In the current grid infrastructure, the power flow reaching the limits of the grid capacity induces several problems. A way to circumvent this issue is the introduction of flexibility in order to alleviate the impact on the energy network. However, the estimation of the required flexibility remains a problem in the current state of the art. In this thesis, an approach is proposed to estimate the flexibility of distribution systems by identifying assets based on aggregated transformer measurements. Flexibility can be seen as the ability of the system to adapt to changes coming from demand and generation. Different assets can be available such as solar panels, and heat pumps and have their specificities. The problem thus translates into the identification process in aggregated data. Several steps are important in the process, such as the consumption baseline, the study of the different characteristics of the profiles, and the number of iterations. To allow the accuracy to reach high levels and to match the standards of distribution system operators (DSO), the process requires the minimization of squared error between the data and the chosen profiles. Different cases are investigated and validated prior to the study of the results. For the identification of heat pumps and solar panels, individual approaches have been investigated in the literature. However, the combined identification of assets has not yet been studied. A mixed identification process is proposed in this work, and the method has been tested, reaching a high level of accuracy. Within this new framework aiming at separating the assets, options are proposed to enhance the accuracy of the process.