Short-Term Line Maintenance Scheduling of Distribution Network With PV Penetration Considering Uncertainties

• Published in: IEEE access Vol. 6; pp. 33621 - 33630
• Main Authors: Bao, Zhejing, Gui, Canzhi, Guo, Xiaogang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Chance-constrained programming; Clouds; Constraints; Economic forecasting; Electric power demand; Electricity consumption; Failure rates; Forecasting; fuzzy; Hybrid systems; Irradiance; Maintenance engineering; Maintenance management; maintenance scheduling; Optimization; Penetration; random; Randomness; Reliability; renewable energy; Scheduling; Solar power generation; System effectiveness; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2838082

In this paper, the methodology for short-term line maintenance scheduling in distribution network with PV penetration is proposed, in which the mixed randomness and fuzziness of solar power generation concerning the available cloud amount forecasting, along with other random or fuzzy factors, such as electricity demand and component historical failure rate, are considered. First, based on the obtained historical data from NASA, the empirical mapping from cloud amount to solar irradiance can be established where the uncertainty is represented by combining randomness and fuzziness. Second, the short-term line maintenance scheduling of distribution network with uncertainties is modeled by using random fuzzy chance-constrained programming aiming at minimizing the pessimistic value in terms of economics and reliability subject to the chance constraints. Finally, a hybrid intelligent algorithm with two-layer optimization is implemented to solve the model. Simulation experiments are carried out on the IEEE 33-Bus and IEEE RBTS 2-Bus systems, and the results demonstrate the effectiveness of the proposed short-term line maintenance scheduling solution for distribution network with the mixed uncertainties of randomness and fuzziness.