Contents I. Introduction
The growth in stochastic (renewable) generation and loads leads to an increased uncertainty in future load profiles and consequently complicates reliable network planning. Smart grid alternatives such as demand side management (DSM) and energy storage could be beneficial as they allow local control of grid loading, thereby making it possible to deal with bottlenecks to defer or prevent traditional grid investments [1]. The proper evaluation of such alternatives however, requires more detailed load and generation models than is currently common in network assessments. So far, grid operators often make use of deterministic and aggregated load models in the evaluation of their network investment plans [2]. These models have the great advantage of being straightforward to apply and clear to assess, however they fail to explicitly show the inherent uncertainties that are present, especially at the low voltage grid level. This might lead to erroneous expected network loadings and suboptimal conclusions about the valuation and effectiveness of different investment alternatives. The growth in stochastic local renewable generation and the increase in new high peak power loads such as electric vehicles and heat pumps warrants the use of more detailed load models to properly assess grid adequacy.
