At present, the energy transition is increasingly being driven by the integration of renewable energy into the grid. However, this weather-dependent energy generation imposes challenges to the system operators since the deviations between the forecasted and the actual generation keep increasing due to this intermittent generation. Moreover, new consumption patterns and the emergence of new types of loads in the distribution grids, such as heat pumps and electric vehicles, increase the dynamics and complexity of the power system. Therefore, distribution grids need to become more active, being able to monitor the real-time changes and controlling the different network elements. In this context, operational flexibility is seen by the DSOs as a suitable solution for keeping the power balance of the system during a specific time period, coping with fluctuations and responding to variations of electricity supply and demand. For this purpose, the impact of the integration of different flexibility sources in the distribution grid has to be assessed by the DSO in order to overcome the operational challenges that it is currently facing. In order to assess the performance of flexibility in a network, this thesis proposes how to model an active distribution network based on the Dutch standards. Taking as a reference a distribution grid built on the software Vision Network Analysis, a converter was developed in order to export the network data to the input data needed in the data matrices of the open-source tool Matpower. The second step was centred upon the development of controls that simulated the action of different flexibility sources, applied to the Matpower network. For this research, flexibility from the PV distributed generation, flexible loads with demand response and short-term flexible storage have been considered. With the aim of performing a comparative assessment of the impact of these flexibility sources, the operating limits of the test network have been simulated under three different demand levels for a complete day. The base line scenario keeps the demand levels already set for the network, while the under-scaled scenario assumes a 40\% lower demand for the complete day. Additionally, an over-scaled scenario has been proposed, which considers the expected demand increase for 2030 with the corresponding integration of heat pumps and electric vehicles. In general, higher demand rates imposed longer undervoltage peaks. Then, the effect of the flexibility sources was assessed by means of the 1-hour resolution controls. It was found that the voltage regulation performed by the droop control applied to distributed generators was minimum and not useful for this type of study. On the other hand, the combination of demand response and flexible storage can deal with all the voltage deviations beyond the allowed limits. The analysis for each case will depend on the limit set for the power curtailment by demand response and the size of the necessary flexible storage system to reach a complete voltage regulation along the network. This analysis demonstrates the influence of reactive power in voltage control, as well as the influence of active power in the loading levels of the network branches. Based on these factors and in order to provide the DSO with operational indicators for the quantification of short-term flexibility, four indicators have been proposed in order to compare the needs of flexibility between networks and the performance of flexibility sources in terms of voltage regulation and loadings of the branches.