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Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method

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  • Kyu-Hyung Jo

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea)

  • Mun-Kyeom Kim

    (Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea)

Abstract

In light of the dissemination of renewable energy connected to the power grid, it has become necessary to consider the uncertainty in the generation of renewable energy as a unit commitment (UC) problem. A methodology for solving the UC problem is presented by considering various uncertainties, which are assumed to have a normal distribution, by using a Monte Carlo simulation. Based on the constructed scenarios for load, wind, solar, and generator outages, a combination of scenarios is found that meets the reserve requirement to secure the power balance of the power grid. In those scenarios, the uncertainty integration method (UIM) identifies the best combination by minimizing the additional reserve requirements caused by the uncertainty of power sources. An integration process for uncertainties is formulated for stochastic unit commitment (SUC) problems and optimized by the improved genetic algorithm (IGA). The IGA is composed of five procedures and finds the optimal combination of unit status at the scheduled time, based on the determined source data. According to the number of unit systems, the IGA demonstrates better performance than the other optimization methods by applying reserve repairing and an approximation process. To account for the result of the proposed method, various UC strategies are tested with a modified 24-h UC test system and compared.

Suggested Citation

  • Kyu-Hyung Jo & Mun-Kyeom Kim, 2018. "Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method," Energies, MDPI, vol. 11(6), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1387-:d:149587
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    References listed on IDEAS

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    Cited by:

    1. Luis Montero & Antonio Bello & Javier Reneses, 2022. "A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods," Energies, MDPI, vol. 15(4), pages 1-40, February.
    2. Wook-Won Kim & Jong-Keun Park & Yong-Tae Yoon & Mun-Kyeom Kim, 2018. "Transmission Expansion Planning under Uncertainty for Investment Options with Various Lead-Times," Energies, MDPI, vol. 11(9), pages 1-19, September.
    3. Walter M. Villa-Acevedo & Jesús M. López-Lezama & Jaime A. Valencia-Velásquez, 2018. "A Novel Constraint Handling Approach for the Optimal Reactive Power Dispatch Problem," Energies, MDPI, vol. 11(9), pages 1-23, September.
    4. Mohammad Dehghani & Mohammad Mardaneh & Om P. Malik & Josep M. Guerrero & Carlos Sotelo & David Sotelo & Morteza Nazari-Heris & Kamal Al-Haddad & Ricardo A. Ramirez-Mendoza, 2020. "Genetic Algorithm for Energy Commitment in a Power System Supplied by Multiple Energy Carriers," Sustainability, MDPI, vol. 12(23), pages 1-23, December.
    5. Cristian Camilo Marín-Cano & Juan Esteban Sierra-Aguilar & Jesús M. López-Lezama & Álvaro Jaramillo-Duque & Walter M. Villa-Acevedo, 2019. "Implementation of User Cuts and Linear Sensitivity Factors to Improve the Computational Performance of the Security-Constrained Unit Commitment Problem," Energies, MDPI, vol. 12(7), pages 1-20, April.

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