The world is going through an energy transition towards decentralized electricity generation like photovoltaic systems and new loads like electric vehicles, which contribute to the variability of the operation state of distribution systems. To ensure future operation under safe and efficient conditions, it becomes increasingly important to have precise knowledge of the state of the grid. For this, Distribution System State Estimation (DSSE) has extensively been discussed in the literature. However, distribution networks at present are not only characterized by sparse measurement locations, which makes application of DSSE difficult due to un-observability, but also the anticipated highly stochastic load profiles will affect the accuracy of the system state estimate. Therefore, it is now necessary to assess how the DSSE accuracy is affected by different factors that result from continuous changes in the consumption pattern and measurement scheme. For this purpose, an improved measurement scheme is proposed in this thesis, that not only considers the meter placement, but also the required measurement intervals, to establish a minimum required accuracy level of the state estimator under stochastic conditions. The work presented in this thesis starts with a sensitivity analysis of the Weighted Least Square (WLS) DSSE algorithm applied on a synthetic model of an existing 10kV distribution network in the Netherlands, with respect to the stochasticity of the load profiles applied. Based on this analysis, an algorithm is developed to determine the measurement configuration that satisfies a predefined maximum DSSE error. The effects of the following parameters on the state estimation accuracy are assessed: i) Percentage of pseudo measurements ii) Stochasticity of the applied load profiles iii) Interval of measurements The analysis can be used to recognize the conditions at which the DSSE results become unacceptable. From here, a novel algorithm for determining a proper measurement scheme is presented, considering the existing meter placement and the required interval of measurements to compensate for the uncertainty in the load profiles. The focus is on determining intervals of measurement to establish a minimum predefined DSSE accuracy level, more than placing additional meters. The method first establishes the meter locations to achieve observability. Then, the algorithm uses the SE error resulting from Monte Carlo simulations to improve the accuracy. The measurement intervals are determined based on the analysis of sensitivity to the interval of measurements. This results in a measurement configuration that maintains the SE error within the acceptable range at every instant of time. It is tested on the 10kV distribution network model, in the future scenario with high stochasticity in load profiles resulting from the projections of the energy transition for the year 2040. The results will show the effectiveness of the proposed algorithm for the determination of measurement scheme. This is important for the network operators who would use the results of the SE algorithm to make decisions on the grid operation.