Achieving net-zero emissions by 2050 requires a transition to renewable energy sources. With this transition comes the electrification of industrial processes, transport, and heating. However, this electrification also increases the risk of grid congestion, and grid capacity becomes scarce. In the Netherlands alone, more than 12,000 companies are currently awaiting new or expanded grid connections, which is slowing down the energy transition.

One solution to this problem is the Energy Hub (EH). This smart, decentralized system locally coordinates the generation, storage, conversion, and consumption of energy. Special juridical forms of an Energy Hub have recently been developed, such as the Group Transport Agreement (GTA) and the Capacity Limiting Contract (CLC). This thesis focuses on allocating electrical power within Energy Hubs operating under group contracts.

The core challenge lies in the collective management of this shared, limited power capacity. An Energy Management System (EMS) is required to allocate power to diverse flexible assets dynamically. These assets, including a battery, solar park, batch process, and refrigeration unit, are modeled to simulate an Energy Hub that leverages the flexibility of these assets, ensuring optimal utilization of capacity and minimizing both collective and individual costs. For this, a Model Predictive Control (MPC) scheme is employed. By proposing and evaluating an integral model for power allocation, this thesis demonstrates that the Energy Hub can simultaneously reserve significantly less overall grid capacity and reduce operating costs while strictly adhering to the limits of the aforementioned group contracts.