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Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications

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  • Ying-Yi Hong

    (Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan)

  • Gerard Francesco DG. Apolinario

    (Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 32023, Taiwan
    Electrical Engineering Department, Technological Institute of the Philippines, Manila 1001, Philippines)

Abstract

The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

Suggested Citation

  • Ying-Yi Hong & Gerard Francesco DG. Apolinario, 2021. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications," Energies, MDPI, vol. 14(20), pages 1-47, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6658-:d:656355
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    Cited by:

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    2. 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).
    3. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    4. Zhenhuan Ding & Xiaoge Huang & Zhao Liu, 2022. "Active Exploration by Chance-Constrained Optimization for Voltage Regulation with Reinforcement Learning," Energies, MDPI, vol. 15(2), pages 1-17, January.
    5. Juseung Choi & Hoyong Eom & Seung-Mook Baek, 2022. "A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation," Energies, MDPI, vol. 15(24), pages 1-17, December.

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