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End date: 2023 × Start date: 2023 × End date: 2024 × Start date: 2020 × DDC: 620 × 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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    Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China
    ([London] : Nature Publishing Group UK, 2021) Xing, Xiaofan; Wang, Rong; Bauer, Nico; Ciais, Philippe; Cao, Junji; Chen, Jianmin; Tang, Xu; Wang, Lin; Yang, Xin; Boucher, Olivier; Goll, Daniel; Peñuelas, Josep; Janssens, Ivan A.; Balkanski, Yves; Clark, James; Ma, Jianmin; Pan, Bo; Zhang, Shicheng; Ye, Xingnan; Wang, Yutao; Li, Qing; Luo, Gang; Shen, Guofeng; Li, Wei; Yang, Yechen; Xu, Siqing
    As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China.
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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.