Araştırma Makalesi
BibTex RIS Kaynak Göster

Accurate Frequency Control of Energy Storage Systems with a Symbolic Game Theory Approach

Yıl 2026, Cilt: 41 Sayı: 1, 195 - 212, 25.03.2026
https://doi.org/10.21605/cukurovaumfd.1878644
https://izlik.org/JA49ZH35XH

Öz

The increasing influence of renewable energy sources leads to reduced system inertia, making power grids highly vulnerable to frequency deviations and stochastic disturbances. Traditional control methods rely on linearized models and cannot mathematically guarantee safety constraints under extreme conditions, potentially leading to battery saturation and system interruptions. In this paper, a correct-by-construction control framework for Energy Storage Systems is proposed using Symbolic Discrete Controller Synthesis. A controller that strictly enforces hard constraints on both grid frequency and battery State of Charge (SoC) is synthesised by modeling the frequency regulation problem as a safety game on a finite state abstraction. Comparative benchmarks demonstrate that while standard dead-band and droop control cause battery overcharge and service unavailability during worst-case volatility events, the symbolic controller guarantees 100% safety and continuous operation by maintaining the system strictly within designated bounds (i.e., 0.2 ≤SoC ≤0.9) without requiring complex runtime optimization.

Kaynakça

  • 1. Kundur, P. (1994). Power system stability and control. McGraw-Hill, New York.
  • 2. Bevrani, H. (2014). Robust power system frequency control. Springer, 391.
  • 3. Machowski, J., Bialek, J. & Bumby, J. (2012). Power system dynamics: stability and control. Wiley, 660.
  • 4. Elgerd, O. I. & Fosha, C. E. (1970). Optimum megawatt-frequency control of multiarea electric energy systems. IEEE Transactions on Power Apparatus and Systems, 89, 556-563.
  • 5. Vandoorn, T., Kooning, J., Meersman, B. & Vandevelde, L. (2013). Voltage-based droop control of renewables to avoid on-off oscillations caused by overvoltages. IEEE Transactions On Power Delivery, 28(2), 845-854.
  • 6. Parisio, A., Rikos, E. & Glielmo, L. (2014). A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 22(5), 1813-1827.
  • 7. Alskaif, T., Luna Hernandez, A., Guerrero-Zapata, M., Guerrero, J. & Bellalta, B. (2017). Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery. Journal of Energy and Buildings, 138, 228-239.
  • 8. Aboukhris, A.B.O., Karimzadeh Kolamroudi, M. & Kavalcıoğlu, C. (2025). Optimizing energy management strategies in grid-connected hybrid pv-battery-supercapacitor systems: a comprehensive review. Gazi University Journal of Science, 38(4), 1892-1920.
  • 9. Lin, Y., Yang, Q., Zhou, J., Chen, X. & Wen, J. (2023). Model predictive control based frequency regulation for power systems containing massive energy storage clusters. The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022),1183-1190.
  • 10. Kouro, S., Cortes, P., Vargas, R., Ammann, U. & Rodriguez, J. (2009). Model predictive control-a simple and powerful method to control power converters. Industrial Electronics, IEEE Transactions on, 56, 1826-1838.
  • 11. Tabuada, P. (2009). Verification and control of hybrid systems: a symbolic approach. Springer, 202.
  • 12. Zamani, M., Pola, G., Mazo Jr, M. & Tabuada, P. (2011). Symbolic models for nonlinear control systems without stability assumptions. IEEE Transactions on Automatic Control, 57(7), 1804-1809.
  • 13. Rungger, M. & Zamani, M. (2016). SCOTS: A tool for the synthesis of symbolic controllers. In Proc. 19th Int. Conf. Hybrid Syst. Comput. Control (HSCC), 99-104.
  • 14. Reissig, G., Weber, A. & Rungger, M. (2017). Feedback refinement relations for the synthesis of symbolic controllers. IEEE Transactions on Automatic Control, 62, 1781-1796.
  • 15. Pola, G., Girard, A. & Tabuada, P. (2010). Approximately bisimilar symbolic models for nonlinear control systems. Automatica, 44(10), 2508-2516.
  • 16. Zamani, M., Esfahani, P., Majumdar, R., Abate, A. & Lygeros, J. (2013). Symbolic control of stochastic systems via approximately bisimilar finite abstractions. Automatic Control, IEEE Transactions on. 59.
  • 17. Dörfler, F., Simpson-Porco, J. & Bullo, F. (2015). Plug-and-play control and optimization in microgrids. Proceedings of the IEEE Conference on Decision and Control, 211-216.
  • 18. Li, J., Hu, J. & Xin, D. (2025). Model predictive control-based optimal control of primary frequency regulation power for hydrogen fuel cell-energy storage battery system. Renewable Energy, Elsevier, 248.
  • 19. Inga Espinoza, C. H., & Palma, M. T. (2025). A Coordinated Neuro-Fuzzy Control System for Hybrid Energy Storage Integration: Virtual Inertia and Frequency Support in Low-Inertia Power Systems. Energies, 18(17), 4728.
  • 20. Zhang, B., Iu, H., Zhang, X. & Chau, T.K. (2024). A NoisyNet deep reinforcement learning method for frequency regulation in power systems. IET Gener. Transm. Distrib., 18, 3042-3051.
  • 21. Nacar Çıkan, N. (2025). Distribution network reconfiguration for voltage profile enhancement and power loss reduction under hourly energy consumption using quantum approximate optimization algorithm. Cukurova University, Journal of the Faculty of Engineering, 40(1), 79-87.
  • 22. Çıkan, M. (2025). IEEE 13-baralı dengesiz üç fazlı güç dağıtım sistemlerinde yenilenebilir enerji kaynaklarının optimum konumlandırılması ve boyutlandırılması. Çukurova Üniversitesi, Mühendislik Fakültesi Dergisi, 40(1), 89-98.
  • 23. Kurucan, M., Michailidis, P., Michailidis, I. & Minelli, F. (2025). A modular hybrid soc-estimation framework with a supervisor for battery management systems supporting renewable energy integration in smart buildings. Energies, 18(17), 4537.
  • 24. Kurucan, M., Özbaltan, M., Yetgin, Z. & Alkaya, A. (2024). Applications of artificial neural network based battery management systems: A literature review. Renewable and Sustainable Energy Reviews, 192, 114262.
  • 25. Rydin Gorjão, L., Jumar, R., Maass, H., Hagenmeyer, V., Yalcin, G. C., Kruse, J., Timme, M., Beck, C., Witthaut, D. & Schafer, B. (2020). Open database analysis of scaling and spatio-temporal properties of power grid frequencies. Nat Commun., 11, 6362.

