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End date: 2022 × Start date: 2022 × DDC: 620 × Type: Article × WGL Institute: PIK ×

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    Why the sustainable provision of low-carbon electricity needs hybrid markets
    (Oxford : Elsevier, 2022) Keppler, Jan Horst; Quemin, Simon; Saguan, Marcelo
    Deep decarbonization of energy systems poses considerable challenges to electricity markets and there is a growing consensus that an energy-only design based on short-term marginal cost pricing cannot deliver adequate levels of investment and long-term coordination across actors and sectors. Based on the instructive example of the evolution of European electricity market designs, we discuss several shortcomings of energy-only markets and illustrate how ad-hoc policies that intend to address them have limitations of their own, notably a lack of systemwide coordination. Second, we describe how the sheer scale and nature of deep decarbonization targets requiring massive investment in capital-intensive low-carbon technologies exacerbate these issues. Ambitious emission reduction targets thus require an evolution of market design towards hybrid regimes. Hybrid markets separate long-term investment decisions from short-term operations through a balanced and differentiated use of competitive and regulatory design elements to coordinate and de-risk investment. Finally, a historical analysis of the evolution of different electricity market designs shows how hybrid markets constitute contemporary forms of long-run marginal cost pricing that are appropriate for meeting deep decarbonization targets with reduced uncertainty and hence lower private and social costs.
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    Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment
    (Basel : MDPI AG, 2022) Haselhoff, Timo; Braun, Tobias; Hornberg, Jonas; Lawrence, Bryce T.; Ahmed, Salman; Gruehn, Dietwald; Moebus, Susanne
    As sustainable metropolitan regions require more densely built-up areas, a comprehensive understanding of the urban acoustic environment (AE) is needed. However, comprehensive datasets of the urban AE and well-established research methods for the AE are scarce. Datasets of audio recordings tend to be large and require a lot of storage space as well as computationally expensive analyses. Thus, knowledge about the long-term urban AE is limited. In recent years, however, these limitations have been steadily overcome, allowing a more comprehensive analysis of the urban AE. In this respect, the objective of this work is to contribute to a better understanding of the time-frequency domain of the urban AE, analysing automatic audio recordings from nine urban settings over ten months. We compute median power spectra as well as normalised spectrograms for all settings. Additionally, we demonstrate the use of frequency correlation matrices (FCMs) as a novel approach to access large audio datasets. Our results show site-dependent patterns in frequency dynamics. Normalised spectrograms reveal that frequency bins with low power hold relevant information and that the AE changes considerably over a year. We demonstrate that this information can be captured by using FCMs, which also unravel communities of interlinked frequency dynamics for all settings.
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    Data-Driven Discovery of Stochastic Differential Equations
    (Beijing : Engineering Sciences Press, 2022) Wang, Yasen; Fang, Huazhen; Jin, Junyang; Ma, Guijun; He, Xin; Dai, Xing; Yue, Zuogong; Cheng, Cheng; Zhang, Hai-Tao; Pu, Donglin; Wu, Dongrui; Yuan, Ye; Gonçalves, Jorge; Kurths, Jürgen; Ding, Han
    Stochastic differential equations (SDEs) are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different sources. The identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system's dynamics. The practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data resources. This study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning (SBL) technique to search for a parsimonious, yet physically necessary representation from the space of candidate basis functions. More importantly, we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series data. The effectiveness of the proposed framework is demonstrated using real data on stock and oil prices, bearing variation, and wind speed, as well as simulated data on well-known stochastic dynamical systems, including the generalized Wiener process and Langevin equation. This framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences, economics, and engineering fields for analysis, prediction, and decision making.