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Reliability evaluation of a composite power system in the presence of renewable generations

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  • Firouzi, Mohsen
  • Samimi, Abouzar
  • Salami, Abolfazl

Abstract

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved Estimation of Distribution Algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability Distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

Suggested Citation

  • Firouzi, Mohsen & Samimi, Abouzar & Salami, Abolfazl, 2022. "Reliability evaluation of a composite power system in the presence of renewable generations," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000710
    DOI: 10.1016/j.ress.2022.108396
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    3. Rusin, Andrzej & Wojaczek, Adam, 2023. "Changes in the structure of the Polish energy mix in the transition period to ensure the safety and reliability of energy supplies," Energy, Elsevier, vol. 282(C).
    4. Stover, Oliver & Karve, Pranav & Mahadevan, Sankaran, 2023. "Reliability and risk metrics to assess operational adequacy and flexibility of power grids," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Siripha Junlakarn & Radhanon Diewvilai & Kulyos Audomvongseree, 2022. "Stochastic Modeling of Renewable Energy Sources for Capacity Credit Evaluation," Energies, MDPI, vol. 15(14), pages 1-27, July.
    6. Guo, Yongjin & Wang, Hongdong & Guo, Yu & Zhong, Mingjun & Li, Qing & Gao, Chao, 2022. "System operational reliability evaluation based on dynamic Bayesian network and XGBoost," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    7. Wang, Chaonan & Wang, Shuli & Xing, Liudong & Guan, Quanlong, 2023. "Efficient performability analysis of dynamic multi-state k-out-of-n: G systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Jerzy Andruszkiewicz & Józef Lorenc & Agnieszka Weychan, 2023. "Determination of the Optimal Level of Reactive Power Compensation That Minimizes the Costs of Losses in Distribution Networks," Energies, MDPI, vol. 17(1), pages 1-24, December.

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