Peak electricity demand is an energy policy concern for all countries throughout
the world, causing blackouts and increasing electricity tariffs for consumers.
This calls for load curtailment strategies to either redistribute or reduce electricity
demand during peak periods. This thesis attempts to address this problem
by providing relevant information through a frequentist and Bayesian
modelling framework for daily peak electricity demand using South African
data. The thesis is divided into two parts. The first part deals with modelling
of short term daily peak electricity demand. This is done through the investigation
of important drivers of electricity demand using (i) piecewise linear
regression models, (ii) a multivariate adaptive regression splines (MARS) modelling
approach, (iii) a regression with seasonal autoregressive integrated moving
average (Reg-SARIMA) model (iv) a Reg-SARIMA model with generalized
autoregressive conditional heteroskedastic errors (Reg-SARIMA-GARCH). The
second part of the thesis explores the use of extreme value theory in modelling
winter peaks, extreme daily positive changes in hourly peak electricity demand
and same day of the week increases in peak electricity demand. This is done
through fitting the generalized Pareto, generalized single Pareto and the generalized
extreme value distributions.
One of the major contributions of this thesis is quantification of the amount of
electricity which should be shifted to off peak hours. This is achieved through
accurate assessment of the level and frequency of future extreme load forecasts.
This modelling approach provides a policy framework for load curtailment and determination of the number of critical peak days for power utility companies.
This has not been done for electricity demand in the context of South Africa to
the best of our knowledge. The thesis further extends the autoregressive moving
average-exponential generalized autoregressive conditional heteroskedasticity
model to an autoregressive moving average exponential generalized autoregressive
conditional heteroskedasticity-generalized single Pareto distribution.
The benefit of this hybrid model is in risk modelling of under and over
demand predictions of peak electricity demand.
Some of the key findings of this thesis are (i) peak electricity demand is influenced
by the tails of probability distributions as well as by means or averages,
(ii) electricity demand in South Africa rises significantly for average temperature
values below 180C and rises slightly for average temperature values above
220C and (iii) modelling under and over demand electricity forecasts provides a
basis for risk assessment and quantification of such risk associated with forecasting
uncertainty including demand variability.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ufs/oai:etd.uovs.ac.za:etd-08192014-134154 |
| Date | 19 August 2014 |
| Creators | Sigauke, Caston |
| Contributors | Dr D Chikobvu |
| Publisher | University of the Free State |
| Source Sets | South African National ETD Portal |
| Language | en-uk |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.uovs.ac.za//theses/available/etd-08192014-134154/restricted/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University Free State or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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