Abstract
The emergence of Smart Grids is posing a wide range of challenges for electric utility companies and network operators: Integration of non-dispatchable power from renewable energy sources (e.g., photovoltaics, hydro and wind), fundamental changes in the way energy is consumed (e.g., due to dynamic pricing, demand response and novel electric appliances), and more active operations of the networks to increase efficiency and reliability. A key in managing these challenges is the ability to forecast network loads at low levels of locality, e.g., counties, cities, or neighbourhoods. Accurate load forecasts improve the efficiency of supply as they help utilities to reduce operating reserves, act more efficiently in the electricity markets, and provide more effective demand-response measures. In order to prepare for the Smart Grid era, there is a need for a scalable simulation environment which allows utilities to develop and validate their forecasting methodology under various what-if-scenarios. This paper presents a massive-scale simulation platform which emulates electrical load in an entire electrical network, from Smart Meters at individual households, over low- to medium-voltage network assets, up to the national level. The platform supports the simulation of changes in the customer portfolio and the consumers’ behavior, installment of new distributed generation capacity at any network level, and dynamic reconfigurations of the network. The paper explains the underlying statistical modeling approach based on Generalized Additive Models, outlines the system architecture, and presents a number of realistic use cases that were generated using this platform.
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Notes
- 1.
More details are available at http://www.erdfdistribution.fr/linky/
- 2.
Assuming that each data point requires 8Â bytes memory.
- 3.
It is important to note that the GAMs learned on aggregated load data do not really represent load at the individual Meter level, but more an “average consumer”. As will be shown in Sect. 6, GAMs fit well for aggregates of 70 households or more. The purpose for using GAMs, nevertheless, at the Meter level, is to represent shifts in the customer portfolio and changes in the consumers’ behaviors, as will be explained at the end of this subsection.
- 4.
The installment of new wind power capacity can be represented by network nodes which, at specified time points, change their simulation model from a “void” GAM (producing zero values) to a GAM model simulating wind farm output. Compare with the remark at the end of Sect. 4.2.
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Pompey, P., Bondu, A., Goude, Y., Sinn, M. (2015). Massive-Scale Simulation of Electrical Load in Smart Grids Using Generalized Additive Models. In: Antoniadis, A., Poggi, JM., Brossat, X. (eds) Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics(), vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-18732-7_11
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