This article outlines methods to facilitate the assessment of the impact of electric vehicle charging on distribution networks at planning stage and applies them to a case study. As network planning is becoming a more complex task, an approach to automated network planning that yields the optimal reinforcement strategy is outlined. Different reinforcement measures are weighted against each other in terms of technical feasibility and costs by applying a genetic algorithm. Traditional reinforcements as well as novel solutions including voltage regulation are considered. To account for electric vehicle charging, a method to determine the uptake in equivalent load is presented. For this, measured data of households and statistical data of electric vehicles are combined in a stochastic analysis to determine the simultaneity factors of household load including electric vehicle charging. The developed methods are applied to an exemplary case study with Norwegian low-voltage networks. Different penetration rates of electric vehicles on a development path until 2040 are considered.
Not all urban low-voltage grids will be able to integrate new loads such as charging infrastructure for electric mobility or electrical heat pumps into existing structures without further measures. Therefore, this article analyzes to what extent load management is more cost-effective than conventional grid expansion. Methodically, the different load types are first apportioned from country to grid-level on the basis of different parameters. Subsequently, both conventional grid planning as a reference variant and innovative grid planning with different variants of load management are carried out. As a result, it can be summarized that the future success of load management is strongly dependent on its costs and whether the necessary information and communication technology is already deployed in the grids. Regardless of the costs, there is also considerable potential for savings in conventional grid expansions.
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