Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2022 12:00 AM
End Date
7-1-2022 12:00 AM
Description
This paper addresses the design of local control methods for voltage control in distribution networks with high levels of distributed energy resources (DERs). The designed control methods modulate the active and reactive power output of DERs proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control; thus, the design focuses on determining the droop characteristics that satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging; hence, this paper proposes chance constraints to limit the risk from intermittent DERs by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.
Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks
Online
This paper addresses the design of local control methods for voltage control in distribution networks with high levels of distributed energy resources (DERs). The designed control methods modulate the active and reactive power output of DERs proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control; thus, the design focuses on determining the droop characteristics that satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging; hence, this paper proposes chance constraints to limit the risk from intermittent DERs by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.
https://aisel.aisnet.org/hicss-55/es/monitoring/3