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Essential to the toolset of modern power engineers are the business environment knowledge, an appreciation for customer service/satisfaction as well as the ability to analyse, negotiate and articulate clearly with technical and non-technical personnel. Whilst most engineering oriented universities recognise the role of practical experience, challenges exist with the students' ability to translate the theoretical into practical knowledge. This paper investigates the application of the game-based learning (GBL) instructional method, as an alternative method of enhancing the practical application of course delivery, through the integration of business thinking principles into the final year undergraduate power engineering curriculum. IBM's Innov8: CityOne Game was chosen as the game of preference as it embedded core course content material via a serious game. The Game was administered to two consecutive cohorts (2012 and 2013) of the level three undergraduate course power system analysis. A qualitative and quantitative review of both cohorts' feedback and performance are individually analysed and compared.

Significant efforts has been dedicated to a statistical bivariate or univariate probability density function (pdf) as the model for compact representation of the enormous volume of raw wind data of a regime. This is essential for wind energy conversion system (WECS) studies or for power system planning and reliability studies. However, the literature review of this paper found that acceptance of only the industry-standard two-parameter Weibull model (WE2) is questionable. Complex wind speed phenomena demand a model that is good for both interpolation and extrapolation. The main objective of this study is to revitalise interest in wind speed frequency modelling by defining a catalogue of flexible theoretical/empirical models belonging to the Johnson, Gamma and Extreme Value families for new applicability. Definition is focussed on the three or more parameter models that are physically adaptable to wind speed. The robust Maximum Likelihood Method (MLM) and Probability Weighted Moments (PW M) method accomplish parametric estimation. The lesser efficient, but popular, Method of Moments (MOM) is also given. This paper is a glossary of such models. The substantial task of investigation and justification of particular models for wind speed and wind power analysis is not attempted here.