The majority of power distribution networks were planned, designed and built as
a passive but reliable link between the bulk power transmission point and the in-
dividual customer. Enough latent capacity in cables and lines to accommodate
anticipated demand growth was allowed and so the system was left unmonitored.
Following the signicant development in business regulation, technology evolutions
and various government policies towards low carbon renewable generation, it has
become necessary to operate the distribution systems efficiently and in a controlled
manner. This obviously needs state estimation for network control functions. State
estimation is the core function of any energy management system in transmission
networks. However little emphasis have been given to the distribution system state
estimation, mainly due to the absence of adequate network measurements and also
lack of rigorous methodology and tools that could be applied on restricted measure-
ments.
The scarcity of measured information offers formidable challenge to the state
estimator to provide reasonably meaningful estimates of the system states. This
introduces bottlenecks in carrying out a range of substation and feeder automa-
tion tasks that rely on the quality of the state estimator and opens up many issues
like modelling of demand, identification of suitable estimator and placement of new
measurements etc. This thesis attempts to address these issues. Thus, the objec-
tives of this research are to model the demand as pseudo measurement, identify the
state estimation methodology to suite the distribution scenarios and find the effec-
tive locations for placing measurements for improving the quality of the estimated
quantities. The thesis discusses in detail the criterion for identifying suitable solvers
for the distribution system state estimation and stochastic optimisation methods to
model the demand. It also discusses a probabilistic technique for identifying effective locations for measurement placement. The robustness of the state estimation
algorithm against changes in network topology has been addressed in a statistical
framework. All the concepts have been demonstrated on 12-bus radial and 95-bus
UKGDS network models.