Periods Of High Demand Research Articles

A framework for forecasting the hourly nodal water demand and improving the performance of real-time hydraulic models considering model uncertainty

Abstract The real-time hydraulic model (RTHM) is a key assistive tool in water distribution system (WDS) management, and its performance directly affects assisted decision-making. This study develops a framework to improve the timeliness and accuracy of RTHMs, which includes the following five steps: flow data processing, establishing nodal water demand (NWD) prediction models, node grouping, data assimilation (DA) and uncertainty analysis. Based on the actual network data, the performance of two data processing methods and three machine learning algorithms are, respectively, compared, and the best is selected for modeling. In the establishment of the hourly NWD prediction models, massive data, including flow measurement and data of all 26 input variables on climate, time and social influencing factors are used. It is found that the time features are the most important model input parameter. Application results of actual network prove that the flow data processing method, accurate NWD prediction, node grouping and Kalman filter-based DA method reduce the uncertainty in the RTHM and improve its timeliness and accuracy, so as to obtain the real-time state estimation of the WDS. Accurate NWD estimation (especially in the high-demand period) and combining RTHM with DA have a great influence on the uncertainty reduction in water pressure estimation, although uncertainty is weakened in the propagation process.

Comparison of Fourteen Reference Evapotranspiration Models With Lysimeter Measurements at a Site in the Humid Alpine Meadow, Northeastern Qinghai-Tibetan Plateau.

Evapotranspiration is a key component in the terrestrial water cycle, and accurate evapotranspiration estimates are critical for water irrigation management. Although many applicable evapotranspiration models have been developed, they are largely focused on low-altitude regions, with less attention given to alpine ecosystems. In this study, we evaluated the performance of fourteen reference evapotranspiration (ET0) models by comparison with large weight lysimeter measurements. Specifically, we used the Bowen ratio energy balance method (BREB), three combination models, seven radiation-based models, and three temperature-based models based on data from June 2017 to December 2018 in a humid alpine meadow in the northeastern Qinghai–Tibetan Plateau. The daily actual evapotranspiration (ETa) data were obtained using large weighing lysimeters located in an alpine Kobresia meadow. We found that the performance of the fourteen ET0 models, ranked on the basis of their root mean square error (RMSE), decreased in the following order: BREB > Priestley-Taylor (PT) > DeBruin-Keijman (DK) > 1963 Penman > FAO-24 Penman > FAO-56 Penman–Monteith > IRMAK1 > Makkink (1957) > Makkink (1967) > Makkink > IRMAK2 > Hargreaves (HAR) > Hargreaves1 (HAR1) > Hargreaves2 (HAR2). For the combination models, the FAO-24 Penman model yielded the highest correlation (0.77), followed by 1963 Penman (0.75) and FAO-56 PM (0.76). For radiation-based models, PT and DK obtained the highest correlation (0.80), followed by Makkink (1967) (0.69), Makkink (1957) (0.69), IRMAK1 (0.66), and IRMAK2 (0.62). For temperature-based models, the HAR model yielded the highest correlation (0.62), HAR1, and HAR2 obtained the same correlation (0.59). Overall, the BREB performed best, with RMSEs of 0.98, followed by combination models (ranging from 1.19 to 1.27 mm day−1 and averaging 1.22 mm day−1), radiation-based models (ranging from 1.02 to 1.42 mm day−1 and averaging 1.27 mm day−1), and temperature-based models (ranging from 1.47 to 1.48 mm day−1 and averaging 1.47 mm day−1). Furthermore, all models tended to underestimate the measured ETa during periods of high evaporative demand (i.e., growing season) and overestimated measured ETa during low evaporative demand (i.e., nongrowing season). Our results provide new insights into the accurate assessment of evapotranspiration in humid alpine meadows in the northeastern Qinghai–Tibetan Plateau.

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Resting costs too: the relative importance of active and resting energy expenditure in a sub-arctic seabird.

