Under the increasing electrification of end uses in the energy transition towards more renewable integration, the electricity price keeps gaining importance on every scale from individual well-being to the competitiveness of an economy. Though scarce in the scientific literature, Long-Term Electricity Price Projection (LEPP) has great potentials in decision-making and planning, as well as complementing the long-term energy scenarios. This study takes features from the Dutch, Spanish and Danish data in five years (2015-2019) to train deep neural networks in the conditional Wasserstein Generative Adversarial Nets with Gradient Penalty (cWGAN-GP) framework, in order to project Day-Ahead Market (DAM) price series under Dutch 2050 energy scenarios.

The LEPP to 2050 is made possible by normalising the selected markets. As a result, the conditions unprecedented in Dutch data are covered in the normalised and combined data set. Generally, under scenarios with high proportions of hydrogen power in the energy portfolio, the cWGAN-GP model projects that DAM price series would have slightly lower mean and daily standard deviation than the 2019 level. Whereas much lower mean and daily standard deviation are projected when natural gas is still the fuel of the most frequent final generating technology. To explore the possible application of the projector model, the German DAM prices series in 2019 have been projected and evaluated, and the projections under Dutch 2050 energy scenarios have been used in calculating the generic profit potential of energy storage.

Five findings can be summarised from the main results. Firstly, from a literature survey and importance analyses, seven features are shown relevant to the DAM price in the combined data set, namely month of the year, day of the week, total hourly load forecast, national daily mean temperature, fuel cost of the most frequent final generating technology, hourly renewable power generation forecast and total installed renewable power capacity. Secondly, it has been found that two of the four proposed market state normalisation solutions, the Renewable Scarcity Factor (RSF) and the Renewable-Load Ratio (RLR) help the cWGAN-GP model strike a balance between price value distribution and hourly inter-dependencies. Thirdly, in this LEPP study, the cWGAN-GP model performs better than the Conditional Variational Auto-Encoder (CVAE) and multivariate Gaussian distribution (mGaus) models. Compared with the two alternatives, the cWGAN-GP model produces samples in better quality while remaining sensitive to temporal conditions. Fourthly, projections by the cWGAN-GP model are more realistic than those made by the Energy Transition Model (ETM), with price values varying continuously in smooth boundaries. Finally, the fuel cost of the most frequent final generating technology is found critical to LEPP. The annual mean and daily standard deviation of the DAM price series are expected to rise significantly when natural gas is mostly replaced by hydrogen power in the national energy portfolio.

The production and consumption of electricity need to be balanced at all times. Due to the ever-growing shift towards renewable energy generation, this poses an increasingly difficult challenge. Currently, supply is regulated to maintain balance. However, there is potential to improve reliability and save costs by shifting the balancing to the demand side, known as demand response. The flexibility of water systems can play a role in this, thereby benefiting from cheaper price fluctuations and reducing operating costs.

This research investigates the IJmuiden pumping station, which drains water from the Noordzeekanaal-Amsterdam-Rijnkanaal system into The North Sea. The primary focus of the control of this system is ensuring safe water levels as it runs through areas of high economic value. The flexibility of the range of safe water levels allows costs to be minimized by selecting favourable moments to consume electricity. This simultaneously contributes to the stability of the electrical grid. This research explores the potential for a Reinforcement Learning controller for such an optimization problem, as there are some drawbacks to the Model Predictive Control methods that are currently widely used. The research objective is formulated as follows:

To optimize the control of the IJmuiden pumping station using Reinforcement Learning while complying with local water level restrictions and compare it to the state-of-the-art Model Predictive Control methods in terms of constraint violation, energy costs, and computational speed.

The Reinforcement Learning controller will use a deep Q-learning algorithm that chooses the most cost efficient control in IJmuiden while respecting the water level restrictions. To do so, the model makes decisions based on electricity prices and details about the state of the water system for the current time step as well as a forecast of 48 hours ahead. This data is provided as an input to the model.

The inputs of the model consist of historical data, meaning that the associated uncertainties are not included. The water system that the model can interact with is represented by a linear reservoir model. Therefore, the water system is influenced dynamically by the actions taken by the model. The possible actions are determined by the state of the water system.

The trained model was tested on 2 years of unseen data (data that was not used during training). Using the same test data, control plans were generated using Model Predictive Control. The Reinforcement Learning model was very successful in ensuring safe water levels. However, this did result in approximately 50\% higher energy costs. The use of the gate was close to optimal but the pumping was not clearly correlated with favourable prices and power consumption. The trained model was robust, with consistently accurate results with regards to respecting the water level constraints.

The most significant difference with the Model Predictive Control was the computation time. The Reinforcement Learning model was able to create a control plan approximately 300 times faster. This opens doors for further development of the model and increased complexity. A more accurate model of the water system can be used to take into account temporal and spatial effects and individually representing the six pumps in IJmuiden.

There are still many steps before such a model can be used for operational control, but the method has potential for such an application. Many aspects of the model can be improved as well as making adjustments to increase the usability for control operators.

This research provides benchmark accuracies for forecasting of an aggregated price of the Dutch intraday market. While point forecasts in a single-step-ahead horizon for that unresearched market provide novel insights already, the scope of this research also includes interval forecasts in a multi-step-ahead horizon. A forecasting procedure is established that organizes several stages of in-sample and out-of-sample testing so that the number of arbitrary choices regarding features and hyperparameters is kept as low as possible. It is concluded on the basis of accuracies attained by naive, regression, and artificial neural network models that the machine learning models that are capable to incorporate linear and nonlinear relationships are able to infer to a varying degree what drives intraday from day-ahead prices. Furthermore, it is addressed whether superiority in terms of accuracy coincides with what is deemed as superior in practice. A simulation of a generic system, which consists of a battery and a wind turbine located in the Netherlands, smartly dispatches stored energy according to a schedule optimized with model predictive control based on point forecasts of intraday price. It is concluded that, in general, slightly higher profits are obtained with more accurate point forecasts and that different point forecasts lead to very different dispatch schedules that vary more than 10% in terms of dispatch frequency.