Clarifying the Interpretation and Use of the LOLE Resource Adequacy Metric

The loss-of-load expectation (LOLE) risk metric has been used in probabilistic power system resource adequacy assessment for over 70 years, and today is one of the most recognizable and widely-used measures of system shortfall risk. However, this wide adoption has been accompanied by ambiguities and inconsistencies in its definition and application. This paper provides a unifying reference for defining the metric as it relates to modern analyses, while clarifying a number of common points of confusion in its application. In particular, the paper clarifies that LOLE is not a measure of expected total shortfall duration, a 2.4 hours per year LOLE target implies a less reliable system than a 1 day in 10 years (0.1 days per year) LOLE target, and exact conversions between hourly and daily LOLE targets are not generally possible. Illustrative examples are provided to help explain each of these points.

Clarifying the Interpretation and Use of the LOLE Resource Adequacy Metric

The loss-of-load expectation (LOLE) risk metric has been used in probabilistic power system resource adequacy assessment for over 70 years, and today is one of the most recognizable and widely-used measures of system shortfall risk. However, this wide adoption has been accompanied by ambiguities and inconsistencies in its definition and application. This paper provides a unifying reference for defining the metric as it relates to modern analyses, while clarifying a number of common points of confusion in its application. In particular, the paper clarifies that LOLE is not a measure of expected total shortfall duration, a 2.4 hours per year LOLE target implies a less reliable system than a 1 day in 10 years (0.1 days per year) LOLE target, and exact conversions between hourly and daily LOLE targets are not generally possible. Illustrative examples are provided to help explain each of these points.

The promotion of stress tolerant Symbiodiniaceae dominance in juveniles of two coral species due to simulated future conditions of ocean warming and acidification

The symbiotic relationship between coral and its endosymbiotic algae, Symbiodiniaceae, greatly influences the hosts potential to withstand environmental stress. To date, the effects of climate change on this relationship has primarily focused on adult corals. Uncovering the effects of environmental stress on the establishment and development of this symbiosis in early life stages is critical for predicting how corals may respond to climate change. To determine the impacts of future climate projections on the establishment of symbionts in juvenile corals, ITS2 amplicon sequencing of single coral juveniles was applied to Goniastrea retiformis and Acropora millepora before and after exposure to three climate conditions of varying temperature and pCO2 levels (current and RCP8.5 in 2050 and 2100). Compared to ambient conditions, juvenile corals experienced shuffling in the relative abundance of Cladocopium (C1m, reduction) to Durusdinium (D1 and D1a, increase) over time. We calculated a novel risk metric incorporating functional redundancy and likelihood of impact on host physiology to identify the loss of D1a as a low risk to the coral compared to the loss of higher risk taxa like D1 and C1m. Although the increase in stress tolerant Durusdinium under future warming was encouraging for A. millepora, by 2100, G. retiformis communities displayed signs of symbiosis de-regulation, suggesting this acclimatory mechanism may have species-specific thresholds. These results emphasize the need for understanding of long-term effects of climate change induced stress on coral juveniles and their potential for increased acclimation to heat tolerance through changes in symbiosis.

Development of a Collagen Fibre Remodelling Rupture Risk Metric for Potentially Vulnerable Carotid Artery Atherosclerotic Plaques

Atherosclerotic plaque rupture in carotid arteries can lead to stroke which is one of the leading causes of death or disability worldwide. The accumulation of atherosclerotic plaque in an artery changes the mechanical properties of the vessel. Whilst healthy arteries can continuously adapt to mechanical loads by remodelling their internal structure, particularly the load-bearing collagen fibres, diseased vessels may have limited remodelling capabilities. In this study, a local stress modulated remodelling algorithm is proposed to explore the mechanical response of arterial tissue to the remodelling of collagen fibres. This stress driven remodelling algorithm is used to predict the optimum distribution of fibres in healthy and diseased human carotid bifurcations obtained using Magnetic Resonance Imaging (MRI). In the models, healthy geometries were segmented into two layers: media and adventitia and diseased into four components: adventitia, media, plaque atheroma and lipid pool (when present in the MRI images). A novel meshing technique for hexahedral meshing of these geometries is also demonstrated. Using the remodelling algorithm, the optimum fibre patterns in various patient specific plaques are identified and the role that deviations from these fibre configurations in plaque vulnerability is shown. This study provides critical insights into the collagen fibre patterns required in carotid artery and plaque tissue to maintain plaque stability.

