Measurements and Corrections

The subsequent practices would enhance the reliability and correctness of energy accounting:

1.    Well described procedures for reading of the energy meters (on 11 kV feeders) not having data logging facilities, and consumer meters.

2.    Simple procedures to correct for non-simultaneous reading of consumer meters and billing cycles;

3.  Accounting for un-metered consumption, till such time as 100 percent consumer metering is in place. For these reasons, data derived from sample metering at selected distribution transformers and sample surveys of consumption of several categories of consumers on a system huge basis would be needed.

Scientific sampling methods should be adopted for getting optimum results. The size of the sample and the actual location of the meters to be installed should be based on a detailed analysis of the consumers' profile, identification of regional factors and other parameters having an impact on consumption.

For instance, in the case of agricultural consumers, the parameters would be cropping pattern, irrigation practices, ground water profile, agro-climatic factors, etc.  For domestic and commercial consumers, the parameters could be based on income levels, sanctioned/connected load, etc. In the case of street lighting, the season-wise sample survey, hours of supply and number of working light points and etc., should be considered. With the analysis of data from such surveys, the energy consumption of un-metered consumers could be worked out on a reasonable basis. As per, a reasonably reliable figure of energy losses for each feeder could be derived.

Even after achieving 100 percent metering of consumers, it would be essential to have metering of some selected distribution transformers, serving various categories of load to provide a reference point for checking the energy balance. The data from consumer level metering and sample survey data should be utilized to firm up consumption of every class of consumer and derive validated data. The sample metering data would enable the utility to establish norms of consumption. The utility would also be able to investigate any deviations from general consumption and their causes and purpose. The whole check would also be probable through adopting the population of each class of consumer and applying the sample data for estimation of consumption to validate T&D losses.

At last, we would like to present the major obstacles to energy accounting through utilities and provide some suggestions about how to overcome them.