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

dc.contributor.affiliationNTE Nett AS
dc.contributor.affiliationNTNU
dc.contributor.affiliationNTE Nett AS
dc.contributor.authorTønne, Erling
dc.contributor.authorSand, Kjell
dc.contributor.authorFoosnæs, Jan Andor
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.countryNorway
dc.contributor.detailedauthorTønne, Erling, NTE Nett AS, Norway
dc.contributor.detailedauthorSand, Kjell, NTNU, Norway
dc.contributor.detailedauthorFoosnæs, Jan Andor, NTE Nett AS, Norway
dc.date.accessioned2019-07-24T12:42:56Z
dc.date.available2019-07-24T12:42:56Z
dc.date.conferencedate3-6 June 2019
dc.date.issued2019-06-03
dc.description.abstractToday’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.
dc.description.conferencelocationMadrid, Spain
dc.description.conferencenameCIRED 2019
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionPlanning of power distribution systems
dc.description.sessionidSession 5
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/516
dc.identifier.urihttp://dx.doi.org/10.34890/742
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleProbabilistic load models and Monte Carlo simulations used in distribution system planning
dc.title.number1672
dc.typeConference Proceedings
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
CIRED 2019 - 1672.pdf
Size:
952.17 KB
Format:
Adobe Portable Document Format