Seismic risk assessments for real estate portfolios: Impact of engineering investigation on quality of seismic risk studies

Seismic risk evaluation studies for real estate portfolios conducted by technical professionals (often Civil and Structural Engineers) have become increasingly desirable and common in financial decisions. In this article, we develop a series of risk measures and ratings based on common outcomes from probabilistic portfolio seismic risk assessments. We first define two portfolio risk metrics: Portfolio Expected Loss (PELα) and Portfolio Upper Loss (PULα), where “α” is the annual exceedance probability, or the corresponding return period (“1/α”). PULα/PELα ratio characterizes the uncertainty in estimated portfolio risks which results from the uncertainty in seismic performance of the individual assets. Three uncertainty levels are defined, namely, low, medium, and high, based on the PULα/PELα. We then develop an asset risk metric, called Tail Contribution Index (TCIα), that characterizes the contribution of individual assets to the portfolio losses that fall within the high-consequence “tail” of the portfolio loss distribution. To describe the overall engineering efforts of a portfolio seismic risk study, we develop a portfolio risk metric, called Portfolio Level of Investigation (PLIα), that characterizes the effective level of engineering investigation. Three investigation levels are defined: low ( desktop), medium ( semi-engineered), and high ( engineered), based on the PLIα. Finally, based on the combination of uncertainty level and investigation level, we develop a rating scheme by which the quality (Qα) of a portfolio seismic risk study is characterized. Five quality levels are defined: very poor, poor, fair, good, and very good. These risk indices and ratings can help stakeholders and technical professionals better diagnose and communicate portfolio seismic risks, scope adequate studies, effectively utilize valuable resources, and base financial decisions on risk assessment results that have the desired reliability.

Financial Risk Control of Hydro Generation Systems through Market Intelligence and Stochastic Optimization

In the competitive electricity wholesale market, decisions regarding hydro generators are generally made under uncertain conditions, such as pool price, hydrological affluence, and other players’ strategies. From this perspective, this work presents a computational model formulation with associated market intelligence and game theory tools to support a decision-making process in a competitive environment. The idea behind using a market intelligence tool is to apply a stochastic optimization model with an associated conditional value at risk metric defining a utility function, which calculates the weight that the agents attribute to each stochastic variable associated with the problem to be faced. Subsequently, this utility function is used to emulate the other agents’ strategies based on their previous decisions. The final step finds the Nash equilibrium solution between a player and their competitors. The methodology is applied to the monthly allocation of firm energy by hydro generators under the current Brazilian regulatory framework. The results show a change in the generators’ behavior over the years, from risk-neutral agents seeking to maximize their return with 88% of decisions based on spot price forecasts in 2015, to risk-averse agents with 100% of decisions following a factor that is directly impacted by the hydrological affluence forecasts in 2018.

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology- based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We develop a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (a) individual exposures and (b) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the Regional Relative Risk – a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions (in theory, these regions can range from individual rooms to large geographic areas). In this paper, we apply the Regional Relative Risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km demonstrating that in many common cases, minimal input information is required to use the metric. Also, we demonstrate that it is consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. This work provides context for (a) the initial stages of an airborne disease outbreak and (b) larger scale disease spread, including unexpected low probability disease “sparks” that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks. Importance Airborne infectious disease transmission events occur over a wide range of spatial scales and can be important to disease outbreaks. We develop a new physics- and biology- based theory for the important case in which individuals are infected by a single airborne particle (numerous infectious particles can be emitted into the air and inhaled). Based on this theory, we propose a new epidemiological metric, Regional Relative Risk, that compares the risk between two geographic regions (in theory, regions can range from individual rooms to large areas). Our modeling of outdoors transmission events predicts that for many scenarios of interest, minimal information is required to use this metric for locations 50 m to 20 km downwind. This prediction is consistent with data from prior disease outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as indoor environments. Our results may a priori be applicable to many airborne diseases as these results depend on the physics of airborne particulate dispersion.