Abstract
A two-stage stochastic programming model is used to solve the electricity generation planning problem in South Africa for the period 2013 to 2050, in an attempt to minimise expected cost. Costs considered are capital and running costs. Unknown future electricity demand is the source of uncertainty represented by four scenarios with equal probabilities. The results show that the main contributors for new capacity are coal, wind, hydro and gas/diesel. The minimum costs obtained by solving the two-stage stochastic programming models range from R2 201 billion to R3 094 billion.
Highlights
According to Sen & Zhou [34] stochastic programming (SP) deals with a class of optimisation models and algorithms in which some of the data may be subjected to significant uncertainty
In this study, Advanced Interactive Multidimensional Modelling System (AIMMS) software was used in the modelling process
The determined costs are for the new capacity only as shown by the first term of the objective function in equation (5)
Summary
According to Sen & Zhou [34] stochastic programming (SP) deals with a class of optimisation models and algorithms in which some of the data may be subjected to significant uncertainty. The authors suggest that SP models are appropriate when data evolves over time and decisions need to be made prior to observing the entire data stream. This is the case in this work since electricity generation expansion plans are made in advance and amidst uncertain future electricity demand. A two-stage stochastic programming (TSSP) model for electricity generation expansion planning in South Africa, is proposed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
