TY - GEN
T1 - Analysis of Power Network Stabilities Considering the Uncertainty of Large Renewable Power Generations
AU - Cho, Haesong
AU - Yi, Sung Wook
AU - Kwon, Do Hoon
N1 - Publisher Copyright:
© 2023 ICROS.
PY - 2023
Y1 - 2023
N2 - The South Korea's government declared that it will increase renewable power's share of the energy mix from its current level of 7 % to 20 % by 2030. An increase of the renewable power means that fluctuations and uncertainties of power generation are increased, which brings new challenges of power network stability analysis to the system operators. This paper presents a one of the methods for power network stability analyses considering the uncertainty of large renewable power generations in the Korea Electrotechnology Research Institute (KERI). The proposed method forecasts the renewable power generations with probabilistic ranges instead of a point prediction for next time step. A power network modelling is then conducted automatically several times using forecasted loads and the renewable power generations having probabilistic ranges. Finally, the power network stability analyses are conducted automatically for every forecasted power network models. The results are obtained by number of violations related to transmission line overloads and under- or over-voltage buses as well as fault currents of several buses. Simulation case studies are performed using a PSS/E and the case study results confirm that the proposed method successfully conducts the power network stability analyses considering the uncertainty of large renewable power generations.
AB - The South Korea's government declared that it will increase renewable power's share of the energy mix from its current level of 7 % to 20 % by 2030. An increase of the renewable power means that fluctuations and uncertainties of power generation are increased, which brings new challenges of power network stability analysis to the system operators. This paper presents a one of the methods for power network stability analyses considering the uncertainty of large renewable power generations in the Korea Electrotechnology Research Institute (KERI). The proposed method forecasts the renewable power generations with probabilistic ranges instead of a point prediction for next time step. A power network modelling is then conducted automatically several times using forecasted loads and the renewable power generations having probabilistic ranges. Finally, the power network stability analyses are conducted automatically for every forecasted power network models. The results are obtained by number of violations related to transmission line overloads and under- or over-voltage buses as well as fault currents of several buses. Simulation case studies are performed using a PSS/E and the case study results confirm that the proposed method successfully conducts the power network stability analyses considering the uncertainty of large renewable power generations.
KW - large renewable power
KW - power network stability
KW - probabilistic forecast
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85179179207&partnerID=8YFLogxK
U2 - 10.23919/ICCAS59377.2023.10316905
DO - 10.23919/ICCAS59377.2023.10316905
M3 - Conference contribution
AN - SCOPUS:85179179207
T3 - International Conference on Control, Automation and Systems
SP - 1797
EP - 1801
BT - 23rd International Conference on Control, Automation and Systems, ICCAS 2023
PB - IEEE Computer Society
T2 - 23rd International Conference on Control, Automation and Systems, ICCAS 2023
Y2 - 17 October 2023 through 20 October 2023
ER -