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Abstract This paper proposes a real-time (RT) optimization strategy for managing reactive power reserves in islanded power systems, explicitly maintaining safe margins from voltage limits. Existing formulations, dominated by objective functions aimed at minimizing losses, are shown to systematically drive bus voltages close to their upper admissible limits, potentially reducing operational margins and increasing the risk of overvoltage tripping. This work conducts a comparative study using three alternative multi-objective functions aimed at reducing power losses and mitigating voltage saturation: one previously proposed in the literature and two new formulations presented in this paper to overcome the limitations identified in the first. The analysis is conducted within a realistic real-time hardware-in-the-loop (HIL) optimization laboratory environment (RTOLab) and assessed over a full-day operation with a 5-minute execution interval. The optimization problem computes reactive power and voltage setpoints for power plant controllers in real time and performs redispatch actions as necessary to ensure secure operation. Results demonstrate that the two multi-objective functions proposed in this paper, aimed at avoiding voltage limits or penalizing reactive power reserve utilization, have a limited impact on system losses and exhibit operational robustness in RT optimization–based applications. Key words: Optimal reactive power management, voltage control, optimal control setpoints, real-time optimization, hardware-in-the-loop simulation.
References [1] J. Wang, J. Comden, and A. Bernstein, Real Time–Optimal Power Flow–Based Distributed Energy Resource Management System (DERMS), Golden, CO, USA: National Renewable Energy Laboratory (NREL), Tech. Rep. NREL/TP-5D00-87767, May 2024. [2] E. Mohagheghi, M. Alramlawi, A. Gabash, and P. Li, “A survey of real-time optimal power flow,” Energies, vol. 11, no. 11, Art. no. 3142, 2018, doi: 10.3390/en11113142. [3] Z. Yan and Y. Xu, "Real-Time Optimal Power Flow: A Lagrangian Based Deep Reinforcement Learning Approach," in IEEE Transactions on Power Systems, vol. 35, no. 4, pp. 3270-3273, July 2020, doi: 10.1109/TPWRS.2020.2987292. [4] P. A. Dratsas, G. N. Psarros, and S. A. Papathanassiou, “A real-time redispatch method to evaluate the contribution of storage to capacity adequacy,” IEEE Transactions on Power Systems, vol. 39, no. 1, pp. 1274–1284, Jan. 2024, doi: 10.1109/TPWRS.2023.3243669. [5] C. M. Martín, F. Arredondo, S. Arnaltes, J. Alonso-Martínez and J. L. R. Amenedo, "Optimal Re-Dispatch and Reactive Power Management in the Fuerteventura-Lanzarote Grid Using Real-Time Optimization in the Loop," in 2024 IEEE 15th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Luxembourg, Luxembourg, 2024, pp. 1-6, doi: 10.1109/PEDG61800.2024.10667466. [6] O. S. T. A. Butti, M. Burunkaya, J. Rahebi and J. M. Lopez-Guede, "Optimal Power Flow Using PSO Algorithms Based on Artificial Neural Networks," in IEEE Access, vol. 12, pp. 154778-154795, 2024, doi: 10.1109/ACCESS.2024.3479097. [7] Y. Tang, K. Dvijotham and S. Low, "Real-Time Optimal Power Flow," in IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2963-2973, Nov. 2017, doi: 10.1109/TSG.2017.2704922. [8] L. Rouco, F. M. Echavarren, and E. Lobato, “The overvoltage-driven blackout of the Iberian Peninsula on 28th April 2025,” Sustainable Energy, Grids and Networks, vol. 45, Art. no. 102125, 2026, doi: 10.1016/j.segan.2026.102125. [9] E. Mohagheghi, A. Gabash, M. Alramlawi, and P. Li, “Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm,” Renewable Energy, vol. 126, pp. 509–523, 2018, doi: 10.1016/j.renene.2018.03.072. [10] K. Khatua and N. Yadav, “Voltage stability enhancement using VSC-OPF including wind farms based on genetic algorithm,” International Journal of Electrical Power & Energy Systems, vol. 73, pp. 560–567, 2015, doi: 10.1016/j.ijepes.2015.05.007. [11] M. Ntombela, K. Musasa, and M. C. Leoaneka, “Power loss minimization and voltage profile improvement by system reconfiguration, DG sizing, and placement,” Computation, vol. 10, no. 10, Art. no. 180, 2022, doi: 10.3390/computation10100180. [12] P. Mundra, A. Arya, and S. K. Gawre, “A multi-objective optimization based optimal reactive power reward for voltage stability improvement in uncertain power system,” Journal of Electrical Engineering & Technology, vol. 16, pp. 1–13, 2021, doi: 10.1007/s42835-021-00827-0. [13] S. S. Reddy and P. R. Bijwe, “Day-ahead and real time optimal power flow considering renewable energy resources,” International Journal of Electrical Power & Energy Systems, vol. 82, pp. 400–408, 2016, doi: 10.1016/j.ijepes.2016.03.033.
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