Development of low voltage network templates - Part II peak load estimation by clusterwise regression:peak load estimation by clusterwise regression
Li, R., Gu, C., Li, F., Shaddick, G. and Dale, M., 2015. Development of low voltage network templates - Part II peak load estimation by clusterwise regression:peak load estimation by clusterwise regression. IEEE Transactions on Power Systems, 30 (6), 6981996.
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This paper proposes a novel contribution factor (CF) approach to predict diversified daily peak load of low voltage (LV) substations. The CF for each LV template developed in part I of the paper is determined by a novel method - clusterwise weighted constrained regression (CWCR). It takes into account the contribution from different customer classes to substation peaks, respecting the natural difference in time and magnitude between LV substation peaks and the variance within the templates. In CWCR, intercept and coefficients are constrained to ensure that the resultant coefficients do not lead to reverse load flow and can respect zero-load substations. Cross validation is developed to validate the stability of the proposed method and prevent over fitting. The proposed method shows significant improvement in the accuracy of peak estimation over the current status quo across 800 substations of different mixes of domestic, industrial and commercial (I&C) customers. The work in the two parts of the paper is particularly useful for understanding the capabilities of LV networks to accommodate the increasing penetration of low carbon technologies without large-scale monitoring.
|Creators||Li, R., Gu, C., Li, F., Shaddick, G. and Dale, M.|
|Uncontrolled Keywords||data mining,distribution networks,load modeling,low voltage network,network template,peak estimation|
|Departments||Faculty of Engineering & Design > Electronic & Electrical Engineering|
Faculty of Science > Mathematical Sciences
|Research Centres||Centre for Sustainable Power Distribution|
EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
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