Abstract:
This thesis focused on distributed load control on six feeders connected to the 132 kV
Akure injection substation. There are two algorithms, one on the substation controller
coordinator and the other, on the consumer premises. In both algorithms, distributed
load control was considered without considering network constraints. Considering
algorithm I,the uncertainties in electrical energy distribution and load activations were
ignored, (which optimally schedules the schedulable loads to shape the net electricity
demand) in order to eradicate feeder failures often experienced as a result of peak
demand. Algorithm IIis a real-time algorithm (based on model-predictive control)
implementedto update predictions on electrical energy distribution as the true values,
and computes a pseudo load that aids simulation of future schedulable load. The
pseudo load consumes very low power at the pointtime of simulation, and its total
energy consumption equals the expectation of future deferrable load otalenergy
demand. These two algorithms were implemented using genetic algorithm as an
optimisation tool on a Simulink environment of Matlab. The control unit at the substation
monitors energy demand on each feeder at the substation in comparism to total energy
allocated to the substation. Commands were intelligently initiated at the substation and
implemented on the consumer side to clip the peak periods and fill the valley periods,
thus eradicating black-outs at the consumer premises. Communication between the
control unit at the substation and its embedded counterparts at the consumer premises
are implemented with the aid of transceiver incorporated into the system. Statistical
results reflected that peak clipping and valley filling occurred during optimisation of the
feeders, thus implies that feeder failure as a result of overloading was eradicated.
