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.

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.