%0 Generic
%A van der Heijden, Ties
%D 2024
%T Dataset underlying the PhD thesis 'Unlocking flexibility: risk-aware operational water and energy management'.
%U 
%R 10.4121/e747fa10-af31-41b8-b984-79132a1efbf0.v1
%K demand response
%K forecasting
%K control
%K markets
%K energy
%K water resources management
%X <p>This thesis explores risk-aware operational decision-making methods to support the integration of Renewable Energy Sources (RES) into the energy system by enhancing energy flexibility under operational uncertainty. Amidst the urgent global shift towards RES to combat climate change, this work identifies and addresses the challenges posed by the intermittent and uncertain nature of renewable energies, such as wind and solar power, to grid stability and energy reliability.</p><p><br></p><p>Central to this objective is the use of Demand Response (DR) strategies to balance energy supply and demand dynamically using electricity spot markets, mitigating the risks associated with the variability and uncertainty of renewable energy sources. A significant contribution of this work lies in the examination of the water-energy nexus through a case study of the Noordzeekanaal–Amsterdam-Rijnkanaal in the Netherlands.</p><p><br></p><p>This dataset contains the underlying code for:</p><ul><li>Exploring the potential benefits of applying DR to the NZK-ARK, a critical piece of the Dutch water defence system and a large energy consumer. By formulating a new MPC problem, in which we propose a multi-market strategy, DAM and IDM prices are used to optimize energy-cost optimal pump schedules.</li><li>Several methodologies applied to model operational uncertainty, detailing how neural networks are trained and optimized for forecasting purposes, what architecture is used to quantify uncertainty and generate probabilistic forecasts, a method for multi-distribution sampling with fixed correlation between variables for the purpose of time series sampling, and a scenario-reduction method is to condense a large uncertainty representation into an optimal subset and sparse scenario tree representation.</li><li>A stochastic MPC framework where we apply risk-aware constraint formulations for a computationally efficient and pragmatic trade-off between energy cost savings and water level violations.</li><li>RayCast, a spatially distributed Quantile Regression irradiance nowcast, that proficiently predicts irradiance quantiles using satellite data.</li></ul>
%I 4TU.ResearchData