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An efficient probabilistic-chronological matching modeling for DG planning and reliability assessment in power distribution systems

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  • Alotaibi, Majed A.
  • Salama, M.M.A.

Abstract

In recent decades, power distribution systems have encountered a considerable shift toward utilizing renewable resource based distributed generation (DG) systems. This is due to the proven ability of DGs to reduce fossil fuel consumption, which reduces harm done to the environment. In this paper, a new state reduction algorithm is proposed to determine the minimum number of states required to describe or represent the behavior of wind speed and solar irradiance in DG planning problems and reliability analysis. This algorithm could be generalized to incorporate any planning problem where wind or PV power is part of its parameters. Moreover, an adequate time representation that mimics the fluctuation of renewable resource based DGs and chronologically matches the fluctuations in system demand is presented. Three different data clusters are applied (monthly, seasonal and yearly) to investigate the variability of DG power output and electricity demand on both DG planning problems and reliability assessment. These models are evaluated considering DG siting and sizing problems, as well as a supply adequacy-based reliability assessment. The proposed model measures the deviations in annual energy losses (AEL), total DG penetration, loss of load expectation (LOLE), and loss of energy expectation (LOEE).

Suggested Citation

  • Alotaibi, Majed A. & Salama, M.M.A., 2016. "An efficient probabilistic-chronological matching modeling for DG planning and reliability assessment in power distribution systems," Renewable Energy, Elsevier, vol. 99(C), pages 158-169.
    • Handle: RePEc:eee:renene:v:99:y:2016:i:c:p:158-169
    DOI: 10.1016/j.renene.2016.06.058
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    References listed on IDEAS

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    2. Canizes, Bruno & Soares, João & Lezama, Fernando & Silva, Cátia & Vale, Zita & Corchado, Juan M., 2019. "Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generation," Renewable Energy, Elsevier, vol. 138(C), pages 937-954.
    3. Mokryani, Geev & Hu, Yim Fun & Papadopoulos, Panagiotis & Niknam, Taher & Aghaei, Jamshid, 2017. "Deterministic approach for active distribution networks planning with high penetration of wind and solar power," Renewable Energy, Elsevier, vol. 113(C), pages 942-951.
    4. Chi, Zhexiang & Chen, Ruoran & Huang, Simin & Li, Yan-Fu & Zhou, Bin & Zhang, Wenjuan, 2020. "Multi-State System Modeling and Reliability Assessment for Groups of High-speed Train Wheels," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Zhou, Siyu & Han, Yang & Mahmoud, Karar & Darwish, Mohamed M.F. & Lehtonen, Matti & Yang, Ping & Zalhaf, Amr S., 2023. "A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation," Applied Energy, Elsevier, vol. 348(C).
    6. Mahesh Kumar & Perumal Nallagownden & Irraivan Elamvazuthi, 2017. "Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems," Energies, MDPI, vol. 10(6), pages 1-25, June.
    7. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.
    8. Ahmed A. Alguhi & Majed A. Alotaibi & Essam A. Al-Ammar, 2023. "Probabilistic Planning for an Energy Storage System Considering the Uncertainties in Smart Distribution Networks," Sustainability, MDPI, vol. 16(1), pages 1-23, December.
    9. Zhou, Siyu & Han, Yang & Chen, Shuheng & Yang, Ping & Mahmoud, Karar & Darwish, Mohamed M.F. & Matti, Lehtonen & Zalhaf, Amr S., 2023. "A multiple uncertainty-based Bi-level expansion planning paradigm for distribution networks complying with energy storage system functionalities," Energy, Elsevier, vol. 275(C).
    10. Yang, Shangrong & Yan, Rujing & Zhang, Jing & Wang, Jiangjiang & Bai, Zhang & He, Yu & Hu, Keling & Ao, Xuan, 2025. "Nested long-short-term temporal compression optimization for high-renewable integrated energy systems with hydrogen storage and heat recovery," Energy, Elsevier, vol. 333(C).

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