Economic Dispatch of Electrical Power in South Africa: An Application to the Northern Cape Province

Statistics, Optimization and Information Computing

article

Thakhani Ravele¹  CastonSigauke²  LordwellJhamba³ 

[1] PhD in Statistics candidate;Department of Statistics, University of Venda;Department of Physics, University of Venda

关键词: Additive quantile regression;    Lasso;    Linear quantile regression;    Mixed integer linear programming;    Nonlinear quantile regression;    Unit commitment.;   

DOI  :  10.19139/soic-2310-5070-1057

来源: Istituto Superiore di Sanita

【 摘 要 】

Power utility companies rely on forecasting for the operation of electricity demand. This presents an applicationof linear quantile regression, non-linear quantile regression, and additive quantile regression models for forecasting extreme electricity demand at peak hours such as 18:00, 19:00, 20:00 and 21:00 using Northern Cape data for the period 01 January 2000 to 31 March 2014. The selection of variables was done using the least absolute shrinkage and selection operator. Additive quantile regression models were found to be the best fitting models for hours 18:00, and 19:00, whereas linear quantile regression models were found to be the best fitting models for hours 20:00, and 21:00. Out of sample forecasts for seven days (01 to 07 April 2014) were used to solve the unit commitment problem using mixed-integer programming. The unit commitment problem results showed that it is less costly to use all the generating units such as hydroelectric, wind power, concentrated solar power and solar photovoltaic. The main contribution of this study is in the development of models for forecasting hourly extreme peak electricity demand. These results could be useful to system operators in the energy sector who have to maintain the minimum cost by scheduling and dispatching electricity during peak hours when the grid is constrained due to peak load demand.

【 授权许可】

Unknown