective function provides a balanced distribution of optimal Pareto front, including costs and emissions, in different scenarios, with SOO.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to simultaneously minimize operating costs and net-emissions by considering a microgrid consisting of microturbines (MT), fuel cells (FC), wind turbines (WT), photovoltaic panels (PV), and battery storage systems (BSS).
📊
Key Findings & Data:SOO, for each scenario, found costs as 503.4774 €c/day, 481.6893 €c/day, 1648.7597 €c/day, and 1609.0637 €c/day, respectively.
💡
Conclusion & Impact:It is observed that the multi-objective function provides a balanced distribution of optimal Pareto front, including costs and emissions, in different scenarios, with SOO.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Çok amaçlı optimizasyon · İşletme maliyeti · Mikro Şebeke

Abstract

This study aims to simultaneously minimize operating costs and net-emissions by considering a microgrid consisting of microturbines (MT), fuel cells (FC), wind turbines (WT), photovoltaic panels (PV), and battery storage systems (BSS). A multi-objective Stellar Oscillation Optimizer (SOO) algorithm is proposed for the minimization problem. The study analyzes four different scenarios, considering uncertainties such as solar radiation and wind speed in the energy production of PV/BSS units, as well as capacity losses in components. According to simulation results, SOO performed better than many other algorithms in reducing costs and emissions. SOO, for each scenario, found costs as 503.4774 €c/day, 481.6893 €c/day, 1648.7597 €c/day, and 1609.0637 €c/day, respectively. Also, emissions are found to be 108.2395 kg/day, 110.6441 kg/day, 987.5772 kg/day, and 1037.2845 kg/day, respectively, for each scenario. It is observed that the multi-objective function provides a balanced distribution of optimal Pareto front, including costs and emissions, in different scenarios, with SOO.

Özet

Bu çalışma, mikro türbin (MT), yakıt hücresi (YH), rüzgâr türbini (RT), güneş panelleri (GP) ve batarya depolama sisteminden (BDS) oluşan bir mikro-şebekede işletme maliyetini ve net-emisyonu eş zamanlı olarak en aza indirmeyi hedeflemektedir. En aza indirgeme problemi için çok amaçlı Yıldızsal Salınım İyileştirici (YSİ) algoritması önerilmiştir. Çalışmada, GP/BDS birimlerinin enerji üretiminde güneşlenme ve rüzgâr hızı gibi belirsizlikler ile sistem bileşenlerindeki kapasite kayıpları dikkate alınarak dört farklı senaryo analiz edilmiştir. Benzetim sonuçlarına göre YSİ, maliyet ve emisyonları azaltmada diğer birçok algoritmaya göre daha iyi performans sergilemiştir. YSİ maliyetleri her senaryo için, sırasıyla, 503.4774 €c/gün, 481.6893 €c/gün, 1648.7597 €c/gün ve 1609.0637 €c/gün olarak bulunmuştur. Ayrıca, emisyonlar her senaryo için, sırasıyla, 108.2395 kg/gün, 110.6441 kg/gün, 987.5772 kg/gün ve 1037.2845 kg/gün olarak bulunmuştur. Çok-amaçlı fonksiyonun, farklı senaryolarda, maliyetleri ve emisyonları içeren en uygun (optimal) Pareto ön-yüzeyleri YSİ ile dengeli dağılım sağladığı gözlemlenmiştir.

🔖

Cite This Article

Yapici, H., İnan, R. (2026). Multi-objective optimization of operational cost and net emission for a microgrid. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 16. https://doi.org/10.28948/ngumuh.1856890

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (33)

