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Long Term Forecast of Local Electrical Demand and Evaluation of Future Impacts on the Electricity Distribution Network

Abstract : Enedis (France's main electricity DSO) and Mines Paris Tech are working on a new method called MOSAIC that aims at assessing the impact of long term local development projects on the electrical grid. The MOSAIC method uses a bottom-up simulation tool able to determine the current and future consumption and production load curve of an area. The consumption and production simulators are based on a multi-level data model that makes it possible to run a simulation even if some data are missing. The simulation parameters are calibrated by comparing the simulated load curves with observations on MV feeders (for the consumption simulator) and on 100 renewable energy producers (PV and WP). Once the simulators are calibrated for the current situation, the load curve of each development scenario of the area is estimated (the evolution scenarios are proposed by the local government, based on infeed growing, building projects …). Enedis and Mines Paris Tech are now working on linking the load curve simulators with more traditional electrical study tools. From the simulated load curve, we estimate the most likely maximal power of the grid infrastructures and use these values in the load flow tool called ERABLE to evaluate the impact of the scenarios on the grid. Initial tests were successful and this method will be further developed in 2017 on ten experimentation projects in France. Each project will help to develop new functionalities such as integrating Demand Response or electrical vehicle infrastructures.
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Submitted on : Monday, April 24, 2017 - 4:48:01 PM
Last modification on : Wednesday, November 17, 2021 - 12:32:23 PM
Long-term archiving on: : Tuesday, July 25, 2017 - 5:07:25 PM


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  • HAL Id : hal-01513138, version 1


Nicolas Kong, Maxence Bocquel, Thibaut Barbier, Robin Girard, Elena Magliaro, et al.. Long Term Forecast of Local Electrical Demand and Evaluation of Future Impacts on the Electricity Distribution Network. CIRED 2017, Jun 2017, Glascow, United Kingdom. pp.0743. ⟨hal-01513138⟩



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