IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6373-d1231665.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6373/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/17/6373/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hung, Duong Quoc & Mithulananthan, N. & Bansal, R.C., 2014. "An optimal investment planning framework for multiple distributed generation units in industrial distribution systems," Applied Energy, Elsevier, vol. 124(C), pages 62-72.
    2. Bogdan Tomoiagă & Mircea Chindriş & Andreas Sumper & Antoni Sudria-Andreu & Roberto Villafafila-Robles, 2013. "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II," Energies, MDPI, vol. 6(3), pages 1-17, March.
    3. Dong, Zhen & Li, Zhongguo & Liang, Zhongchao & Xu, Yiqiao & Ding, Zhengtao, 2021. "Distributed neural network enhanced power generation strategy of large-scale wind power plant for power expansion," Applied Energy, Elsevier, vol. 303(C).
    4. Hung, Duong Quoc & Mithulananthan, N., 2014. "Loss reduction and loadability enhancement with DG: A dual-index analytical approach," Applied Energy, Elsevier, vol. 115(C), pages 233-241.
    5. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    6. Schönleber, Kevin & Collados, Carlos & Pinto, Rodrigo Teixeira & Ratés-Palau, Sergi & Gomis-Bellmunt, Oriol, 2017. "Optimization-based reactive power control in HVDC-connected wind power plants," Renewable Energy, Elsevier, vol. 109(C), pages 500-509.
    7. Jae-Kun Lyu & Jae-Haeng Heo & Jong-Keun Park & Yong-Cheol Kang, 2013. "Probabilistic Approach to Optimizing Active and Reactive Power Flow in Wind Farms Considering Wake Effects," Energies, MDPI, vol. 6(11), pages 1-21, October.
    8. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    9. Wang, Ni & Li, Jian & Hu, Weihao & Zhang, Baohua & Huang, Qi & Chen, Zhe, 2019. "Optimal reactive power dispatch of a full-scale converter based wind farm considering loss minimization," Renewable Energy, Elsevier, vol. 139(C), pages 292-301.
    10. Zhang, Qiang & Ogren, Ryan M. & Kong, Song-Charng, 2016. "A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA," Applied Energy, Elsevier, vol. 165(C), pages 676-684.
    11. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    12. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    13. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2019. "Optimal Capacitor Bank Allocation in Electricity Distribution Networks Using Metaheuristic Algorithms," Energies, MDPI, vol. 12(22), pages 1-36, November.
    14. Hung, Duong Quoc & Mithulananthan, N. & Bansal, R.C., 2014. "Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability," Applied Energy, Elsevier, vol. 113(C), pages 1162-1170.
    15. Zelan Li & Yijia Cao & Le Van Dai & Xiaoliang Yang & Thang Trung Nguyen, 2019. "Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm," Energies, MDPI, vol. 12(15), pages 1-31, August.
    16. Lucas do Carmo Yamaguti & Juan Manuel Home-Ortiz & Mahdi Pourakbari-Kasmaei & José Roberto Sanches Mantovani, 2023. "Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation," Energies, MDPI, vol. 16(12), pages 1-21, June.
    17. Biswas (Raha), Syamasree & Mandal, Kamal Krishna & Chakraborty, Niladri, 2016. "Pareto-efficient double auction power transactions for economic reactive power dispatch," Applied Energy, Elsevier, vol. 168(C), pages 610-627.
    18. Gengli Song & Hua Wei, 2022. "Distributionally Robust Multi-Energy Dynamic Optimal Power Flow Considering Water Spillage with Wasserstein Metric," Energies, MDPI, vol. 15(11), pages 1-18, May.
    19. Manbachi, Moein & Farhangi, Hassan & Palizban, Ali & Arzanpour, Siamak, 2016. "Smart grid adaptive energy conservation and optimization engine utilizing Particle Swarm Optimization and Fuzzification," Applied Energy, Elsevier, vol. 174(C), pages 69-79.
    20. Alencar, Marina Valença & da Silva, Diego Nunes & Nepomuceno, Leonardo & Martins, André Christóvão Pio & Balbo, Antonio Roberto & Soler, Edilaine Martins, 2024. "Discrete optimal power flow with prohibited zones, multiple-fuel options, and practical operational rules for control devices," Applied Energy, Elsevier, vol. 358(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6373-:d:1231665. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.