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Open AccessArticle

Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method

Department of Energy System Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
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Energies 2018, 11(6), 1387; https://doi.org/10.3390/en11061387
Received: 3 May 2018 / Revised: 24 May 2018 / Accepted: 28 May 2018 / Published: 29 May 2018
(This article belongs to the Special Issue Electric Power Systems Research 2018)
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. View Full-Text
Keywords: uncertainty integration method; unit commitment; scenario integration technique; improved genetic algorithm; operating cost uncertainty integration method; unit commitment; scenario integration technique; improved genetic algorithm; operating cost
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MDPI and ACS Style

Jo, K.-H.; Kim, M.-K. Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method. Energies 2018, 11, 1387. https://doi.org/10.3390/en11061387

AMA Style

Jo K-H, Kim M-K. Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method. Energies. 2018; 11(6):1387. https://doi.org/10.3390/en11061387

Chicago/Turabian Style

Jo, Kyu-Hyung; Kim, Mun-Kyeom. 2018. "Improved Genetic Algorithm-Based Unit Commitment Considering Uncertainty Integration Method" Energies 11, no. 6: 1387. https://doi.org/10.3390/en11061387

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