Impacts of Demand Data Time Resolution on Estimates of Distribution System Energy Losses


Copper losses in low voltage distribution circuits are a important proportion of total energy losses and contribute to higher client costs and carbon emissions. These losses can be evaluated using network models with client demand data. This paper considers the underneath-estimation of copper losses when the spiky characteristics of real client demands are smoothed by arithmetic mean averaging. This is often investigated through simulation and by analysis of measured data. The mean losses in cables and equipment supplying one dwelling estimated from half-hourly data were found to possess vital errors of fortyp.c, compared to calculations using high resolution knowledge. Similar errors were found in estimates of peak thermal loading over a half-hour amount, with important variation between results for each customer. The errors scale back because the demand is aggregated, with mean losses for a cluster of twenty-two dwellings under-estimated by 7% using half-hourly data. This paper investigates the connection between the demand knowledge time resolution and errors within the estimated losses. Recommendations are then provided for the time resolution to be used in future measurements and simulation studies. A linear extrapolation technique is also presented whereby errors thanks to the use of averaged demand knowledge will be reduced.

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