Energy flexibility is the ability to change power production or consumption over time. It is required for a power system to function properly, to balance supply and demand. Currently, the largest providers of energy flexibility in the Netherlands can be found on the supply side and consist mainly out of fossil fueled power plants. As these are set to phase out in the near future and be replaced by mainly variable renewable energy sources, such as wind and solar, the necessity for energy flexibility on the demand side is set to be increased. Therefore, a new role is expected to arise in the power system, namely that of the aggregator. Aggregators will combine small scale energy flexibility providers and provide this aggregated energy flexibility to the power system

In this MSc thesis, the focus lies with a power scheduling Electric Vehicle (EV) aggregator. Considering that the number of EVs will increase in the near future, the lack of control over charging a large fleet of EVs may result in an overloaded distribution grid. The charging behavior of a large fleet of EVs, connected in a Vehicle to Grid (V2G) setting, is formalized as a Model Predictive Control (MPC) optimization problem. Allowing power consumption to be shifted within a finite prediction horizon. To optimally valorize the energy flexibility of the fleet and respect the limits of the distribution grid, the control problem is extended to include spatial information and network constraints. The goal is to develop multiple control algorithms to solve this control problem using distributed optimization.

The contributions of the work in this MSc thesis are threefold. First, the ability to optimally valorize the energy flexibility is increased by including spatial information in the distribution grid, represented as subsets of the fleet, such that congestion management services can be provided. Secondly, a parallel implementation of a coordinated distributed MPC is developed using resource allocation with feasible iterations for binary on/off input systems. Thirdly, a hierarchical MPC algorithm is developed using virtual batteries to represent the aggregated behavior of a fleet of EVs, for which new tight constraints are derived to better represent the EV fleet.

To conclude, numerical experiments are performed in closed loop to study the behavior of the developed algorithms with respect to a centralized benchmark. The experiments show that for a growing EV fleet, the hierarchical algorithm remains at the same approximate error with respect to the benchmark. This, while the distributed algorithm approaches the benchmark very well, with limited communication and in relatively short computation times with respect to the benchmark. For a growing number of subsets using the same amount of EVs, the hierarchical algorithm is able to come up with a feasible solution reasonably fast. Whereas, the distributed algorithm shows a drastic decrease in computation time as multiple smaller problems are now solved. Both algorithms achieve this at increasing costs. Future work is expected to further improve the hierarchical algorithm such that it will be able to outperform the distributed architecture in practical applications.