  1. A. Chakraborty and S. Ray, Multi-objective operational cost management with minimum net emission of a smart microgrid. Electric power components and systems, 52 (10), 1870-1891, 2024. https://doi.org /10.1080/15325008.2023.2246958.
  2. X. Tan, Q. Li and H. Wang, Advances and trends of energy storage technology in microgrid. International Journal of Electrical Power & Energy Systems, 44 (1), 179-191, 2013. https://doi.org/10.1016/j.ijepes.2012. 07.015.
  3. D. Q. Oliveira, O. R. Saavedra, K. Santos-Pereira, J. D. Pereira, D. S. Cosme, L. S. Veras, R. G. Bento and V. B. Riboldi, A critical review of energy storage technologies for microgrids. Energy Systems, 16(3), 1063-1092, 2025. https://doi.org/10.1007/s12667-021-00464-6.
  4. M. Cikan, N. N. Cikan and B. Kekezoglu, Determination of optimal island regions with simultaneous DG allocation and reconfiguration in power distribution networks. IET Renewable Power Generation, 19 (1), e12942, 2025. https://doi.org/ 10.1049/rpg2.12942.
  5. S. Ahmad, M. Shafiullah, C. B. Ahmed, and M. Alowaifeer, A review of microgrid energy management and control strategies. IEEE Access, 11, 21729-21757, 2023. https://doi.org/10.1109/ACCESS.2023.3248511.
  6. A. Akter, E. I. Zafir, N. H. Dana, R. Joysoyal, S. K. Sarker, L. Li, S. M. Muyeen, S. K. Das and I. Kamwa, A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation. Energy Strategy Reviews, 51, 101298, 2024. https://doi.org/10.1016/j.esr.2024. 101298.
  7. J. K. Kaldellis, D. Zafirakis, and E. Kondili, Optimum sizing of photovoltaic-energy storage systems for autonomous small islands. International journal of electrical power & energy systems, 32 (1), 24-36, 2010. https://doi.org/10.1016/j.ijepes.2009.06.013.
  8. J. Dong, L. Zhu, Y. Su, Y. Ma, Y. Liu, F. Wang, L. M. Tolbert, J. Glass and L. Bruce, Battery and backup generator sizing for a resilient microgrid under stochastic extreme events. IET Generation, Transmission & Distribution, 12 (20), 4443-4450, 2018. https://doi.org/10.1049/ietgtd.2018. 588.
  9. S. Sharma, S. Bhattacharjee, and A. Bhattacharya, Probabilistic operation cost minimization of Micro-Grid. Energy, 148, 1116-1139, 2018. https://doi.org/10.1016/j.energy.2018.01.164.
  10. H. Alharbi, and K. Bhattacharya, Optimal sizing of battery energy storage systems for microgrids. In 2014 IEEE Electrical Power and Energy Conference pp. 275-280, November 2014. https://doi.org/10.1109/EPEC. 2014.44.
  11. H. Borhanazad, S. Mekhilef, V. G. Ganapathy, M. Modiri-Delshad, and A. Mirtaheri, Optimization of micro-grid system using MOPSO. Renewable energy, 71, 295-306, 2014. https://doi.org/10.1016/j.renene. 2014.05.006.
  12. F. J. Vivas, F. Segura, and J. M. Andújar, Fuzzy logic-based energy management system for grid-connected residential DC microgrids with multi-stack fuel cell systems: A multi-objective approach. Sustainable Energy, Grids and Networks, 32, 100909, 2022. https://doi.org/10.1016/j.segan.2022.100909.
  13. B. Dey, S. Basak, and A. Pal, Demand‐side management based optimal scheduling of distributed generators for clean and economic operation of a microgrid system. International journal of energy research, 46 (7), 8817-8837, 2022. https://doi.org/10.1002/er.7758.
  14. B. Dey, F. P. G. Márquez, and A. Bhattacharya, Demand side management as a mandatory inclusion for economic operation of rural and residential microgrid systems. Sustainable Energy Technologies and Assessments, 54, 102903, 2022. https://doi.org/ 10.1016/j.seta.2022.102903.
  15. B. Mukhopadhyay, and D. Das, Optimal multi-objective long-term sizing of distributed energy resources and hourly power scheduling in a grid-tied microgrid. Sustainable Energy, Grids and Networks, 30, 100632, 2022. https://doi.org/10.1016/ j.segan.2022.100632.
  16. S. Arunachalam, R. Saranya, and N. Sangeetha, Hybrid artificial bee colony algorithm and simulated annealing algorithm for combined economic and emission dispatch including valve point effect. In International conference on swarm, evolutionary, and memetic computing, pp. 354-365, December 2013.