Sembolik Oyun Teorisi Yaklaşımı ile Enerji Depolama Sistemlerinin Doğru Frekans Kontrolü

Yıl 2026, Cilt: 41 Sayı: 1, 195 - 212, 25.03.2026
https://doi.org/10.21605/cukurovaumfd.1878644
https://izlik.org/JA49ZH35XH

Öz

Yenilenebilir enerji kaynaklarının artan etkisi, sistem ataletinin azalmasına yol açarak güç şebekelerini frekans sapmalarına ve stokastik bozulmalara karşı oldukça savunmasız hale getirmektedir. Geleneksel kontrol yöntemleri doğrusallaştırılmış modellere dayanmaktadır; bu yöntemler aşırı koşullar altında güvenlik kısıtlarını matematiksel olarak garanti edemez, bu da potansiyel olarak batarya doygunluğuna ve sistem kesintilerine yol açabilir. Bu makalede, Enerji Depolama Sistemleri için Sembolik Ayrık Kontrolör Sentezi kullanılarak yapılandırma itibarıyla doğru bir kontrol çerçevesi önerilmektedir. Frekans düzenleme problemini sonlu durum soyutlaması üzerinde bir güvenlik oyunu olarak modelleyerek, hem şebeke frekansı hem de batarya Şarj Durumu (SoC) üzerinde sert kısıtları kesin olarak uygulayan bir kontrolör sentezlenmiştir. Karşılaştırmalı kıyaslamalar; standart ölü bant ve droop kontrolünün en kötü durum volatalite olayları sırasında bataryanın aşırı şarj olmasına ve hizmet dışı kalmasına neden olduğunu, buna karşın sembolik kontrolörün sistemi kesin olarak belirlenmiş sınırlar içinde (yani 0.2 ≤SoC ≤0.9) tutarak karmaşık çalışma zamanı optimizasyonuna gerek duymadan %100 güvenlik ve kesintisiz çalışma sağladığını göstermektedir.

