This paper presents a class of risk measures to be used as damage indicators within particle filtering (PF)-based real-time prognosis algorithms, with application to the case of state-of-charge prediction in lithium-ion batteries. The proposed risk measure not only incorporates the risk of battery failure but also is a measure for the confidence on the prognosis algorithm itself. In addition, a novel simplified PF-based prognostic method is proposed to estimate the battery discharge time, while providing a computationally inexpensive solution. Computing times for both the novel prognosis routine and the associated risk measure are fast enough to allow their implementation in real-time applications, such as decision-making systems or path-planning algorithms.Index Terms-Lithium-ion (Li-ion) battery, risk management, state-of-charge (SoC) prognosis.

Cost efficient deployment of wind energy is in focus for reaching ambitious targets for renewable energy and transforming the energy supply to one based on renewables. However, as more wind is being deployed the available sites onshore become less attractive in terms of wind conditions and capacity factor and more resistance from population groups affected in the deployment areas results in a reduction of areas that can be developed. We consider three different methods for estimating acceptance costs, one based on compensation and property purchase costs, one based on property value loss near wind turbines, and one based on willingness to pay calculated from a stated preference study. Utilising these methods, we provide an estimation of Levelised Cost of Energy (LCOE) for an expansion in Denmark of onshore and offshore wind capacity of 12 GW. We find that the three methods provide similar estimates for local acceptance, but that a high range of uncertainty exists in the upper bound of acceptance costs. Onshore does not have a clear-cut cost advantage over offshore when considering substantial amounts of wind capacity expansion and using high estimates for nationwide acceptance costs. Moderate onshore wind expansion considering only local acceptance has a cost advantage.

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Using the insights of current research in corporate finance and financial institutions, the authors briefly present a consistent economic framework for looking at insurance. Shareholders of insurance companies provide risk capital that is invested in financial assets and therefore earns the market return of the assets it is invested in. However, due to the legal and fiscal environment insurance companies are in, they have a competitive disadvantage at investing, and this gives rise to frictional capital costs. The core competence of insurers is in managing the size of these frictional capital costs. Insurers must ensure that they can sell insurance for a price in excess of what they need to produce the cover they sell and compensate the incurred frictional costs on risk capital. It is through the ability to do so that insurers create shareholder value.

The U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC) routinely produces finite-fault models following significant earthquakes. These models are spatiotemporal estimates of coseismic slip critical to constraining downstream response products such as ShakeMap ground motion estimates, Prompt Assessment of Global Earthquake for Response loss estimates, and ground failure assessments. Because large earthquakes can involve slip over tens to hundreds of kilometers, point-source approximations are insufficient, and it is vital to rapidly assess the amount, timing, and location of slip along the fault. Initially, the USGS finite-fault products were computed in the first several hours after a significant earthquake, using teleseismic body wave and surface wave observations. With only teleseismic waveforms, it is generally possible to obtain a reliable model for earthquakes of magnitude 7 and larger. Here, we detail newly implemented updates to NEIC’s modeling capabilities, specifically to allow joint modeling of local-to-regional strong-motion accelerometer, Global Navigation Satellite System (GNSS), and Interferometric Synthetic Aperture Radar (InSAR) observations in addition to teleseismic waveforms. We present joint inversion results for the 2015 Mw 8.3 Illapel, Chile, earthquake, to confirm the method’s reliability. Next, we provide examples from recent earthquakes: the 29 July 2021 Mw 8.2 Chignik, Alaska, United States, the 14 August 2021 Mw 7.2 Nippes, Haiti, and the 8 July 2021 Mw 6.0 Antelope Valley, California, United States, earthquakes. These examples confirm that jointly leveraging a variety of geophysical datasets improves the reliability of the slip model and demonstrate that such a combination can be leveraged for rapid response. The inclusion of these new datasets allows for more consistent finite-fault modeling of earthquakes as small as magnitude 6. As accelerometer, GNSS, and InSAR observations worldwide become more accessible, these joint models will become more routine, providing improved resolution and spatiotemporal constraints on rapid finite-fault models, and thereby improving the estimates of downstream earthquake response products.