Sensitivity and uncertainty analyses in external cost assessments of fusion power

Given that existing fire risk models often ignore human and organizational errors (HOEs) ultimately leading to underestimation of risks by as much as 80%, this study employs a technical-human-organizational risk (T-H-O-Risk) methodology to address knowledge gaps in current state-of-the-art probabilistic risk analysis (PRA) for high-rise residential buildings with the following goals: (1) Develop an improved PRA methodology to address concerns that deterministic, fire engineering approaches significantly underestimate safety levels that lead to inaccurate fire safety levels. (2) Enhance existing fire safety verification methods by incorporating probabilistic risk approach and HOEs for (i) a more inclusive view of risk, and (ii) to overcome the deterministic nature of current verification methods. (3) Perform comprehensive sensitivity and uncertainty analyses to address uncertainties in numerical estimates used in fault tree/event trees, Bayesian network and system dynamics and their propagation in a probabilistic model. (4) Quantification of human and organizational risks for high-rise residential buildings which contributes towards a policy agenda in the direction of a sustainable, risk-based regulatory regime. This research contributes to the development of the next-generation building codes and risk assessment methodologies.

Download Full-text

Life Cycle Cost of Electricity Production: A Comparative Study of Coal-Fired, Biomass, and Wind Power in China

Energies ◽

10.3390/en14123463 ◽

2021 ◽

Vol 14 (12) ◽

pp. 3463

Author(s):

Xueliang Yuan ◽

Leping Chen ◽

Xuerou Sheng ◽

Mengyue Liu ◽

Yue Xu ◽

...

Keyword(s):

Power Generation ◽

Life Cycle ◽

Wind Power ◽

Life Cycle Cost ◽

Raw Material ◽

Electricity Production ◽

Maintenance Cost ◽

External Cost ◽

Levelized Cost Of Electricity ◽

Cost Of Electricity

Economic cost is decisive for the development of different power generation. Life cycle cost (LCC) is a useful tool in calculating the cost at all life stages of electricity generation. This study improves the levelized cost of electricity (LCOE) model as the LCC calculation methods from three aspects, including considering the quantification of external cost, expanding the compositions of internal cost, and discounting power generation. The improved LCOE model is applied to three representative kinds of power generation, namely, coal-fired, biomass, and wind power in China, in the base year 2015. The external cost is quantified based on the ReCiPe model and an economic value conversion factor system. Results show that the internal cost of coal-fired, biomass, and wind power are 0.049, 0.098, and 0.081 USD/kWh, separately. With the quantification of external cost, the LCCs of the three are 0.275, 0.249, and 0.081 USD/kWh, respectively. Sensitivity analysis is conducted on the discount rate and five cost factors, namely, the capital cost, raw material cost, operational and maintenance cost (O&M cost), other annual costs, and external costs. The results provide a quantitative reference for decision makings of electricity production and consumption.

Download Full-text

Using Spatial Validity and Uncertainty Metrics to Determine the Relative Suitability of Alternative Suites of Oceanographic Data for Seabed Biotope Prediction. A Case Study from the Barents Sea, Norway

Geosciences ◽

10.3390/geosciences11020048 ◽

2021 ◽

Vol 11 (2) ◽

pp. 48

Author(s):

Margaret F.J. Dolan ◽

Rebecca E. Ross ◽

Jon Albretsen ◽

Jofrid Skarðhamar ◽

Genoveva Gonzalez-Mirelis ◽

...

Keyword(s):

Barents Sea ◽

Predictor Variable ◽

Environmental Data ◽

Spatial Uncertainty ◽

Benthic Habitat ◽

Habitat Distribution ◽

Distribution Models ◽

Entire Study ◽

Oceanographic Data ◽

Uncertainty Analyses

The use of habitat distribution models (HDMs) has become common in benthic habitat mapping for combining limited seabed observations with full-coverage environmental data to produce classified maps showing predicted habitat distribution for an entire study area. However, relatively few HDMs include oceanographic predictors, or present spatial validity or uncertainty analyses to support the classified predictions. Without reference studies it can be challenging to assess which type of oceanographic model data should be used, or developed, for this purpose. In this study, we compare biotope maps built using predictor variable suites from three different oceanographic models with differing levels of detail on near-bottom conditions. These results are compared with a baseline model without oceanographic predictors. We use associated spatial validity and uncertainty analyses to assess which oceanographic data may be best suited to biotope mapping. Our results show how spatial validity and uncertainty metrics capture differences between HDM outputs which are otherwise not apparent from standard non-spatial accuracy assessments or the classified maps themselves. We conclude that biotope HDMs incorporating high-resolution, preferably bottom-optimised, oceanography data can best minimise spatial uncertainty and maximise spatial validity. Furthermore, our results suggest that incorporating coarser oceanographic data may lead to more uncertainty than omitting such data.

Download Full-text