ABSTRACTBreeding is costly for many animals, including birds that must deliver food to a central place (i.e. nest). Measuring energy expenditure throughout the breeding season can provide valuable insight into physiological limitations by highlighting periods of high demand, and ultimately allows improvement of conservation strategies. However, quantifying energy expenditure in wildlife can be challenging, as existing methods do not measure both active (e.g. foraging) and resting energy costs across short and long time scales. Here, we developed a novel method for comparing active and resting costs in 66 pre-breeding and breeding seabirds (black-legged kittiwakes, Rissa tridactyla) by combining accelerometry and triiodothyronine (T3) as proxies for active and resting costs, respectively. Active energy costs were higher during incubation (P=0.0004) and chick rearing (P<0.0001) than during pre-laying, because of an increase in the time spent in flight of 11% (P=0.0005) and 15% (P<0.0001), respectively. Levels of T3, reflecting resting costs, peaked marginally during incubation with a mean (±s.d.) concentration of 4.71±1.97 pg ml−1 in comparison to 2.66±1.30 pg ml−1 during pre-laying (P=0.05) and 3.16±2.85 pg ml−1 during chick rearing (P=0.11). Thus, although chick rearing is often assumed to be the costliest breeding stage by multiple studies, our results suggest that incubation could be more costly as a result of high resting costs. We highlight the importance of accounting for both active and resting costs when assessing energy expenditure.

COMPARATIVE ANALYSIS OF BATTERY STORAGE TECHNOLOGIES FOR RESIDENTIAL PHOTOVOLTAIC SOLAR ENERGY INSTALLATIONS

The study concerns a comparative analysis of battery storage technologies used for photovoltaic solar energy installations used in residential applications. Battery storage is needed because of the intermittent nature of photovoltaic solar energy generation and also because of the need to store up excess energy generated in periods of high demand or for sales to the National Grid System. The study consists of three parts; namely: (a) to undertake a comprehensive review of current battery storage technologies. (b) To investigate the performance of the main battery storage technologies that is commercially available (efficiency, energy density, power density, self-discharge per day and power rating); (c). Undertake comparison of battery energy storage technologies. From the findings, it shows that the Lithium Ion Battery technology is the most reliable and most widely used technology for residential applications. It has the best performance characteristics (efficiency, energy density, power density, moderate self-discharge and power rating) however, lithium ion batteries are still relatively expensive among others. Due to these features the Lithium Ion Battery technology stands a total chance of dominating the Battery technology market for residential and automotive applications. Also shows from the findings, the performing reliability of the Lithium ion battery by using the battery application requirements and the dangers in operating at a not required specification. More recommendations were made in areas of challenges faced by the battery storage technologies in order to make improvement.

Vicia sativa as a grazed forage for lactating ewes in a temperate grassland production system

AbstractHere, we present research to overcome a current limitation of temperate grassland systems to provide home‐grown, early season protein for lactating ewes—a period of high protein demand in these systems. Traditionally used as a forage crop, there is renewed interest in common vetch (Vicia sativa) due to its ability to grow during low temperatures over‐winter compared to other legumes. We hypothesised that vetch would support the sustainable development of lamb production by reducing reliance on purchased protein typically used in grass‐only systems. A grazing study determined the performance of early lactation ewes and their twin lambs grazing either an Italian ryegrass (IRG) sward or a vetch/IRG (V/IRG) mix over a six‐week period. The experiment comprised replicate plots of two treatments, with 8 ewes, each rearing twin lambs, grazing each plot. Plots were divided into sub‐plots using electric fencing and rotationally grazed. Findings showed i) ewes selectively grazed vetch as evidenced by a drop in vetch percentage by, on average, 10% units between the start and end of grazing; ii) vetch re‐grew when rotationally grazed with a 21– 28‐d rest period, with vetch percentage of the sward increasing 10% units; and iii) grazing lactating ewes on V/IRG improved combined ewe and lamb weights by five weeks post‐lambing but longer term effects were limited by vetch availability. Furthermore, there was a tendency for lamb live‐weight gains to be higher for lambs whose dams grazed on vetch/IRG compared to IRG swards (573 versus 563 g ewe−1 d−1, respectively, p = 0.056). As the need for alternatives to imported protein feed increases, this research demonstrates how vegetative common vetch, as part of a mixed sward, has the potential to provide a home‐produced winter‐grown protein feed in temperate grasslands.

