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Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory

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  • Nikoobakht, Ahmad
  • Aghaei, Jamshid
  • Mardaneh, Mohammad

Abstract

In this paper, a robust solution algorithm has been presented for SCUC problem including the uncertainties of wind power generation (WPG). Different flexibility resources have been considered in the proposed model, namely, transmission switching (TS) action and energy storage (ES) systems for managing the uncertainty risk of significant penetration of WPGs to sustain the load-generation balance. Indeed, the flexibility resources have been allocated using a robust optimization model based on the information gap decision theory (IGDT). That is, the IGDT reveals the robust optimal decision variables against the wind power uncertainties without requiring the probability density function of WPGs that leads to low computational burden. The optimization SCUC model is formulated as a mixed-integer linear programming (MIP), being solved by proposed effective solution strategy based on Benders decomposition (BD) technique. Numerical simulation results on the modified 6-bus system and IEEE 118-bus system, clearly demonstrate the benefits of applying flexibility resources for managing the high penetration of WPG uncertainty and validate the applicability of the proposed IGDT-based SCUC model.

Suggested Citation

  • Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
    • Handle: RePEc:eee:energy:v:114:y:2016:i:c:p:846-861
    DOI: 10.1016/j.energy.2016.08.070
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    Cited by:

    1. Hong Zhang & Hao Sun & Qian Zhang & Guanxun Kong, 2018. "Microgrid Spinning Reserve Optimization with Improved Information Gap Decision Theory," Energies, MDPI, vol. 11(9), pages 1-17, September.
    2. Long, Huan & Zhang, Zijun & Sun, Mu-Xia & Li, Yan-Fu, 2018. "The data-driven schedule of wind farm power generations and required reserves," Energy, Elsevier, vol. 149(C), pages 485-495.
    3. Peker, Meltem & Kocaman, Ayse Selin & Kara, Bahar Y., 2018. "Benefits of transmission switching and energy storage in power systems with high renewable energy penetration," Applied Energy, Elsevier, vol. 228(C), pages 1182-1197.
    4. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2017. "Securing highly penetrated wind energy systems using linearized transmission switching mechanism," Applied Energy, Elsevier, vol. 190(C), pages 1207-1220.
    5. Wang, Sen & Li, Fengting & Zhang, Gaohang & Yin, Chunya, 2023. "Analysis of energy storage demand for peak shaving and frequency regulation of power systems with high penetration of renewable energy," Energy, Elsevier, vol. 267(C).
    6. Alabi, Tobi Michael & Lu, Lin & Yang, Zaiyue, 2021. "A novel multi-objective stochastic risk co-optimization model of a zero-carbon multi-energy system (ZCMES) incorporating energy storage aging model and integrated demand response," Energy, Elsevier, vol. 226(C).
    7. Razavi, Seyed-Ehsan & Esmaeel Nezhad, Ali & Mavalizadeh, Hani & Raeisi, Fatima & Ahmadi, Abdollah, 2018. "Robust hydrothermal unit commitment: A mixed-integer linear framework," Energy, Elsevier, vol. 165(PB), pages 593-602.
    8. Nomaguchi, Yutaka & Tanaka, Hiroki & Sakakibara, Akiyuki & Fujita, Kikuo & Kishita, Yusuke & Hara, Keishiro & Uwasu, Michinori, 2017. "Integrated planning of low-voltage power grids and subsidies toward a distributed generation system – Case study of the diffusion of photovoltaics in a Japanese dormitory town," Energy, Elsevier, vol. 140(P1), pages 779-793.

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