Kaynakça

  • 1. Kundur, P. (1994). Power system stability and control. McGraw-Hill, New York.
  • 2. Bevrani, H. (2014). Robust power system frequency control. Springer, 391.
  • 3. Machowski, J., Bialek, J. & Bumby, J. (2012). Power system dynamics: stability and control. Wiley, 660.
  • 4. Elgerd, O. I. & Fosha, C. E. (1970). Optimum megawatt-frequency control of multiarea electric energy systems. IEEE Transactions on Power Apparatus and Systems, 89, 556-563.
  • 5. Vandoorn, T., Kooning, J., Meersman, B. & Vandevelde, L. (2013). Voltage-based droop control of renewables to avoid on-off oscillations caused by overvoltages. IEEE Transactions On Power Delivery, 28(2), 845-854.
  • 6. Parisio, A., Rikos, E. & Glielmo, L. (2014). A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 22(5), 1813-1827.
  • 7. Alskaif, T., Luna Hernandez, A., Guerrero-Zapata, M., Guerrero, J. & Bellalta, B. (2017). Reputation-based joint scheduling of households appliances and storage in a microgrid with a shared battery. Journal of Energy and Buildings, 138, 228-239.
  • 8. Aboukhris, A.B.O., Karimzadeh Kolamroudi, M. & Kavalcıoğlu, C. (2025). Optimizing energy management strategies in grid-connected hybrid pv-battery-supercapacitor systems: a comprehensive review. Gazi University Journal of Science, 38(4), 1892-1920.
  • 9. Lin, Y., Yang, Q., Zhou, J., Chen, X. & Wen, J. (2023). Model predictive control based frequency regulation for power systems containing massive energy storage clusters. The Proceedings of the 5th International Conference on Energy Storage and Intelligent Vehicles (ICEIV 2022),1183-1190.
  • 10. Kouro, S., Cortes, P., Vargas, R., Ammann, U. & Rodriguez, J. (2009). Model predictive control-a simple and powerful method to control power converters. Industrial Electronics, IEEE Transactions on, 56, 1826-1838.
  • 11. Tabuada, P. (2009). Verification and control of hybrid systems: a symbolic approach. Springer, 202.
  • 12. Zamani, M., Pola, G., Mazo Jr, M. & Tabuada, P. (2011). Symbolic models for nonlinear control systems without stability assumptions. IEEE Transactions on Automatic Control, 57(7), 1804-1809.
  • 13. Rungger, M. & Zamani, M. (2016). SCOTS: A tool for the synthesis of symbolic controllers. In Proc. 19th Int. Conf. Hybrid Syst. Comput. Control (HSCC), 99-104.
  • 14. Reissig, G., Weber, A. & Rungger, M. (2017). Feedback refinement relations for the synthesis of symbolic controllers. IEEE Transactions on Automatic Control, 62, 1781-1796.
  • 15. Pola, G., Girard, A. & Tabuada, P. (2010). Approximately bisimilar symbolic models for nonlinear control systems. Automatica, 44(10), 2508-2516.
  • 16. Zamani, M., Esfahani, P., Majumdar, R., Abate, A. & Lygeros, J. (2013). Symbolic control of stochastic systems via approximately bisimilar finite abstractions. Automatic Control, IEEE Transactions on. 59.
  • 17. Dörfler, F., Simpson-Porco, J. & Bullo, F. (2015). Plug-and-play control and optimization in microgrids. Proceedings of the IEEE Conference on Decision and Control, 211-216.
  • 18. Li, J., Hu, J. & Xin, D. (2025). Model predictive control-based optimal control of primary frequency regulation power for hydrogen fuel cell-energy storage battery system. Renewable Energy, Elsevier, 248.
  • 19. Inga Espinoza, C. H., & Palma, M. T. (2025). A Coordinated Neuro-Fuzzy Control System for Hybrid Energy Storage Integration: Virtual Inertia and Frequency Support in Low-Inertia Power Systems. Energies, 18(17), 4728.
  • 20. Zhang, B., Iu, H., Zhang, X. & Chau, T.K. (2024). A NoisyNet deep reinforcement learning method for frequency regulation in power systems. IET Gener. Transm. Distrib., 18, 3042-3051.
  • 21. Nacar Çıkan, N. (2025). Distribution network reconfiguration for voltage profile enhancement and power loss reduction under hourly energy consumption using quantum approximate optimization algorithm. Cukurova University, Journal of the Faculty of Engineering, 40(1), 79-87.
  • 22. Çıkan, M. (2025). IEEE 13-baralı dengesiz üç fazlı güç dağıtım sistemlerinde yenilenebilir enerji kaynaklarının optimum konumlandırılması ve boyutlandırılması. Çukurova Üniversitesi, Mühendislik Fakültesi Dergisi, 40(1), 89-98.
  • 23. Kurucan, M., Michailidis, P., Michailidis, I. & Minelli, F. (2025). A modular hybrid soc-estimation framework with a supervisor for battery management systems supporting renewable energy integration in smart buildings. Energies, 18(17), 4537.
  • 24. Kurucan, M., Özbaltan, M., Yetgin, Z. & Alkaya, A. (2024). Applications of artificial neural network based battery management systems: A literature review. Renewable and Sustainable Energy Reviews, 192, 114262.
  • 25. Rydin Gorjão, L., Jumar, R., Maass, H., Hagenmeyer, V., Yalcin, G. C., Kruse, J., Timme, M., Beck, C., Witthaut, D. & Schafer, B. (2020). Open database analysis of scaling and spatio-temporal properties of power grid frequencies. Nat Commun., 11, 6362.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hesaplamalı Mantık ve Biçimsel Diller, Hesaplama Teorisi (Diğer), Elektrik Enerjisi Depolama
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Kurucan 0000-0003-4359-3726

Gönderilme Tarihi 31 Ocak 2026
Kabul Tarihi 19 Şubat 2026
Yayımlanma Tarihi 25 Mart 2026
DOI https://doi.org/10.21605/cukurovaumfd.1878644
IZ https://izlik.org/JA49ZH35XH
Yayımlandığı Sayı Yıl 2026 Cilt: 41 Sayı: 1

Kaynak Göster

APA Kurucan, M. (2026). Accurate Frequency Control of Energy Storage Systems with a Symbolic Game Theory Approach. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 41(1), 195-212. https://doi.org/10.21605/cukurovaumfd.1878644