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Abstract-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 wellestablished matrix decomposition methods. We will illustrate both these aspects for data obtained from the German day-ahead market.

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a b s t r a c tGermany 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 nonpositive 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 e emergence of nonpositive prices and also shifts of peak price values to hours where solar is less available.

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