  17. K. K. P. Kumar, N. Soren, A. Latif, D. C. Das, S. S. Hussain, A. Al-Durra, and T. S. Ustun, Day-ahead DSM-integrated hybrid power management incorporated CEED of solar thermal/wind/wave/BESS system using HFPSO. Sustainability, 14 (3), 1169, 2022. https://doi.org/10.3390/su14031169.
  18. G. Das, M. De, and K. K. Mandal, Multi-objective optimization of hybrid renewable energy system by using novel autonomic soft computing techniques. Computers & Electrical Engineering, 94, 107350, 2021. https://doi.org/10.1016/j.compeleceng.2021. 107350.
  19. N. N. Çıkan, Cost optimization in microgrids: A scenario-based analysis by using polar the fox optimization algorithm. Journal of Scientific Reports-A, 061, 34–59, 2025. https://doi.org/10.59313/jsr-a.1606950.
  20. M. Akhtari, and O. Karlström, Role of wind speed and solar irradiation on the cost of medium-sized off-grid hybrid renewable energy systems under challenging weather conditions. Energy Conversion and Management: X, 27, 101163, 2025. https://doi.org/10.1016/j.ecmx.2025.101163.
  21. A. Rodan, A. K. Al-Tamimi, L. Al-Alnemer, and S. Mirjalili, Stellar oscillation optimizer: a nature-inspired metaheuristic optimization algorithm. Cluster Computing, 28 (6), 362, 2025. https://doi.org/10.1007/ s10586-024-04976-5.
  22. B. Bahmani-Firouzi, and R. Azizipanah-Abarghooee, Optimal sizing of battery energy storage for micro-grid operation management using a new improved bat algorithm. International Journal of Electrical Power & Energy Systems, 56, 42-54, 2014. https://doi.org/10.1016/j.ijepes.2013.10.019.
  23. M. Milovanović, D. Klimenta, M. Panić, J. Klimenta, and B. Perović, An application of Wild Horse Optimizer to multi-objective energy management in a micro-grid. Electrical Engineering, 104 (6), 4521-4541, 2022. https://doi.org/10.1007/s00202-022-01636-y.
  24. S. Sharma, S. Bhattacharjee, and A. Bhattacharya, Operation cost minimization of a micro-grid using quasi-oppositional swine influenza model based optimization with quarantine. Ain shams Engineering journal, 9 (1), 45-63, 2018. https://doi.org/10.1016/ j.asej.2015.09.007.
  25. A. Rezvani, M. Gandomkar, M. Izadbakhsh, and A. Ahmadi, Environmental/economic scheduling of a micro-grid with renewable energy resources. Journal of cleaner production, 87, 216-226, 2015. https://doi.org/10.1016/j.jclepro.2014.09.88.
  26. A. A. Moghaddam, A. Seifi, T. Niknam, and M. R. A. Pahlavani, Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy, 36 (11), 6490-6507, 2011. https://doi.org/10.1016/ j.energy.2011.09.017.
  27. S. X. Chen, H. B. Gooi, and M. Q. Wang, Sizing of energy storage for microgrids. IEEE transactions on smart grid, 3 (1), 142-151, 2011. https://doi.org/ 10.1109/TSG.2011.2160745.
  28. J. Branke, T. Kaußler, and H. Schmeck, Guidance in evolutionary multi-objective optimization. Advances in engineering software, 32 (6), 499-507, 2001. https://doi.org/10.1016/S0965-9978(00)00110-1.
  29. I. Y. Kim, and O. L. De Weck, Adaptive weighted-sum method for bi-objective optimization: Pareto front generation. Structural and multidisciplinary optimization, 29 (2), 149-158, 2005. https://doi.org/ 10.1007/s00158-004-0465-1.
  30. K. Deb, Advances in evolutionary multi-objective optimization. In International symposium on search-based software engineering, pp. 1-26, Berlin, Germany, September 2012.
  31. C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, Handling multiple objectives with particle swarm optimization. IEEE Transactions on evolutionary computation, 8 (3), 256-279, 2004. https://doi.org/ 10.1109/TEVC.2004.826067.
  32. M. Reyes-Sierra, and C. C. Coello, Multi-objective swarm optimizers: A survey of the state-of-the-art. International journal of computational intelligence research, 2 (3), 287-308, 2006.
  33. C. A. C. Coello, and C. Coello, C. Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Frontiers of Computer Science in China, 3 (1), 18–30, 2009. https://doi.org/10.1007/s11704-009-0005-7.