Frequency control for islanded AC microgrid based on deep reinforcement learning

References

• Hardan F, Norman R, Tricoli P. Control and operation of a ship AC/DC microgrid under transient propulsion and manoeuvring load conditions. Int J Electr Power Energy Syst. 2022;139:107823.
   Web of Science ®Google Scholar
• Mo NL, Guan ZH, Zhang DX, et al. Data-driven based optimal distributed frequency control for islanded AC microgrids. Int J Electr Power Energy Syst. 2020;119:105904.
   Web of Science ®Google Scholar
• Hu ZJ, Liu ZW, Li C, et al. A model-free distributed cooperative frequency control strategy for mt-HVDC systems using reinforcement learning method. J Franklin Inst. 2021;358(13):6490–6507.
   Web of Science ®Google Scholar
• Deng F, Mattavelli P, Zhang X. A distributed current sharing strategy for islanded ac microgrids based on low-bandwidth communication. Electr Power Syst Res. 2022;206:107777.
   Web of Science ®Google Scholar
• Khooban MH, Gheisarnejad M. A novel deep reinforcement learning controller based type-II fuzzy system: frequency regulation in microgrids. IEEE Trans Emerging Top Comput Intell. 2021;5(4):689–699.
   Google Scholar
• Bevrani H, Feizi MR, Ataee S. Robust frequency control in an islanded microgrid: h and μ synthesis approaches. IEEE Trans Smart Grid. 2016;7(2):706–717.
   Web of Science ®Google Scholar
• Yang C, Yao W, Fang J, et al. Dynamic event-triggered robust secondary frequency control for islanded AC microgrid. Appl Energy. 2019;242:821–836.
   Web of Science ®Google Scholar
• Manaz MM, Lu CN. Adaptive generation control for islanded ac microgrid frequency regulation. In: 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia (IFEEC 2017 - ECCE Asia); 2017. p. 2088–2093.
   Google Scholar
• Lal DK, Barisal AK, Tripathy M. Load frequency control of multi area interconnected microgrid power system using grasshopper optimization algorithm optimized fuzzy pid controller. In: 2018 Recent Advances on Engineering, Technology and Computational Sciences, Allahabad, India (RAETCS); 2018. p. 1–6.
   Google Scholar
• Chen X, Qu G, Tang Y, et al. Reinforcement learning for selective key applications in power systems: recent advances and future challenges. IEEE Trans Smart Grid. 2022;13(4):2935–2958.
   Web of Science ®Google Scholar
• Xiangyu Z, Gang Y, Nianjie T, et al. Regional power grid AGC control strategy research based on Q-learning algorithm under WACPB mode. In: 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia); 2019. p. 105–109.
   Google Scholar
• Park B, Zhang Y, Olama MM, et al. A model-free frequency control approach for diesel-wind powered microgrids. In: 2020 IEEE Power Energy Society General Meeting, Montreal, QC, Canada (PESGM); 2020. p. 1–5.
   Google Scholar
• Yan Z, Xu Y. A multi-agent deep reinforcement learning method for cooperative load frequency control of a multi-area power system. IEEE Trans Power Syst. 2020;35(6):4599–4608.
   Web of Science ®Google Scholar
• Chen C, Cui M, Li F, et al. Model-free emergency frequency control based on reinforcement learning. IEEE Trans Ind Inform. 2021;17(4):2336–2346.
   Web of Science ®Google Scholar
• Dayan P, Watkins CJ. Reinforcement learning. Stevens’ handbook of experimental psychology. 2002;3:103–129.
   Google Scholar
• Fu Q, Han Z, Chen J, et al. Applications of reinforcement learning for building energy efficiency control: a review. J Build Eng. 2022;50:104165.
   Web of Science ®Google Scholar
• Sutton RS, Barto AG . Introduction to reinforcement learning. Vol. 135, Massachusetts: MIT press Cambridge; 1998.
   Google Scholar
• Wiering MA, Van Otterlo M. Reinforcement learning. Adaptation, learning, and optimization. 2012;12(3):729.
   Google Scholar
• Yang Y, Yang P, Lin W, et al. A decentralized control for Non-error frequency regulation in an islanded microgrid containing multiple VSGs. Int J Electr Power Energy Syst. 2021;133:107337.
   Web of Science ®Google Scholar
• Park B, Zhang Y, Olama M, et al. Model-free control for frequency response support in microgrids utilizing wind turbines. Electr Power Syst Res. 2021;194:107080.
   Web of Science ®Google Scholar
• Yu C, Lu X, Lai J, et al. Distributed frequency restoration and SoC balancing control for AC microgrids. Int J Electr Power Energy Syst. 2022;136:107557.
   Web of Science ®Google Scholar
• Vidyanandan K, Senroy N. Frequency regulation in a wind–diesel powered microgrid using flywheels and fuel cells. IET Gener Transm Distrib. 2016;10(3):780–788.
   Web of Science ®Google Scholar
• Alghamdi B, Cañizares C. Frequency and voltage coordinated control of a grid of AC/DC microgrids. Appl Energy. 2022;310:118427.
   Web of Science ®Google Scholar
• Shayeghi H, Shayanfar H, Jalili A. Load frequency control strategies: a state-of-the-art survey for the researcher. Energy Convers Manag. 2009;50(2):344–353.
   Web of Science ®Google Scholar
• Zhao C, Topcu U, Li N, et al. Design and stability of load-side primary frequency control in power systems. IEEE Trans Autom Control. 2014;59(5):1177–1189.
   Web of Science ®Google Scholar
• Ray PK, Mohanty A. A robust firefly swarm hybrid optimization for frequency control in wind/PV/FC based microgrid. Appl Soft Comput. 2019;85:105823.
   Web of Science ®Google Scholar
• Lalor G, Mullane A, O’Malley M. Frequency control and wind turbine technologies. IEEE Trans Power Syst. 2005;20(4):1905–1913.
   Web of Science ®Google Scholar
• Zhou J, Xue S, Xue Y, et al. A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning. Energy. 2021;224:120118.
   Web of Science ®Google Scholar
• Zhang F, Li J, Li Z. A TD3-based multi-agent deep reinforcement learning method in mixed cooperation-competition environment. Neurocomputing. 2020;411:206–215.
   Web of Science ®Google Scholar
• Xu Y, Yan R, Wang Y, et al. A multi-agent quantum deep reinforcement learning method for distributed frequency control of islanded microgrids. IEEE Trans Control Network Syst. 2022; 1: 1–1.
   Google Scholar