A STATISTICAL APPROACH IN DETERMINING THE ELECTRICAL SHORT TERM DEMAND IN A RAPID RAILWAY SYSTEM Grant Manuel, Prof Jan-Harm C Pretorius University of Johannesburg South Africa jhcpretorius@uj.ac.za ABSTRACT South Africa has commissioned a new rapid railway system between the cities of Pretoria and Johannesburg. Commuter rail has proven to be more ideal, particular when considering the reduction of carbon emersions and the relief of congestion on city highways. This new railway system is being supplied by an electrical propulsion system which source is obtained from a local electrical supplier. Supply agreements for particularly high energy users and in this case essential services has meant carefully calculating the current and short term demand. This data is used to ensure that the energy supplier has reserved the appropriate capacity for this essential service. Short term forecast relate to the operational and maintenance function of commuter travel. Predicting a possible crisis can be met with a contingency plan. This paper evaluates the current energy trends of the commuter rail system and how from a statistical point of view, the data from energy loggers may be used to determine a short term load forecast and maximum demand. A statistical model has proven successful, especially because many forecasted data was derived from statistical data. KEY WORDS maximum demand, short term load forecast, commuter rail, statistical methods, Gautrain 1. INTRODUCTION Global warming has become a focal point for many world leaders of first and third world countries alike. With commitments from many countries to reduce their carbon footprint, greener energy has become imperative. For many developing countries such as South Africa, the abundant supply of fossil fuels to produce electricity is more feasible than greener alternatives. Third world countries rely on greener solutions from other energy sectors to meet their carbon reduction targets. One such sector is the transportation sector. Commuter transport by means of rapid railway reduces carbon emersions and this mode of transportation limits the emersion of greenhouse gasses [7]. In South Africa, a new rapid railway system has just been commissioned between the cities of Johannesburg and Pretoria situated approximately 57 kilometres apart. Thus far carbon reduction has met with satisfactory results. However, new problematic areas have been presented. In this case, one of the problematic focal point is that of the energy demand. This railway line is supplied with 25kV derived from a main propulsion substation (MPS). The MPS is supplied with 88kV from the local electrical supplier. As in the case with many supply agreements of high energy users, the maximum capacity or demand is imperative in order for the supplier to reserve the capacity for the consumer. The importance of demand forecasting needs to be emphasised at all levels as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry [4]. The maximum demand is also essential when determining the capacity of the transformers to supply the catenary. Many designers tend to over design to accommodate a possible increase in demand. The result of which is often unutilised capacity of the supply at a substantial expense. Another aspect is that of the importance of a short term demand forecast. This serves as a function of maintenance and operational activities. If losses and other low demand contributing factors were ignored, a major contributing factor would be that of the number of commuters using the rail. To explain, consider the following. The more passengers that utilise the rail, the more weight and therefore more tractive effort needed to propel the train. Hence more consumption. It has been proven that the greatest contributing factor to the demand is a direct result of the volume of commuters. Many methods exist for calculating the maximum demand as well as short term forecast. However many of these methods are suitable only for low and medium energy users. Although many standards exist that may act as guidelines in the determination of electrical demand for low and high energy users, little to non govern the design or standards in determining the demand in a high energy system, specifically a rapid railway system. Generally in low and medium demand supply systems, the demand is determined using conventional methods such as Fuzzy logic and Neural networks. Proceedings of the IASTED International Conference June 22 - 24, 2011 Crete, Greece Power and Energy Systems (EuroPES 2011) DOI: 10.2316/P.2011.714-077 428