Electricity demand analysis - unconstrained vs constrained scenarios. (1st January 2003)

• Record Type: Journal Article
• Authors: Balachandra, P.; Chandru, V.; Subrahmanya, M.H. Bala
• Is Part Of: International journal of global energy issues. Volume 20:Number 1(2003)
• Page Start: 1
• Page End: 35
• Publication Date: 2003-01-01
• Subjects: electricity planning -- resource constraints -- electricity demand analysis -- load curves -- modelling electricity demand -- annual load duration curve; Power resources -- Periodicals; 333.79
• Languages: English
• ISSNs: 0954-7118

Abstract

In India, the electricity systems are chronically constrained by shortage of both capital and energy resources. These result in rationing and interruptions of supply with a severely disrupted electricity usage pattern. From this background, we try to analyse the demand patterns with and without resource constraints. Accordingly, it is necessary to model appropriately the dynamic nature of electricity demand, which cannot be captured by methods like annual load duration curves. Therefore, we use the concept - Representative Load Curves (RLCs) - to model the temporal and structural variations in demand. As a case study, the electricity system of the state of Karnataka in India is used. Four years demand data, two unconstrained and two constrained, are used and RLCs are developed using multiple discriminant analysis. It is found that these RLCs adequately model the variations in demand and bring out distinctions between unconstrained and constrained demand patterns. The demand analysis attempted here helped to study the differences in demand patterns with and without constraints, and the success of rationing measures in reducing demand levels as well as greatly disrupting the electricity usage patterns. Multifactor ANOVA analyses are performed to find out the statistical significance of the ability of logically obtained factors in explaining overall variations in demand. The results showed that the factors that are taken into consideration accounted for maximum variations in demand at very high significance levels.