JavaScript is disabled for your browser. Some features of this site may not work without it.
Please be advised that the site will be down for maintenance on Sunday, September 1, 2024, from 08:00 to 18:00, and again on Monday, September 2, 2024, from 08:00 to 09:00. We apologize for any inconvenience this may cause.
A heuristic optimal approach for coordinated volt/var control in distribution networks
This dissertation focuses on daily volt/var control in distribution networks with feeder capacitors,
substation capacitors and transformers equipped with on-load tap changers. A hybrid
approach is proposed to solve the daily volt/var control problem. To reduce the computational
requirements of the problem, this approach combines two methods, namely heuristic
and optimal scheduling for the substation and feeder sub-problems respectively.
The feeder capacitor dispatch schedule is determined based on a heuristic reactive power setpoint
method. At this stage the objective is to minimize the reactive power flow through the
substation bus in every time-interval. And as such, mathematical modeling of the distribution
network components is adapted to suit time-varying conditions. Furthermore, an optimization
model to determine a proper dispatch schedule of the substation devices is formulated.
The objective of this model is to minimize the daily total energy loss and voltage deviations.
Additionally, the reference voltage of the substation secondary bus and the transformer tap
position limits are modified to adapt to given load profiles. The optimization model is solved
with a discrete particle swarm optimization algorithm, which incorporates Newton’s method
to determine the power-flow solution. The proposed method is applied to a time-varying distribution system and evaluated under
different operational scenarios. It is also compared to on-line volt/var control with various
settings. Simulation results show that the proposed approach minimizes both the voltage deviations
and the total energy loss, while on-line control prioritizes one objective over the other depending
on the specified settings.
Description:
Dissertation (MEng)--University of Pretoria, 2015.