Increased variable renewable energy sources penetration in The Netherlands require more flexibility that could be provided by distributed energy resources located at small end-users combined with demand response. Aggregators could act as intermediary entities to exploit the flexibility potential of small end-users and create value for Distribution System Operators (DSOs) by delivering electric flexibility services that support congestion management. Aggregator Eneco CrowdNett is planning to commission a pool of households which will have a combined system of solar-PV and residential Battery Energy Storage (BES) units. Next to optimizing the household’s self-consumption, these systems will jointly be able to deliver power to the grid. Therefore, the system as designed by Eneco CrowdNett is used as a case-study during this research. This research aims to provide a comprehensive understanding of how the value of an Aggregator’s electric flexibility services can be determined for DSOs, without jeopardizing the business case for the Aggregator and the CrowdNett participants themselves. A tool that simulates the behavior of residential BES units connected to the LV distribution grid is developed and tested, followed by a reflection on its usability for other cases. The system under review in this study is framed as a Unit Commitment Problem (UCP), in which the generation units are represented by CrowdNett’s residential BES units that primarily deliver local service, but can also deliver congestion management services through load shifting. Linear Programming (LP) is considered a useful method to solve the UCP, which is implemented in the modelling software Linny-R. It can be concluded the value of Eneco CrowdNett’s electric flexibility services for DSOs can not be determined by exclusive using a Unit Commitment model with a LP solver to replicate the behavior of residential BES units located at the LV distribution grid. The model rather provides a tool to generally assess whether an Aggregator is able to reduce congestions in a given distribution network without jeopardizing her own business case and that of the end-users owning the BES units. The model should be supplied with input data regarding residential demand profiles, PV production profiles and the power capacity of the LV substation before the model output can be compared with the expected costs of grid reinforcements on a case-by-case basis to determine the value for DSOs.