Distributed Online Load Sensitivity Identification by Smart Transformer and Industrial Metering

Load power sensitivity to voltage changes continuously in the distribution grid due to the increased variability of the load demand (e.g., electric vehicles charging) and generation production (e.g., photovoltaic). Classical sensitivity identification methods do not respect the fast dynamics of such changes: they require long data history and/or high computational power to update the load sensitivity. The proposed online load sensitivity identification (OLLI) approach is able to identify the load sensitivity in real time (e.g., every minute). This paper demonstrates that the OLLI can be achieved not only with the advanced smart transformer metering system but also with commercial industrial metering products. It is shown that OLLI is able to identify correctly the load sensitivity also in the presence of noise or fast stochastic variation of power consumption. The industrial metering-based OLLI application has been proven by means of a power-hardware-in-loop evaluation applied on an experimental microgrid.

[1]  Ian A. Hiskens,et al.  Achieving Controllability of Electric Loads , 2011, Proceedings of the IEEE.

[2]  Steve Cox,et al.  Unlocking New Sources of Flexibility: CLASS: The World's Largest Voltage-Led Load-Management Project , 2017, IEEE Power and Energy Magazine.

[3]  Brendan Fox,et al.  On-line load relief control , 1994 .

[4]  N. D. Hatziargyriou,et al.  Frequency Control in Autonomous Power Systems With High Wind Power Penetration , 2012, IEEE Transactions on Sustainable Energy.

[5]  M. La Scala,et al.  Modelling of induction motor loads in power‐system voltage stability studies , 2007 .

[6]  Luis F. Ochoa,et al.  Voltage-Led Load Management in Whole Distribution Networks , 2018, IEEE Transactions on Power Systems.

[7]  Marco Liserre,et al.  The Smart Transformer: Impact on the Electric Grid and Technology Challenges , 2016, IEEE Industrial Electronics Magazine.

[8]  Francisco de Leon,et al.  Field-Validated Load Model for the Analysis of CVR in Distribution Secondary Networks: Energy Conservation , 2013, IEEE Transactions on Power Delivery.

[9]  Massimo La Scala,et al.  First activities and power-hardware-in-the-loop tests at the public research laboratory LabZERO , 2018, 2018 AEIT International Annual Conference.

[10]  Ugo Stecchi,et al.  Monitoring and Control of a Smart Distribution Network in Extended Real-Time DMS Framework , 2011 .

[11]  Siamak Arzanpour,et al.  Quasi real-time ZIP load modeling for Conservation Voltage Reduction of smart distribution networks using disaggregated AMI data , 2015 .

[12]  Paolo Pinceti.  Emergency load-shedding algorithm for large industrial plants , 2002 .

[13]  Xavier Masip-Bruin,et al.  A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration , 2018, ACM Comput. Surv..

[14]  Joseph H. Eto,et al.  Design and Operation of Smart Loads to Prevent Stalling in a Microgrid , 2016, IEEE Transactions on Industry Applications.

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

[16]  Marco Liserre,et al.  On-Line Load Sensitivity Identification in LV Distribution Grids , 2017, IEEE Transactions on Power Systems.

[17]  R. C. Dugan,et al.  Distribution System Analysis and the Future Smart Grid , 2011, IEEE Transactions on Industry Applications.

[18]  Ioan Ungurean,et al.  Monitoring and control system for smart buildings based on OPC UA specifications , 2016, 2016 International Conference on Development and Application Systems (DAS).

[19]  M. La Scala,et al.  Voltage stability analysis of electric power systems with frequency dependent loads , 1993 .

[20]  Ioan Silea,et al.  Achieving interoperability using low-cost middleware OPC UA wrapping structure. Case study in the water industry , 2017, 2017 IEEE 15th International Conference on Industrial Informatics (INDIN).

[21]  D.G. Infield,et al.  Stabilization of Grid Frequency Through Dynamic Demand Control , 2007, IEEE Transactions on Power Systems.

[22]  Marco Liserre,et al.  The Smart Transformer: A solid-state transformer tailored to provide ancillary services to the distribution grid , 2017, IEEE Power Electronics Magazine.

[23]  Jianhui Wang,et al.  Robust Time-Varying Load Modeling for Conservation Voltage Reduction Assessment , 2018, IEEE Transactions on Smart Grid.

[24]  Brian B. Johnson,et al.  Achieving a 100% Renewable Grid: Operating Electric Power Systems with Extremely High Levels of Variable Renewable Energy , 2017, IEEE Power and Energy Magazine.

[25]  Michele Trovato,et al.  Intelligent load shedding schemes for industrial customers with cogeneration facilities , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[26]  Marco Liserre,et al.  Real-Time Primary Frequency Regulation Using Load Power Control by Smart Transformers , 2019, IEEE Transactions on Smart Grid.

[27]  Rolando Burgos,et al.  Review of Solid-State Transformer Technologies and Their Application in Power Distribution Systems , 2013, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[28]  Francisco de Leon,et al.  Experimental Determination of the ZIP Coefficients for Modern Residential, Commercial, and Industrial Loads , 2014, IEEE Transactions on Power Delivery.

[29]  Marco Liserre,et al.  Load control using sensitivity identification by means of smart transformer , 2017 .