Due to the rapid electrification, congestion has become a critical challenge in modern power systems, particularly in the Dutch grid. Capacity limitations are negatively affecting existing and new grid connections. Flexibility, represented by the ability to adjust power injections and withdrawals, offers a potential solution.

This thesis investigated the optimal distribution of congestion-relieving power injections in terms of time, location and quantity for energy system planning purposes. A methodology was developed to assess congestion and determine optimal power injection locations using a DC Security-Constrained Optimal Power Flow (SC-OPF) formulation. Where congestion was defined as a state in which an increase in power flow on one or more network branches would lead to the violation of operational security limits. The approach was based on Power Transfer Distribution Factors (PTDFs) and Line Outage Distribution Factors (LODFs) to model the effects of power injections, which represent flexibility. A 73-bus system was used to test with a year of hourly timesteps, considering both N-0 and N-1 security constraints.



According to the results, buses close to overloaded lines and at the end of radial lines are optimal to manage the congestion under N-1 constraints. The radial lines appear to be optimal due to the reduction of system losses. Under N-0 constraints, only buses close to overloaded lines were found to be optimal. The system experienced overloading for 67.4\% of the hours and needed 2,995,225 MWh to manage the congestion under N-1. By contrast, under N-0 constraints, overloading occurred in only 17.1% of the hours, requiring 279,264 MWh. Limiting optimal injection locations under N-0 increased energy needs by 58.2%, while N-1 constraints allowed multiple near-optimal solutions. Scenario comparisons also indicated that increasing branch limits reduced the required corrective energy, whereas imposing additional constraints on injections increased it.

These findings provide insights into the role of flexibility in congestion management for system planning and emphasize the location sensitivity of optimal solutions under different constraints. Future work should explore the sensitivity, uniqueness, and feasibility of solutions, as well as apply the methodology to real system data and location-dependent costs to enhance practical applicability.