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.

Fuel consumption reduction in Hybrid Electric Vehicles (HEV) powertrains has been an important area of research over the past few decades. HEV powertrains have two energy sources : fuel and battery. The important task of splitting the energy/power demand between both these sources is performed by the Energy Management Systems (EMS). There are many EMS methods and the focus of
this thesis is on a method called Modular ECMS (MEMS) implemented by TNO. MEMS finds the optimal power split among the subsystems by minimizing the energy loss in each subsystem. This strategy assumes that the operating speed of the subsystems of the powertrains is known and uses this knowledge to find the optimal power split and torque among these subsystems. The objective of this thesis is to find the optimal operating speed of the subsystems as well. This is done by a least squares fitting of the objective function and constraints as functions of subsystems speed and torque. A revised Optimal Control Problem (OCP) is formulated as a quadratic programming problem of speed and torque and is termed as Speed-Torque Coupled MEMS (ST-MEMS). The ST-MEMS algorithm is tested on a series-hybrid wheel loader powertrain model and its performance is compared to MEMS, with the model and data provided by TNO. It is concluded that the ST-MEMS, while adding the speed and torque bounds as degrees of freedom, does not achieve a good distribution of power between the 2 sources. The reason for this behaviour is analyzed and an alternate approachis suggested for future work.