Modelling of segment level travel time on urban roadway arterials using floating vehicle and GPS probe data

With the increasing traffic congestion levels on urban arterials, an essential step to tackling this challenge is to effectively quantify it and understand how it relates to its contributing factors. Although researchers have proposed models that relate travel time with several traffic and roadway factors for quantifying congestion especially in the Western world, most of these models lack the predictive accuracy for arterials in low-income countries due to differences in roadway and roadside interference factors, heterogeneous traffic flow, and others. A number of them do not incorporate delays from factors including on-street parking activities. Additionally, some existing models are complex in structure and require several parameters which may not be available in many low-income countries such as Ghana or may be expensive to collect. This study aimed at exploring a simple model for predicting travel time which will capture the contributing factors of congestion typical of low-income country arterial road environment and flow characteristics using a multiple linear regression model. Using moving observer method, traffic and roadway data were collected from eight arterial roadways in the Greater Kumasi Metropolitan Area for modeling travel time at the segment level. The fitted model that captures the impact of factors including on-street parking, access density, traffic density, and segment downstream bounding conditions on travel time will aid decision making by transport planners on the factors to consider to mitigate congestion. The model demonstrates how inadequate enforcement of on-street parking restrictions on arterial roadways exacerbates congestion during periods of high demand.

Determinants of Demand Response Program Participation: Contingent Valuation Evidence from a Smart Thermostat Program

As renewable electricity generation continues to increase in the United States (US), considerable effort goes into matching heterogeneous supply to demand at a subhour time-step. As a result, some electric providers offer incentive-based programs for residential consumers that aim to reduce electric demand during high-demand periods. There is little research into determinants of consumer response to incentive-based programs beyond typical sociodemographic characteristics. To add to this body of literature, this paper presents the findings of a dichotomous choice contingent valuation (CV) survey targeting US ratepayers’ participation in a direct-load-control scheme utilizing a smart thermostat designed to reallocate consumer electricity demand on summer days when grid stress is high. Our results show approximately 50% of respondents are willing to participate at a median willingness-to-accept (WTA) figure of USD 9.50 (95% CI: 3.74, 15.25) per month that lasts for one summer (June through August)—or slightly less than USD 30 per annum. Participation is significantly affected by a respondent’s attitudes and preferences surrounding various environmental and institutional perspectives, but not by sociodemographic characteristics. These findings suggest utilities designing direct-load-control programs may improve participation by designing incentives specific to customers’ attitudes and preferences.

Revenue management in restaurants: the role of customers’ suspicion of price increases

ABSTRACT The restaurant industry has yet to fully embrace revenue management (RM) in the form of peak-load pricing. Restaurateurs fear customers’ reactions to price increases could negatively impact their satisfaction and return intentions. In this study an experimental design was used to examine if the explanation provided to customers of a hypothetical restaurant that implemented peak-load menu pricing during periods of high (peak) demand would predict customers’ reactions. Reactions assessed included respondents’ suspicion of the restaurant, perceptions of pricing fairness and price-value, satisfaction, and return intentions. Additionally, the moderating effects of customers’ familiarity with RM and the direct effects of decisional control and income were assessed. Data from 409 respondents showed that higher suspicion was negatively associated with perceptions of pricing fairness and price-value, satisfaction, and return intentions during peak-demand periods (weekends). These lower return intentions, however, were also found for off-peak demand periods (weekdays) when no price increase had been implemented. The results suggest that suspicion, once activated, can lead to customer loss during both peak and off-peak demand periods, and that restaurant operators wanting to use peak-load pricing must carefully consider various factors in its implementation.

Pooled Sampling is an Efficient and Economical Strategy for SARS-CoV-2 Detection in Low-Prevalence Areas.

Corona virus disease 2019 (COVID-19) is a severe acute respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Different pooling testing strategies have been applied for the detection of SARS-CoV-2. However, the discrepancies among different pooling strategies are still to be explored. The aim of this study was to evaluate the two pooling strategies of collecting respiratory specimens for the detection of SARS-CoV-2 RNA. Two groups of five-sample pools were prepared to evaluate the impact of sample pooling and pooled sampling on test sensitivity, respectively. Viral RNA of coronavirus was extracted with the automation system. The N and ORF1ab genes of SARS-CoV-2 RNA were detected with real-time reverse-transcription PCR. The turnaround time of SARS-CoV-2 testing was analyzed before and after the implement of pooled sampling. The pooled sampling displayed advantages in assay sensitivity over the sample pooling. The implementation of pooled sampling significantly shortened the turnaround time of SARS-CoV-2 testing. The pooled sampling is an efficient and economical strategy for SARS-CoV-2 detection during the periods of high screening demand in low-prevalence areas.

