Summary:
Ever increasing use of renewable energies as uncertainty resources has caused the power system state to become highly unpredictable. Accurate prediction of the power system state is very important, especially in operational decisions, market contracts and risk management.
This paper presents a probabilistic optimal power flow (P-OPF) in order to maximize the predictability of the system while minimizing the total cost of power generation. The well-known non-dominated sorting genetic algorithm (NSGA) is used to manage multiple objective functions considering operational constraints. In order to show the efficiency of the proposed method, the IEEE 30-bus standard test system is selected as a case study and the results are presented. The importance of this study and efficiency of the proposed method are discussed comprehensively.
Keywords: Multi objective optimization; Non-dominated sorting genetic algorithm; Probabilistic optimal power flow; Predictability; Two point estimation method
JCR Impact Factor and WoS quartile: 3,211 - Q2 (2019); 3,300 - Q2 (2023)
DOI reference:
https://doi.org/10.1016/j.epsr.2019.02.011
Published on paper: June 2019.
Published on-line: February 2019.
Citation:
S. Galvani, S. Rezaeian-Marjani, Optimal power flow considering predictability of power systems. Electric Power Systems Research. Vol. 171, pp. 66 - 73, June 2019. [Online: February 2019]
