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Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems

Author

Listed:
  • Martín M. Sánchez-Mora

    (Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Faculty of Engineering, University of Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia)

  • Walter M. Villa-Acevedo

    (Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Faculty of Engineering, University of Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia)

  • Jesús M. López-Lezama

    (Research Group on Efficient Energy Management (GIMEL), Department of Electrical Engineering, Faculty of Engineering, University of Antioquia, Calle 70 No 52-21, Medellín 050010, Colombia)

Abstract

Factors such as persistent demand growth, expansion project delays, and the rising adoption of renewable energy sources highlight the importance of operating power systems within safe operational margins. The optimal reactive power dispatch (ORPD) seeks to find operating points that allow greater flexibility in reactive power reserves, thus ensuring the safe operation of power systems. The main contribution of this paper is a multi-area and multi-period ORPD (MA-MP-ORPD) model, which seeks the minimization of the voltage deviation in pilot nodes, the reactive power deviation of shunt elements, and the total reactive power generated, all taking into account the operational constraints for each area. The MA-MP-ORPD was implemented in the Python programming language using the Pyomo library; furthermore, the BONMIN solver was employed to solve this mixed-integer nonlinear programming problem. The problem was formulated from the standpoint of the system operator; therefore, it minimizes the variations of critical variables from the desired operative values; furthermore, the number of maneuvers of the reactive compensation elements was also minimized to preserve their lifetimes. The results obtained on IEEE test systems of 39 and 57 buses validated its applicability and effectiveness. The proposed approach allowed obtaining increases in the reactive power reserves of up to 59% and 62% for the 39- and 57-bus test systems, respectively, while ensuring acceptable operation values of the critical variables.

Suggested Citation

  • Martín M. Sánchez-Mora & Walter M. Villa-Acevedo & Jesús M. López-Lezama, 2023. "Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems," Energies, MDPI, vol. 16(17), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6373-:d:1231665
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    References listed on IDEAS

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    1. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Ginidi, Ahmed R., 2022. "An improved heap optimization algorithm for efficient energy management based optimal power flow model," Energy, Elsevier, vol. 250(C).
    2. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Jaime A. Valencia-Velásquez, 2018. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem," Energies, MDPI, vol. 11(9), pages 1-23, September.
    3. Martinez-Rojas, Marcela & Sumper, Andreas & Gomis-Bellmunt, Oriol & Sudrià-Andreu, Antoni, 2011. "Reactive power dispatch in wind farms using particle swarm optimization technique and feasible solutions search," Applied Energy, Elsevier, vol. 88(12), pages 4678-4686.
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