Abstract
In the current context of profound changes in the planning and operations of electrical systems, many Distribution System Operators (DSOs) are deploying Smart Meters at a large scale. The latter should participate in the effort of making the grid smarter through active management strategies such as storage or demand response. These considerations involve to model electrical quantities as locally as possible and on a sequential basis. This paper explores the possibility to model microscopic loads (individual loads) using Seasonal Auto-Regressive Moving Average (SARMA) time series based solely on Smart Meters data. A systematic definition of models for 18 customers has been applied using their consumption data. The main novelty is the qualitative analysis of complete SARMA models on different types of customers and an evaluation of their general performance in an LV network application. We find that residential loads are easily captured using a single SARMA model whereas other profiles of clients require segmentation due to strong additional seasonalities.
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Notes
- 1.
There exists more sophisticated techniques that can process non-Gaussian ARMA series, but they are significantly more complicated to implement.
- 2.
The value of 227 V is arbitrarily chosen in order to obtain significant indexes.
References
Hernandez, J.C., Ruiz-Rodriguez, F.J., Jurado, F.: Technical impact of photovoltaic-distributed generation on radial distribution systems: stochastic simulations for a feeder in Spain. Int. J. Electr. Pow. Energy Syst. 50(1), 25–32 (2013)
Klonari, V., Toubeau, J., De Grève, Z., Durieux, O., Lobry, J., Vallée, F.: Probabilistic simulation framework of the voltage profile in balanced and unbalanced low voltage networks, pp. 1–20
Vallée, F., Klonari, V., Lisiecki, T., Durieux, O., Moiny, F., Lobry, J.: Development of a probabilistic tool using Monte Carlo simulation and smart meters measurements for the long term analysis of low voltage distribution grids with photovoltaic generation. Int. J. Electr. Pow Energy Syst. 53, 468–477 (2013)
Ardakanian, O., Keshav, S., Rosenberg, C.: Markovian models for home electricity consumption. In: Proceedings of the 2nd ACM SIGCOMM Workshop on Green Networking ’11, p. 31 (2011)
Collin, A.J., Tsagarakis, G., Kiprakis, A.E., McLaughlin, S.: Development of low-voltage load models for the residential load sector. IEEE Trans. Pow. Syst. 29(5), 2180–2188 (2014)
Singh, R.P., Gao, P.X., Lizotte, D.J.: On hourly home peak load prediction. In: 2012 IEEE 3rd International Conference on Smart Grid Communications (SmartGridComm), pp. 163–166 (2012)
Sevlian, R., Rajagopal, R.: Short term electricity load forecasting on varying levels of aggregation. pp. 1–8 (2014)
Von Sachs, R., Van Bellegem, S.: Séries Chronologiques, notes de cours (Université Catholique de Louvain), p. 209 (2005)
Klöckl, B., Papaefthymiou, G.: Multivariate time series models for studies on stochastic generators in power systems. Electr. Pow. Syst. Res. 80(3), 265–276 (2010)
Acknowledgements
The authors would like to thank ORES, the operator in charge of managing the electricity and domestic gas distribution grids in 196 municipalities of Wallonia (Belgium), for its support in terms of financing and grid data supply both necessary for carrying out this research study.
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Hupez, M., Toubeau, JF., De Grève, Z., Vallée, F. (2017). SARMA Time Series for Microscopic Electrical Load Modeling. In: Rojas, I., Pomares, H., Valenzuela, O. (eds) Advances in Time Series Analysis and Forecasting. ITISE 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-55789-2_10
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DOI: https://doi.org/10.1007/978-3-319-55789-2_10
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