Multi-Objective optimization of the Hydrocarbon supply chain under price and demand uncertainty

ABSTRACT The energy sector is known for its enormous investments, despite the erratic behaviour of the oil market (e.g. changes in crude oil prices). Therefore, strategic and tactical planning of the Hydrocarbon supply chain (HCSC), considering market uncertainty, is a significant area of research. HCSC construction involves the integration of crude oil and natural gas supply chains (SCs). In this study, a stochastic multi-objective optimization model is developed for the tactical planning of HCSC. The model considers price and demand uncertainty by formulating a two-stage stochastic programming model. Financial objectives are considered in terms of cost minimization and revenue maximization, while a non-financial objective is considered in terms of depletion rate minimization (i.e. reserves sustainability maximization). The model assists the decision-maker in quantifying the amount of production required to meet demand under different scenarios. Furthermore, the proposed model assists in evaluating the trade-offs among alternatives. A real-world HCSC is used to elucidate the practicability of the model, and some managerial insights are derived by conducting a sensitivity analysis. For instance, production can be reduced during high demand periods to maintain enough reserves, and the excess demand can be satisfied from the outside market based on medium-term contracts.

A Review on Well Integrity Issues for Underground Hydrogen Storage

Abstract Renewable energy production is limited by the fluctuations limiting their application. Underground hydrogen storage (UHS) is one possible alternative to reduce the gap between supply and demand by storing the energy converted to hydrogen as a carrier and store it during surplus to produce it during high demand periods. The hydrogen is stored in the subsurface in geological formations containing the gas and is injected/produced via wells. There is a lack of experience associated with this technology and only a small number of projects worldwide. There are several mechanisms that can compromise the integrity of the well and generate leakage of the stored gas. This paper aims to introduce the challenges associated with well integrity of UHS. Mechanisms that can compromise well integrity and generate leaks include microbial corrosion, hydrogen blistering hydrogen-induced cracking and hydrogen embrittlement, cement degradation, elastomer failure, and caprock sealing failure. Propose well completion criteria, recommendation, and materials selection for newly constructed wells or existing wells. A comparison with more developed storage technologies aims to provide a better understanding of the limitations of hydrogen storage by comparing it to carbon dioxide (carbon capture and storage) and methane (underground gas storage). Finally, evaluation and monitoring techniques are required to see the influence of hydrogen on well integrity. Future research and development will reduce the uncertainties and limitations associated with UHS increasing its feasibility and implementation.

Demand-side flexibility in a residential district: What are the main sources of uncertainty?

With the increasing share of intermittent renewable energy sources in the energy mix, demand-side flexibility is likely to play a key role in the future. For buildings, flexibility is defined as the ability to shift their energy consumption away from “peak periods” i.e. high-demand periods of the electrical network. In France, these episodes occur mainly during the wintertime due to the significant demand for space heating. To achieve flexibility objectives, we explore an indirect control strategy at district scale by adjusting the dwelling thermostat during peak periods. The study is conducted over 337 dwellings in order to better predict the load curve by taking advantage of the aggregation effect. Three main research questions are addressed in relation to the assessment of flexibility potential: (i) the effect of aggregation, (ii) the identification of the most influencing factors, including occupant behavior, and (iii) the quantification of uncertainties. Using an urban building energy modeling tool populated with various national data sources (building envelope, energy class of equipment, etc), we perform a sensitivity analysis on 22 parameters representing the geometry, the appliances, the building characteristics, the occupants, and the grid. The output indicator is the average power shifted during the flexibility (or demand response) event. From this analysis, 7 parameters appear as being the most influential. A regression analysis on these parameters is performed, depending on both the duration of the event and the typology of district. The results show that the duration of the flexibility event and the occupant pre-selected temperature change are the most influential parameters. It results to approximately ± 90 W of uncertainty on an average potential of 290 W of shiftable power per household in a recent district. Furthermore, the occupants are highlighted as making a significant contribution to flexibility. Finally, we observed that the thermal properties investigated with the study of an old fabric district play a key role. Low thermal performance means high heating consumption and increased flexibility potential, but a similar relative uncertainty.

Pre-Employment physical capacity testing as a predictor of musculoskeletal injury in Victorian paramedics.

