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Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm

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  • Muhyaddin Rawa

    (Smart Grids Research Group, Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Department of Electrical and Computer Engineering, Faculty of Engineering, K. A. CARE Energy Research and Innovation Center, Jeddah 21589, Saudi Arabia)

Abstract

In this paper, a transmission expansion planning (TEP) model is proposed to guarantee the resilience of power systems and mitigate cascading failures’ impacts. The energy storage systems and fault current limiters’ planning models are integrated into the TEP problem to minimize cascading outages and comply with short-circuit current reliability constraints. Most studies in the literature adopt a single strategy to simulate power systems’ cascading failures that may not be enough to guarantee networks’ resilience. This work elaborates on two scenarios for initiating cascading failures to study the impact of various initiating events on the planned system’s strength and the projects required. The TEP problem is formulated as a non-linear, non-convex large-scale problem. To avoid linearization issues and enhance meta-heuristics performance, a hybridization of two meta-heuristic techniques, namely snake optimizer and sine cosine algorithm (SO-SCA), is proposed to solve the problem. Two hybridization strategies are suggested to improve the exploration and exploitation stages. Defining future loads growth is essential for TEP. Hence, a load forecasting technique based on SO-SCA is investigated and compared with some methods reported in the literature. The results obtained proved the efficiency of the proposed approach in predicting load growth. TEP’s calculations were carried out on the Garver and the IEEE 24-bus system. The results demonstrated the superiority of the hybrid SO-SCA in solving the TEP problem. Moreover, the projects required to expand networks differed according to the type of cascading failures’ initiating scenario.

Suggested Citation

  • Muhyaddin Rawa, 2022. "Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm," Mathematics, MDPI, vol. 10(8), pages 1-25, April.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1323-:d:795221
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    References listed on IDEAS

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    1. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    2. Abdulaziz Almalaq & Khalid Alqunun & Mohamed M. Refaat & Anouar Farah & Fares Benabdallah & Ziad M. Ali & Shady H. E. Abdel Aleem, 2022. "Towards Increasing Hosting Capacity of Modern Power Systems through Generation and Transmission Expansion Planning," Sustainability, MDPI, vol. 14(5), pages 1-26, March.
    3. Xie, Yuying & Li, Chaoshun & Tang, Geng & Liu, Fangjie, 2021. "A novel deep interval prediction model with adaptive interval construction strategy and automatic hyperparameter tuning for wind speed forecasting," Energy, Elsevier, vol. 216(C).
    4. Neshat, Mehdi & Nezhad, Meysam Majidi & Abbasnejad, Ehsan & Mirjalili, Seyedali & Groppi, Daniele & Heydari, Azim & Tjernberg, Lina Bertling & Astiaso Garcia, Davide & Alexander, Bradley & Shi, Qinfen, 2021. "Wind turbine power output prediction using a new hybrid neuro-evolutionary method," Energy, Elsevier, vol. 229(C).
    5. Hamdi Abdi & Mansour Moradi & Sara Lumbreras, 2021. "Metaheuristics and Transmission Expansion Planning: A Comparative Case Study," Energies, MDPI, vol. 14(12), pages 1-23, June.
    6. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    7. Mohamed M. Refaat & Shady H. E. Abdel Aleem & Yousry Atia & Ziad M. Ali & Adel El-Shahat & Mahmoud M. Sayed, 2021. "A Mathematical Approach to Simultaneously Plan Generation and Transmission Expansion Based on Fault Current Limiters and Reliability Constraints," Mathematics, MDPI, vol. 9(21), pages 1-21, November.
    8. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
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    Cited by:

    1. Hongyan Dui & Yuheng Yang & Yun-an Zhang & Yawen Zhu, 2022. "Recovery Analysis and Maintenance Priority of Metro Networks Based on Importance Measure," Mathematics, MDPI, vol. 10(21), pages 1-20, October.

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