| Summary: | Includes bibliographical references. === Modern economies are dependent on a reliable electricity supply for sustaining economic health and development, enabled by adequate energy planning and/or investment in capacity. Identifying drivers such as changes in income distribution that impact electricity demand is thus critical. This project made use of a system dynamics methodology with feedback loops to provide an insightful alternative to the conventional linear statistical empirical approaches such as multiple regression analysis and principal component analysis, generally used to explore the sensitivities of key driving forces which affect income distribution. The system dynamics simulation tool highlighted the direct causal influence of Gini coefficient on residential electricity consumption, by using equations as opposed to correlations. Results show that for a GDP growth rate of 2, by year 2035, a Gini coefficient of 0.5 is linked to a 3.14 increase in residential electricity demand while a Gini coefficient of 0.4 means a 4.73 increase in residential electricity demand. This dynamic is an important consideration for energy planners since government has (and continues to) introduce policies and mechanisms to ensure a more equal income distribution and hence a decrease in Gini coefficient from 0.67 to lower values.
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