Abstract
Electricity demand exhibits a large degree of randomness in South Africa, particularly in summer. Its description requires a detailed analysis using statistical methodologies, in particular stochastic processes. The paper presents a Markov chain analysis of peak electricity demand. The data used is from South Africa's power utility company Eskom, for the period 2000 to 2011. This modelling approach is important to decision makers in the electricity sector particularly in scheduling maintenance and refurbishments of power-plants. The randomness effect is accountable to meteorological factors and major electricity appliance usage. Aggregated data on daily electricity peak demand is used to develop the transition probability matrices, steady-state probabilities, mean return- and the first passage times. Such analysis are important to Eskom and other energy companies in planning load-shifting, load analysis and scheduling of electricity particularly during peak period in summer.
Highlights
Electricity demand exhibits a large degree of randomness, in South Africa
South Africa is faced with the challenge to appropriately schedule power-station plant maintenance and insufficient potential to meet the electricity demand for consumers; which is mainly due to the meteorological influences, use of major electricity appliances and the demand varies with consumers’ behavior
The two-state problem is extended to the three-state problem, where the daily electricity demand changes in different hours are split into the following states: small increases, extreme increases of the hourly peak demands and last, negative hourly changes are classified as decreases
Summary
Electricity demand exhibits a large degree of randomness, in South Africa. South Africa is faced with the challenge to appropriately schedule power-station plant maintenance and insufficient potential to meet the electricity demand for consumers; which is mainly due to the meteorological influences, use of major electricity appliances and the demand varies with consumers’ behavior. Electricity demand in South Africa vary from sector to sector, and the major driver is mainly temperature as discussed by [4] Under these circumstances, South Africa’s power utility company is faced with the challenge to keep up with the rising electricity demand. The use of electricity major appliances (refrigerators, ventilators, air-conditioners, e.t.c) account for a large proportion of overall daily demand in summer This becomes problematic for Eskom to perform maintenance and refurbishments on the power-station plants. The paper focuses on the use of Markov chain analysis to model summer daily peak demand to help schedule maintenance of power plants. The last section concludes and provides short overview on the findings
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