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Demand-Side Management of Electricity Transmission in French Rural Areas: Defining Macro-Scale Projects

Par : Contributeur(s) : Type de matériel : TexteTexteLangue : français Détails de publication : 2005. Sujet(s) : Ressources en ligne : Abrégé : The aim of this paper is to define precisely operational zones for the implementation of demand-side management (DSM) programmes for electricity transmission in rural areas. The approach implemented emphasises the geographical aspects of demand without neglecting fundamental aspects pertaining to customer end uses and the low-voltage grid. In France, the grid was extended to rural areas in 1970. However, the current increase in investment needs for the rural grid has prompted the regulatory authorities to promote DSM as a less costly alternative to grid reinforcement. Among the determinants of electricity demand and its increase, the geographical context seems preponderant. It encompasses the natural environment and local population trends resulting from changing lifestyles. To take this into account, a classification based on Gaussian mixture models and integrating spatial constraints of the neighbouring area can be used to identify areas of high electricity consumption.
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The aim of this paper is to define precisely operational zones for the implementation of demand-side management (DSM) programmes for electricity transmission in rural areas. The approach implemented emphasises the geographical aspects of demand without neglecting fundamental aspects pertaining to customer end uses and the low-voltage grid. In France, the grid was extended to rural areas in 1970. However, the current increase in investment needs for the rural grid has prompted the regulatory authorities to promote DSM as a less costly alternative to grid reinforcement. Among the determinants of electricity demand and its increase, the geographical context seems preponderant. It encompasses the natural environment and local population trends resulting from changing lifestyles. To take this into account, a classification based on Gaussian mixture models and integrating spatial constraints of the neighbouring area can be used to identify areas of high electricity consumption.

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