Paramedic work has periods of intermittent high physical demand, a risk of workplace injury, may be confounded by inherent fitness of the paramedic. This study aimed to identify the nature of workplace musculoskeletal injury, and determine if there was a relationship between pre-employment physical capacity testing (PEPCT) scores and risk of workplace musculoskeletal injury within the paramedic industry. A retrospective case review using PEPCT scores and workplace injury (WI) manual handling data collected from 2008 to 2015 by an Australian pre-hospital emergency care provider (Ambulance Victoria), enabled comparison and analysis of two distinct data sets. A total of 538 paramedics were included for analysis with 34 paramedics reporting a workplace musculoskeletal injury from manual handling. The mean time to injury from commencement of employment was 395.4 days (SD 516.2). Female paramedics represented 53.0%and male paramedics represented 47%of the sample. Mean total PEPCT score for the entire sample was 19.1 (SD 2.9) with a range from 16.2-22, while for those reporting injuries it was 18.3 (SD 2.6) with a range from 15.7-20.9. Musculoskeletal injury amongst Victorian paramedics is more prevalent where the paramedic is female, and/or within three years of commencement of employment. The PEPCT score did not differentiate those at risk of subsequent injury.

Appraisal of surface water quality in vicinity of industrial areas and associated ecological and human health risks: a study on the Bangshi river in Bangladesh

Anthropogenic interventions along the river of the developing countries like Bangladesh have resulted in water quality deterioration and significant environmental and human health hazards. This study aimed to evaluate the water quality of the Bangshi river subjected to the anthropogenic activities, especially resulting from industrial and domestic activities, and to decipher probable ecological and human health risks of river water during the high-demand period. Thus, surface water samples were collected from six sampling stations along the mainstream of the river, and thirteen different water quality parameters were investigated. Weighted arithmetic water quality index (WQI) was employed to evaluate the suitability of river water for drinking and fisheries purposes, whereas the hazard quotient (HQ), hazard index (HI), and ecological risk index (ERI) were used to measure the potential human health and ecological risks derived from polluted river water. The results exhibited that some water quality parameters (EC, DO, and Fe) exceeded the permissible limits at certain sampling stations, indicating the influence of anthropogenic interventions on the river water quality at those areas. The WQI analysis revealed that the river water is unsuitable for drinking and/or fisheries and needs to be treated properly prior to utilization. Principal component analysis (PCA) extracted four principal components accounted for 95.84% of the total variance, suggesting both natural and anthropogenic sources of pollution. The HQ and HI values (<1) suggested low possibility of causing a potential health risks via oral and dermal exposures, while the ERI values (1.79–7.27) also indicated low ecological risks in the study area. Unhealthy human activities that expose this waterbody to pollutants should be stopped, and eco-friendly waste and effluent treatment methods should be used instead.

Dynamic policies for resource reallocation in a robotic mobile fulfillment system with time-varying demand

A Robotic Mobile Fulfillment System (RMFS) is an automated parts-to-picker material handling system, in which robots carry pods with products to the order pickers. It is particularly suitable for e-commerce order fulfillment and can quickly and frequently reallocate workers and robots across the picking and replenishment processes to respond to strong demand fluctuations. More resources for the picking process means lower customer wait times, whereas more resources for the replenishment process means a higher inventory level and product availability. This paper models the RMFS as a queuing network and integrates it within a Markov decision process (MDP), that aims to allocate robots across the pick and replenishment processes during both high and low demand periods, based on the workloads in these processes. We extend existing MDP models with one resource type and one process to an MDP model for two resources types and two processes. The policies derived from the model are compared with benchmark policies from practice. The results show that the length of the peak demand phase and the height of the peak affects the optimal policy choice. In addition, policies that continually reallocate resources based on the workload outperform benchmark policies from practice. Moreover, if the number of robots is limited, continual resource reallocation can reduce costs sharply. The results show that optimal dynamic policies can reduce the cost by up to 52.18% on average compared to optimal fixed policies.

Deep decarbonization impacts on electric load shapes and peak demand

The existing literature has shown the important role of electrification in deep decarbonization pathways, increasing electricity demand as end uses decarbonize. However, studies have not focused on the effects of electrification on aggregate load shapes and peak demand, which influence power sector investments, operations, and costs. Here we investigate potential impacts of deep decarbonization on regional load shapes and peak electricity demand using a detailed end-use simulation model linked to an electric sector capacity planning model. Scenario results suggest that electrification may contribute to peak load increases and shifts from summer peaks to winter ones, especially in cooler climates due to space heating electrification. We illustrate how net-zero emissions goals can amplify electrification and may entail 120%–165% increases in electric system capacity by 2050 due to a combination of electrification and high renewables deployment. The intensity and frequency of peak demand can be limited by load flexibility (providing incentives for electric end uses to shift away from periods of high demand, e.g. through deferrable electric vehicle charging), alternate end-use technology configurations (deploying higher efficiency end-use equipment to lower electricity consumption during peaks or using dual-fuel systems such as heat pumps paired with gas furnaces), and carbon removal (displacing higher marginal abatement cost electrification while reaching an equivalent emissions cap). This analysis is a first step toward systematically exploring load curves for electrified and decarbonized energy systems, and the results highlight opportunities for future research to better understand load shape impacts and flexibility.

