Abstract:
Daily peak electricity data from ESKOM, South African power utility company for the period, January
1997 to December 2013 consisting of 6209 observations were used in this dissertation. Since 1994, the
increased electricity demand has led to sustainability issues in South Africa. In addition, the electricity
demand continues to rise everyday due to a variety of driving factors. Considering this, if the electricity
generating capacity in South Africa does not show potential signs of meeting the country’s demands in
the subsequent years, this may have a significant impact on the national grid causing it to operate in a
risky and vulnerable state, leading to disturbances, such as load shedding as experienced during the past
few years. In particular, it is of greater interest to have sufficient information about the extreme value
of the stochastic load process in time for proper planning, designing the generation and distribution
system, and the storage devices as these would ensure efficiency in the electrical energy in order to
maintain discipline in the grid systems.
More importantly, electricity is an important commodity used mainly as a source of energy in industrial,
residential and commercial sectors. Effective monitoring of electricity demand is of great importance
because demand that exceeds maximum power generated will lead to power outage and load shedding.
It is in the light of this that the study seeks to assess the frequency of occurrence of extreme peak
electricity demand in order to come up with a full electricity demand distribution capable of managing
uncertainties in the grid system.
In order to achieve stationarity in the daily peak electricity demand (DPED), we apply a penalized
regression cubic smoothing spline to ensure the data is non-linearly detrended. The R package “evmix”
is used to estimate the thresholds using the bounded corrected kernel density plot. The non-linear
detrended datasets were divided into summer, spring, winter and autumn according to the calender
dates in the Southern Hemisphere for frequency analysis. The data is declustered using Ferro and
Segers automatic declustering method. The cluster maxima is extracted using the R package “evd”.
We fit Poisson GPD and stationary point process to the cluster maxima and the intensity function of
the point process which measures the frequency of occurrence of the daily peak electricity demand per
year is calculated for each dataset.
The formal goodness-of-fit test based on Cramer-Von Mises statistics and Anderson-Darling statistics
supported the null hypothesis that each dataset follow Poisson GPD (σ, ξ) at 5 percent level of
significance. The modelling framework, which is easily extensible to other peak load parameters, is
based on the assumption that peak power follows a Poisson process. The parameters of the developed
i
models were estimated using the Maximum Likelihood. The usual asymptotic properties underlying the
Poisson GPD were satisfied by the model.
