Authors :
Swati K. Warungase; Dr. M.V. Bhatkar
Volume/Issue :
Volume 8 - 2023, Issue 2 - February
Scribd :
https://bit.ly/3YH3bhn
DOI :
https://doi.org/10.5281/zenodo.7730952
Abstract :
- Today’s energy market having flexibility for
any time transaction of electricity for buyers and sellers
due to open access. There are number of reason for
occurrence of contingency in transmission network like
generator failure, transmission line maintenance or
transaction of electricity. This contingency we can
mitigate by load shedding, generator rescheduling, or by
using distributed generations. Optimal placement of DGs
can be finding out by using Real Power Transmission
Congestion Distribution factors. In this research work,
Biomass Distributed Generations optimal placement is
found out with the consideration of uncertainty of solar
and winds DGs. As solar and wind generation is affected
by geographical location. Uncertainty of wind and solar
output is analyzed by Weibull probability distribution
function and Beta probability distribution function
respectively. By using Multi-objective Grey Wolf
Optimization, optimal size of Biomass DGs found out to
minimized Voltage stability Margin and Loss Margin
can be minimized. Standard IEEE-30 bus system is used
to validate performance of MO-GWO.
Keywords :
MO-GWO, Distributed Generations, RPTCDFs, Contingency.
- Today’s energy market having flexibility for
any time transaction of electricity for buyers and sellers
due to open access. There are number of reason for
occurrence of contingency in transmission network like
generator failure, transmission line maintenance or
transaction of electricity. This contingency we can
mitigate by load shedding, generator rescheduling, or by
using distributed generations. Optimal placement of DGs
can be finding out by using Real Power Transmission
Congestion Distribution factors. In this research work,
Biomass Distributed Generations optimal placement is
found out with the consideration of uncertainty of solar
and winds DGs. As solar and wind generation is affected
by geographical location. Uncertainty of wind and solar
output is analyzed by Weibull probability distribution
function and Beta probability distribution function
respectively. By using Multi-objective Grey Wolf
Optimization, optimal size of Biomass DGs found out to
minimized Voltage stability Margin and Loss Margin
can be minimized. Standard IEEE-30 bus system is used
to validate performance of MO-GWO.
Keywords :
MO-GWO, Distributed Generations, RPTCDFs, Contingency.