Nuclear Power: a Hedge against Uncertain Gas and Carbon Prices?

High fossil fuel prices have rekindled interest in nuclear power. This paper identifies specific nuclear characteristics making it unattractive to merchant generators in liberalised electricity markets, and argues that non-fossil fuel technologies have an overlooked à ¢à  à  option valueà ¢à  à  given fuel and carbon price uncertainty. Stochastic optimisation estimates the company option value of keeping open the choice between nuclear and gas technologies. This option value decreases sharply as the correlation between electricity, gas, and carbon prices rises, casting doubt on whether private investorsà ¢à  à  fuel-mix diversification incentives in electricity markets are aligned with the social value of a diverse fuel-mix

Psychological Perspectives on Expertise

No abstract availabl

A counterfactual study of the Charge of the Light Brigade

We use a mathematical model to perform a counterfactual study of the 1854 Charge of the Light Brigade. We first calibrate the model with historical data so that it reproduces the actual charge’s outcome. We then adjust the model to see how that outcome might have changed if the Heavy Brigade had joined the charge, and/or if the charge had targeted the Russian forces on the heights instead of those in the valley. The results suggest that all of the counterfactual attacks would have led to heavier British casualties. However, a charge by both brigades along the valley might plausibly have yielded a British victory

Third Coast Percussion presents Points of Contact

KSU School of Music presents Third Coast Percussion, Points of Contact.https://digitalcommons.kennesaw.edu/musicprograms/1096/thumbnail.jp

Identifying road user classes based on repeated trip behaviour using Bluetooth data

© 2018 The Authors Analysing the repeated trip behaviour of travellers, including trip frequency and intrapersonal variability, can provide insights into traveller needs, flexibility and knowledge of the network, as well as inputs for models including learning and/or behaviour change. Data from emerging data sources provide new opportunities to examine repeated trip making on the road network. Point-to-point sensor data, for example from Bluetooth detectors, is collected using fixed detectors installed next to roads which can record unique identifiers of passing vehicles or travellers which can then be matched across space and time. Such data is used in this research to segment road users based on their repeated trip making behaviour, as has been done in public transportation research using smart card data to understand different categories of users. Rather than deciding on traveller segmentation based on a priori assumptions, the method provides a data driven approach to cluster together travellers who have similar trip regularity and variability between days. Measures which account for the strengths and weaknesses of point-to-point sensor data are presented for (a) spatial variability, using Sequence Alignment, and (b) time of day variability, using Model Based Clustering. The proposed method is also applied to one year of data from 23 fixed Bluetooth detectors in a town in northwest England. The data consists of almost 7.5 million trips made by over 300,000 travellers. Applying the proposed methods allows three traveller user classes to be identified: infrequent, frequent, and very frequent. Interestingly, the spatial and time of day variability characteristics of each user class are distinct and are not linearly correlated with trip frequency. The frequent travellers are observed 1–5 times per week on average and make up 57% of the trips recorded during the year. Focusing on these frequent travellers, it is shown that these can be further separated into those with high spatial and time of day variability and those with low spatial and time of day variability. Understanding the distribution of travellers and trips across these user classes, as well as the repeated trip characteristics of each user class, can inform further data collection and the development of policies targeting the needs of specific travellers

Tradeoff analysis for electric power planning in New England

The use of a multi-attribute trade-off analysis technique as a vehicle to provide information to a diverse group of electric industry interests can play a beneficial role for developing long-range strategies for the electric power sector. The advisory group/analysis team structure presented here allows different groups to evaluate multiple issues simultaneously, incorporating the range of supply and demand options, and future uncertainties characteristic of complex systems.The initial phase of such an Integrated Resource Planning project for New England electric power industry has identified that: significant gains in the areas of reliability and environmental emissions can be made by the introduction of new generating technologies; the recent emphasis on natural gas fired technologies should be matched by an effort to ensure adequate supplies of gas, and other effort to guard against fuel related vulnerabilities