The Netherlands is currently undergoing an energy transition in an effort to decarbonize its electrical grid and build a more sustainable generation model. This transition is being led by the integration of non-controllable, sustainable generation sources such as wind and photovoltaic (PV) power. The Dutch wholesale energy market has an imbalance settlement period in which the TSO penalizes or rewards deviations from the last submitted trading program (based on whether the deviations aggravate or relieve the overall market imbalance), and therefore, non-controllable sources are at a higher risks of suffering undesired deviations. The consequences of this are twofold: on one hand, they impose a strain on the grid and an increased demand of ancillary services; and on the other, they risk the economic profitability of the plant.
This issue can be bridged by combining non-controllable generation sources with storage assets.

Although the the dispatch of non-controllable energy sources has been studied extensively, there is a research gap in the proposal of revenue-maximizing strategies for operating hybrid power plants (with wind and PV generation, and energy storage capabilities) in the Dutch wholesale energy market, that account for the stochastic nature of the weather resources and include financial contingency factors.
This thesis aims to bridge that gap by setting up an optimization-based dispatch, using Mixed Integer Linear Programming. The optimization was extended to a scenario-based stochastic optimization, and the Conditional Value at Risk was introduced to account for the intrinsic financial risk of the dispatch under random weather conditions. The resulting problem is a two-stage optimization which was solved using a modified Bender's cut. The intra-day optimizations were also adapted as rolling-horizon dispatches, permitting the operation with periodic updates to the weather forecasts.


The study case for this research was the SWITCH lab, a small-scale laboratory developed by TNO to conduct empirical research on the integration of renewable energies and storage into the grid. TNO also provided the basis for a non-optimized dispatch strategy based on price benchmarking, which was used to compare the performance of the optimized strategy.

The optimized dispatch proved to be an effective strategy for producing maximal-revenue trading programs on all market closings. The optimized revenue provided revenues between 85.8% and 260.1% higher than a generation-alone plant configuration; and an increase in revenue with respect to a generation-only baseline between 300.0% and 8962.9% compared to the non-optimized strategy. Operation under a hybrid configuration using the optmized dispatch also yielded the best economic outlook, having the highest 10-year Net Present Value projections, an average Internal Return on Investment 57.2% higher than the hybrid plant under a non-optimized scheme and a 47.5% lower payback time.

The optimized strategy provided the most profitable trading programs for both the case of deficit and surplus of generation at delivery, turning a positive revenue even under unfavourable market conditions. Conversely, the non-optimized dispatch had the lowest economic outlook of any configuration, with worse NPV, IRR, and payback times than the generation-only plant.

These results highlight the importance of developing dispatch strategies that consider the long-term behaviour of generation and prices, as opposed to here-and-now strategies whose performance was shown to be comparatively deficient; and the synergy between storage and renewable generation sources to bridge the non-controllability problem.