A tale of two countries: Increased uptake of digital mental health services during the COVID-19 pandemic in Australia and New Zealand

BackgroundDigital mental health services (DMHS) provide highly accessible psychological supports and interventions that can supplement existing mental health services. Concerns about the mental health impact of the COVID-19 pandemic have been widespread and provide a unique impetus to examine the utility and responsivity of DMHS. This study examined the service utilisation and user characteristics of two DMHS (THIS WAY UP and Just a Thought) in Australia and New Zealand before and during the early pandemic period in these countries (March to June 2020). MethodsService use indices (website visits, course registrations and prescriptions, clinician registrations) were compared across different time periods before (12, 6 and 3 months) and during the pandemic in Australia and New Zealand. The demographic and clinical characteristics of course registrants (anxiety and depression symptom severity and psychological distress) were also compared across the pre- and during-COVID periods. ResultsComparing pre-COVID to during-COVID time periods, we observed substantial increases across all service use indices in both DMHS (increases of >100% on the majority of service use indices). For example, in the 3 months prior to the pandemic, 2806 people registered for a THIS WAY UP course and 1907 people registered for a Just a Thought course, whereas 21,872 and 5442 registered for a THIS WAY UP and Just a Thought course, respectively, during the first 3 months of the COVID pandemic. Slight differences in the demographic and clinical profiles of course registrants were found between pre- and during-COVID time periods, with limited evidence of elevated anxiety and depression symptom severity in the COVID period. ConclusionsFollowing the outbreak of COVID-19 in Australia and New Zealand, the volume of users accessing DMHS increased yet the demographic and clinical characteristics of course registrants remained stable. Results underscore how nimble and scalable DMHS can be during periods of high demand.

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

The Effect of Egg Laying on Feather and Plasma Corticosterone Concentrations in Turkey (Meleagris gallopavo) Hens.

Simple SummaryIt is not known if an energetically demanding process, such as egg laying, can affect corticosterone measured in turkey feathers and blood plasma, or if there are differences between feather types and genetic lines. The objective of this study was to compare hormone levels from feathers and plasma taken before and during egg laying. We found that the corticosterone in the feathers were higher during egg laying, but only for one genetic line. This genetic line produces more eggs and, so, the elevated corticosterone may reflect the higher metabolic investment in producing eggs. Increases in corticosterone levels were found in different feather types; however, the overall hormone level differed between feather types. Unlike the feathers, we found a decrease in plasma hormone measures between the two periods which might reflect the influence of gonadal development or suppression of acute hormone production during lay. From this study, we conclude that feathers can be used to detect increases in corticosterone during periods of high energy demand, but the genetic line needs to be considered, and the results may not correspond with plasma measures. This study also reinforces the requirement for consistent feather sampling when using corticosterone levels in feathers as non-invasive biomarkers.Phenological differences in energy demand (i.e., reproductive status) might influence the measurement of corticosterone. The objective of this study was to compare corticosterone concentrations in feathers (FCORT) and plasma (PCORT) for turkey hens before and during egg laying. Secondary feathers 1 and 3, and a plasma sample were collected from 50 hens at 30 weeks (before egg laying) from two purebred lines. The hens were reexamined during lay (45 weeks) to collect regrown feathers and plasma samples. Corticosterone concentrations were measured using an ELISA. Linear mixed models were used to assess the effect of genetic line (A vs. B) and period (pre-lay vs. lay) on FCORT and PCORT levels. An increase in FCORT during lay was detected for line B (p < 0.0001), but not line A (p = 0.3076). An increase in FCORT during lay was detectable in both feather types, but there was a difference between secondary 1 and 3 in FCORT concentration within each line studied. Conversely, PCORT decreased between the pre-lay and lay periods for both lines, although the decrease was more substantial for line A (p < 0.0001). Differences in metabolic investment in egg production between the two genetic lines may explain the different FCORT response during lay. The results from this study provide insight into how periods of high energy demand may influence corticosterone which should be considered when interpreting results.