AUTHORS: Ales Svigelj

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ABSTRACT: Conventional distribution systems, growing in complexity due to increasing penetration of the distributed generation such as wind and solar plants, lack observability or monitoring capabilities in order to allow higher and higher levels of penetration of distributed energy resources such as electric vehicles and batteries. The key is the distribution system state estimation, which can provide the observability. Thus, in this paper we are focusing on pseudo measurements calculation, based on real prosumers characterisation, which are among the topology parameters and phasor measurements one of the key inputs into the distribution system state estimation.

KEYWORDS: Smart grid, Prosumer characterization, Pseudo measurements, State estimation

REFERENCES:

[1] D. Poli, M. Giuntoli et al., “The PRIME Project: the assessment of the impact of electric vehicles on the power system and on the environment”, WSEAS Advances in Power and Energy Systems, Prague, 2012.

[2] P. Chrobák, J. Skovajsa, M. Zálešák, “Production of Electricity Using Photovoltaic Panels and Effects of Cloudiness”, WSEAS Transactions on Power Systems, Vol. 12, 2017, pp. 346-35.

[3] A. Meier, D. Culler, A. McEachern, R. Arghandeh, 'Micro-Synchrophasors for Distribution Systems', IEEE PES Innovative Smart Grid Technologies Conference, (ISGT 2014), 2014.

[4] D. Santis, G. Abbatantuono, S.Bruno, M. ScalaLow, “Voltage Grid Control through Electrical Vehicles Charging Stations”, WSEAS Transactions on Power Systems, Vol. 11, 2016, pp. 283-288.

[5] Jimmy Nielsen, A. Svigelj, et al. “Secure realtime monitoring and management of smart distribution grid using shared cellular networks”, IEEE wireless communications, 2017, vol. 24, no. 2, pp. 10-17.

[6] U. Kuhar, J. Jurše, K. Alič, G. Kandus, A. Svigelj, “A unified three-phase branch model for a distribution-system state estimation”, In proc.of IEEE PES innovative smart grid technologies, Europe, (ISGT-Europe) Ljubljana, Slovenia, 2016.

[7] D. Della Giustina, M. Pau, P. A. Pegoraro, F. Ponci, S. Sulis, “Electrical distribution system state estimation: Measurement issues and challenges”, IEEE Instrumentation and Measurement Magazine, vol.1094, no. 6969/14, 2014.

[8] EC FP7 SUNSEED (Sustainable and robust networking for smart electricity distribution), https://sunseed-fp7.eu/ , accessed 2018.

[9] S. Ramos, J. Duarte, J. Soares, Z. Vale, F. Duarte, “Typical Load Profiles in the Smart Grid Context – A Clustering Methods Comparison”, IEEE Power and Energy Society General Meeting 2012, San Diego, California, USA, July 2012.

[10] R. Singh, B. C. Pal, and R. A. Jabr, “Choice of estimator for distribution system state estimation,” Generation, Transmission & Distribution, IET, vol. 3, no. 7, 2009, pp. 666– 678.

[11] J. MacQueen, 'Some Methods for classification and Analysis of Multivariate Observations', 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281-297.

[12] T. C. Xygkis, G. D. Karlis, I. K. Siderakis and G. N. Korres, 'Use of near real-time and delayed smart meter data for distribution system load and state estimation,' MedPower 2014, Athens, 2014, pp. 1-6.

[1] D. Poli, M. Giuntoli et al., “The PRIME Project: the assessment of the impact of electric vehicles on the power system and on the environment”, WSEAS Advances in Power and Energy Systems, Prague, 2012.

[2] P. Chrobák, J. Skovajsa, M. Zálešák, “Production of Electricity Using Photovoltaic Panels and Effects of Cloudiness”, WSEAS Transactions on Power Systems, Vol. 12, 2017, pp. 346-35.

[3] A. Meier, D. Culler, A. McEachern, R. Arghandeh, 'Micro-Synchrophasors for Distribution Systems', IEEE PES Innovative Smart Grid Technologies Conference, (ISGT 2014), 2014.

[4] D. Santis, G. Abbatantuono, S.Bruno, M. ScalaLow, “Voltage Grid Control through Electrical Vehicles Charging Stations”, WSEAS Transactions on Power Systems, Vol. 11, 2016, pp. 283-288.

[5] Jimmy Nielsen, A. Svigelj, et al. “Secure realtime monitoring and management of smart distribution grid using shared cellular networks”, IEEE wireless communications, 2017, vol. 24, no. 2, pp. 10-17.

[6] U. Kuhar, J. Jurše, K. Alič, G. Kandus, A. Svigelj, “A unified three-phase branch model for a distribution-system state estimation”, In proc.of IEEE PES innovative smart grid technologies, Europe, (ISGT-Europe) Ljubljana, Slovenia, 2016.

[7] D. Della Giustina, M. Pau, P. A. Pegoraro, F. Ponci, S. Sulis, “Electrical distribution system state estimation: Measurement issues and challenges”, IEEE Instrumentation and Measurement Magazine, vol.1094, no. 6969/14, 2014.

[8] EC FP7 SUNSEED (Sustainable and robust networking for smart electricity distribution), https://sunseed-fp7.eu/ , accessed 2018.

[9] S. Ramos, J. Duarte, J. Soares, Z. Vale, F. Duarte, “Typical Load Profiles in the Smart Grid Context – A Clustering Methods Comparison”, IEEE Power and Energy Society General Meeting 2012, San Diego, California, USA, July 2012.

[10] R. Singh, B. C. Pal, and R. A. Jabr, “Choice of estimator for distribution system state estimation,” Generation, Transmission & Distribution, IET, vol. 3, no. 7, 2009, pp. 666– 678.

[11] J. MacQueen, 'Some Methods for classification and Analysis of Multivariate Observations”, 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281-297.

[12] T. C. Xygkis, G. D. Karlis, I. K. Siderakis and G. N. Korres, 'Use of near real-time and delayed smart meter data for distribution system load and state estimation,' MedPower 2014, Athens, 2014, pp. 1-6.