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Summary Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.

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This paper analyses wind speed characteristics and wind power potential of Port Elizabeth using statistical Weibull parameters. A measured 5–minute time series average wind speed over a period of 5 years (2005 - 2009) was obtained from the South African Weather Service (SAWS). The results show that the shape parameter (k) ranges from 1.319 in April 2006 to 2.107 in November 2009, while the scale parameter (c) varies from 3.983m/s in May 2008 to 7.390 in November 2009.The average wind power density is highest during Spring (September–October), 256.505W/m2 and lowest during Autumn (April-May), 152.381W/m2. This paper is relevant to a decision-making process on significant investment in a wind power project.