Clustering of Transmission System Loads Based on Monthly Load Class Energy Consumptions

This paper presents an approach for clustering aggregated loads based on load class energy consumption time series, and choosing representative loads for each group. The described approach was applied in a transmission system load modelling study. The goal of the study was to choose representative loads for measurement-based modelling. The work was motivated by the limited number of available measurement devices and available personnel for data processing. The monthly energy consumption of each load class was known for each aggregated bus load. After measurement data pre-processing the larger loads were clustered using K-means algorithm, and smaller assigned to clusters. Representative loads were selected from each cluster.

[1]  J. Kilter,et al.  Ramping Behaviour Analysis of Wind Farms , 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[2]  Andrej Kosir,et al.  Statistical Load Time Series Analysis for the Demand Side Management , 2018, 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[3]  Jovica V. Milanovic,et al.  International Industry Practice on Power System Load Modeling , 2013, IEEE Transactions on Power Systems.

[4]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[5]  Jovica V. Milanovic,et al.  Identification of static load characteristics based on measurements in medium-voltage distribution network , 2008 .

[6]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[7]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[8]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[9]  F. Scarlatache,et al.  Load estimation for distribution systems using clustering techniques , 2012, 2012 13th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM).

[10]  Ivo Palu,et al.  Wind power variation identification using ramping behavior analysis , 2017 .

[11]  Peter J. Rousseeuw,et al.  Finding Groups in Data: An Introduction to Cluster Analysis , 1990 .

[12]  Franklin L. Quilumba,et al.  Customers' demand clustering analysis — A case study using smart meter data , 2016, 2016 IEEE PES Transmission & Distribution Conference and Exposition-Latin America (PES T&D-LA).

[13]  Jovica V. Milanovic,et al.  A step-by-step data processing guideline for load model development based on field measurements , 2015, 2015 IEEE Eindhoven PowerTech.

[14]  Jako Kilter,et al.  Identification of Intra-Day Variations of Static Load Characteristics Based on Measurements in High-Voltage Transmission Network , 2018, 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[15]  Zvonko Bregar,et al.  Improved model for the spatial load forecasting of the Slovenian distribution network , 2017 .

[16]  Koji Yamashita,et al.  Modelling and aggregation of loads in flexible power networks , 2014 .

[17]  W. Marsden.  I and J , 2012 .

[19]  Wilsun Xu,et al.  Online Tracking of Voltage-Dependent Load Parameters Using ULTC Created Disturbances , 2013, IEEE Transactions on Power Systems.

[20]  Jako Kilter,et al.  Impact of Distributed Generation on Estimation of Exponential Load Models , 2019, 2019 IEEE Power & Energy Society General Meeting (PESGM).

[21]  Z. A. Styczynski,et al.  Parameter estimation of dynamic load model using field measurement data performed by OLTC operation , 2012, 2012 IEEE Power and Energy Society General Meeting.