Germany is a forerunner in developing renewable energies. It is therefore of considerable interest to investigate the impact of switch to renewables on the market during transition era. The aim of this study is in two parts: 1) Investigating the volatility; and 2) Conducting a descriptive study on the evolution of daily profiles and emergence of non-positive prices. In terms of volatility quantification, the following characteristics of EPEX prices should be recognized: 1) Covering the whole year (24/7); 2) Taking non-positive values; 3) Depending on calendar information; and 4) Changing according to demand and supply availability. We, therefore, propose a robust and generic approach to account for diurnal or seasonal patterns of human activities in volatility analysis. An important distinction of our work is in introducing an alternative representation (as matrices) for quasi-periodic price data. We, herein, propose a new notion of volatility using a matrix decomposition technique, namely the singular value decomposition (SVD). Our observations indicate price volatility reduction, in the recent years. The second part of this article provides evidences of effect of renewables on daily price profiles – emergence of non-positive prices and also shifts of peak price values to hours where solar is less available.

Scenario-based probabilistic forecasting models have been explored extensively in the literature in recent years. The performance of such models evidently depends to a large extent on how different input (temperature) scenarios are being generated. This paper proposes a generic framework for probabilistic load forecasting using an ensemble of regression trees. A major distinction of the current work is in using matrices as an alternative representation for quasi-periodic time series data. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios in a robust and timely manner. The strength of our proposed method lies in its simplicity and robustness, in terms of the training window size, with no need for subsetting or thresholding to generate temperature scenarios. Furthermore, to systematically account for the non-linear interactions between different variables, a new set of features is defined: the first and second derivatives of the predictors. The empirical case studies performed on the data from the load forecasting track of the Global Energy Forecasting Competition 2014 (GEFCom2014-L) show that the proposed method outperforms the top two scenario-based models with a similar set-up.

We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without the need for specifying user-defined parameters. From a data mining perspective, this opens up new ways of analyzing time series in a data-driven, bottom-up fashion. However, in order to get correct results, it is important to understand how the SVD-spectrum of a time series is influenced by various characteristics of the underlying signal and noise. In this paper, we have extended the work in earlier papers by initiating a more systematic analysis of these effects. We then illustrate our findings on some real-life data.

Many time series in smart energy systems exhibit two different timescales. On the one hand there are patterns linked to daily human activities. On the other hand, there are relatively slow trends linked to seasonal variations. In this paper we interpret these time series as matrices, to be visualized as images. This approach has two advantages: First of all, interpreting such time series as images enables one to visually integrate across the image and makes it therefore easier to spot subtle or faint features. Second, the matrix interpretation also grants elucidation of the underlying structure using well-established matrix decomposition methods. We will illustrate both these aspects for data obtained from the German day-ahead market.

Electrical power grids are vulnerable to cascading failures that can lead to large blackouts. The detection and prevention of cascading failures in power grids are important problems. Currently, grid operators mainly monitor the states (loading levels) of individual components in a power grid. The complex architecture of a power grid, with its many interdependencies, makes it difficult to aggregate the data provided by local components in a meaningful and timely manner. Indeed, monitoring the resilience of an operational power grid to cascading failures is a major challenge.

This paper attempts to address this challenge. It presents a robustness metric based on the topology and operative state of a power grid to quantify the robustness of the grid. Also, it presents a distributed computation method with self-stabilizing properties that can be used for near real-time monitoring of grid robustness. The research thus provides insights into the resilience of a dynamic operational power grid to cascading failures during real-time in a manner that is both scalable and robust. Computations are pushed to the power grid network, making the results available at each node and enabling automated distributed control mechanisms to be implemented.