Probabilistic load models and Monte Carlo simulations used in distribution system planning

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Paper number
1672
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Tønne, Erling, NTE Nett AS, Norway
Sand, Kjell, NTNU, Norway
Foosnæs, Jan Andor, NTE Nett AS, Norway
Abstract
Today’s deterministic fit-and-forget methodology for distribution system planning is not suitable for planning of the future smart distribution grid. Alternative methodologies have been developed. The paper describes the new approach and compares it with todays practice. The ongoing and continuing increase in grid connection of distributed generation from renewable energy sources and change in consumption patterns will make the power flow more unpredictable and stochastic than before.  Generation from renewables is normally unregulated (e.g. solar and wind) and will vary a lot. Measurements show that the increased use of energy efficient but power-intensive equipment (like heat pumps, electric vehicles and induction cookers) together with time-varying tariffs, give faster dynamics in consumption patterns and a more stochastic behaviour of the power flow.   Reliable models for loads and generation variations and development are essential for the long-term development of the distribution and transmission grid. Poor models will probably result in a large difference between the estimated and real (measured) loads, and this again will result in wrong decisions and over- or underinvestment in the grid. The Distribution System Operators (DSOs) will get a lot more data from smart meters, new sensors, control systems etc. that can be used to make better load estimations and forecasts.  A new probabilistic method for load and generation modelling utilizing new data from smart meters have been developed and compared with today’s deterministic method. The new models have been used in probabilistic network load flow calculations by Monte Carlo simulations and shows promising results.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/516
http://dx.doi.org/10.34890/742
ISSN
2032-9644
ISBN
978-2-9602415-0-1