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A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework

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  • Heejung Park

    (School of Energy Engineering, Kyungpook National University, Daegu 41566, Korea)

Abstract

Grid integration of renewable resources such as solar and wind energy can significantly raise the level of uncertainty in power systems, making the scheduled operation of generating units difficult. Therefore, the importance of operating reserves is more emphasized to prevent disruption by sudden changes in outputs of generators. In this paper, a stochastic unit commitment (UC) model to reflect uncertainty due to a large amount of renewable resources is proposed, in which upward and downward operating reserves are deployed simultaneously, and feasibility of the reserves is examined to make the deployed reserves supplied reliably. Uncertain parameters considered in the model are wind power availability, solar direct normal irradiance, and electric load. Two-stage stochastic programming is applied to the mathematical formulation, where UC decisions including dispatch are modeled as non-anticipative variables at the first stage, and redispatch decisions to serve realized electric demand are made at the second stage as recourse. By solving the UC problem, feasible and reliable stochastic UC and dispatch solutions can be provided to power system operators.

Suggested Citation

  • Heejung Park, 2022. "A Unit Commitment Model Considering Feasibility of Operating Reserves under Stochastic Optimization Framework," Energies, MDPI, vol. 15(17), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6221-:d:898615
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    References listed on IDEAS

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    Cited by:

    1. Ebrie, Awol Seid & Kim, Young Jin, 2024. "Reinforcement learning-based optimization for power scheduling in a renewable energy connected grid," Renewable Energy, Elsevier, vol. 230(C).
    2. Sebastian, Oliva H. & Carlos, Bahamonde D., 2024. "Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints," Renewable Energy, Elsevier, vol. 232(C).
    3. 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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