Peak Consumption Research Articles

Use of sacubitril/valsartan as 'bridge to transplant'in patients with end-stage hypertrophic cardiomyopathy.

The endstage or burned-out phase is an uncommon but challenging clinical evolution of hypertrophic cardiomyopathy (HCM). The management of end-stage HCM is empirically based on the use of drugs approved for heart failure with reduced ejection fraction; however, cardiac transplantation often represents the best option to improve survival. In our case, we describe the use of sacubitril/valsartan as a 'bridge to transplant' in a patient with end-stage HCM. After introducing the drug, enhancements in functional capacity, a reduction in natriuretic peptides and an increase in left ventricular ejection fraction occurred. Given their improved volume of oxygen consumption (VO2)peak and hemodynamic parameters, our patient was left off the waiting list for cardiac transplant and continues to be regularly followed-up with every 3months.

Bi-objective optimisation approaches to Job-shop problem with power requirements

Nowadays, a large focus is given to mass personalisation, and multiple path shop floors are suited to such production environments. Hence, this paper deals with the Job-shop scheduling problem that is used for modelling a manufacturing system. Meanwhile, a large attention is given to energy consumption of production systems, but few works consider power requirements of the production systems in order to process operations. In order to contribute in filling this gap, this paper considers the problem where the objective is to minimise both the total completion time of all operations and the instant available power required to process these operations. The problem results in the Bi-objective Job-shop Problem with Power Requirements (Bi-JSPPR). The goal of this paper is to provide a Pareto frontier of schedules minimising both criteria, considering that operations may consume a lot of power at the beginning of the process (consumption peak), more than its consumption after a while, which allows to model power profiles of manufacturing operations. To solve the problem two metaheuristic approaches are investigated: a hybrid Non-dominated Sorting Genetic Algorithm (NSGA-II) and an iterated Greedy Randomized Adaptive Search Procedure coupled with an Evolutionary Local Search (iGRASP×ELS). An efficient local search procedure is specifically designed to improve the quality of solutions in the Pareto frontier of the hybrid NSGA-II (hNSGA-II). Computational experiments and statistical tests are conducted to demonstrate the efficiency of the approaches. Results show that both approach are complementary, having the hNSGA-II showing better average performances, while the iGRASP×ELS is better when high peak power consumption are considered.

Novel integrated structure of carbon dioxide liquefaction energy storage system using solar energy

Humans turned to use renewable energies by underground resource reduction and carbon dioxide emissions increase. One of the ways to reduce carbon dioxide and generate power at the consumption peak is using solar energy to compression and liquefaction of carbon dioxide. At the peak power consumption, the liquid carbon dioxide is gasified to generate power. In this article, a new integrated structure for carbon dioxide liquefaction to power generation using parabolic trough solar collectors is developed and analyzed. This process implements an absorption–compression refrigeration cycle that can generate refrigeration of 17.4 MW to liquefy carbon dioxide at -56 °C in the charging mode. In the discharge mode, a two-stage cryogenic ORC plant and a gas turbine power cycle produce 7.872 MW. This integrated structure has an electricity storage efficiency of 67.87% and a round-trip efficiency of 41.22%. Exergy analysis shows that total exergy efficiency and irreversibility are 83.84% and 2265 kW, respectively. HYSYS and TRNSYS software and MATLAB programming with the geographical conditions of Tehran in Iran are used to simulate the process. The economic analysis by the annualized costs of system method shows that the period of return and the prime cost of product are 5.12 years and 8.55 cents per kWh of electricity, respectively. Finally, the effects of the drastic parameters on the performance of the process are studied investigated.

Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact on the HEMS is assessed. The influence of changing the battery’s capacity and the PV system size on the energy costs and the lost energy are also evaluated. A significant reduction in energy costs and energy lost to the utility can be achieved by combining a suitable overnight charging level, an appropriate sample time, and an accurate forecasting tool. The HEMS has been implemented on an experimental house emulation system to demonstrate it can operate in real-time.

Optimization of the robot motion law by the criterion of minimizing maximum instantaneous power and electric motor size

Maximum instantaneous power consumption of robot drives determines requirements for power supply systems and dimensions of drives for robots and different process machines. The tasks of minimizing peaks in power consumption and drive sizes are acquired when creating autonomous robots operating under restrictions for installed capacity and weight-size parameters of the equipment. For a large number of such robots there are no process restrictions for types of applied motion laws and their numerical characteristics: maximum speeds and accelerations. The motion law type and especially its parameters are traditionally determined by the designer preferences without any justification. These are, for example, restrictions for maximum accelerations or speeds. The first is usually associated with the need to ensure strength and accuracy of the robot, the second - with safety of service personnel in the robot working area. Using the example of one of the most common motion laws the relation of instantaneous power and energy consumption on the payload are studied. Results of this research can be used as a basis for robot drives design in view of accepted criteria when optimal parameters of the motion law are calculated for each drive operation cycle depending on anticipated or actual load.

Optimal Combination of Frequency Control and Peak Shaving With Battery Storage Systems

Combining revenue streams by providing multiple services with battery storage systems increases profitability and enhances the investment case. In this work, we present a novel optimisation and control framework that enables a storage system to optimally combine the provision of primary frequency control services with peak shaving of a consumption profile. We adopt a dynamic programming framework to connect the daily bidding in frequency control markets with the longer term peak shaving objective: reducing the maximum consumption peak over an entire billing period. The framework also allows to aggregate frequency control capacity of multiple batteries installed at different sites, creating synergies when the consumption profile peaks occur on different times. Using a case study of two batteries at two industrial sites, we show that the presented approach increases net profit of the batteries significantly compared to using the batteries for only peak shaving or frequency control.

An empirical analysis of domestic electricity load profiles: Who consumes how much and when?

With the increased share of renewables in power generation, end users play a key role in keeping the demand at levels that better match variable supply, maintaining lower overall system costs, and reducing carbon dioxide emissions. To increase the potential for demand-side flexibility, a deeper understanding of domestic electricity load profiles is needed. Informed by customer grouping based on similar consumption patterns and drivers, targeted interventions can be better designed to time-shift peak loads and reduce overall demand. Thus, the objectives of this study are (i) to segment households in relation to their electricity load patterns using K-means clustering and (ii) to investigate household characteristics that have an influence on electricity load patterns by employing multinomial probit regression. This study uses hourly electricity consumption for 2017, combined with population-based register data for a large sample of Danish households. The results indicate that four distinct Danish household groups are characterized by different timing and magnitudes of electricity consumption, which are influenced by specific sociodemographics and dwelling characteristics. Similarities between the groups emerge with respect to the evening peak consumption, seasonal variation in electricity demand, and weekend morning demand ramp-up. Challenges and opportunities for domestic load profiling in the power industry and policymaking are discussed.

Combining Machine Learning Analysis and Incentive-Based Genetic Algorithms to Optimise Energy District Renewable Self-Consumption in Demand-Response Programs

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district’s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district’s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District’s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator’s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption.

O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions

Abstract. In order to examine O2 consumption and CO2 emission in a megacity, continuous observations of atmospheric O2 and CO2 concentrations, along with CO2 flux, have been carried out simultaneously since March 2016 at the Yoyogi (YYG) site located in the middle of Tokyo, Japan. An average O2 : CO2 exchange ratio for net turbulent O2 and CO2 fluxes (ORF) between the urban area and the overlaying atmosphere was obtained based on an aerodynamic method using the observed O2 and CO2 concentrations. The yearly mean ORF was found to be 1.62, falling within the range of the average OR values of liquid and gas fuels, and the annual average daily mean O2 flux at YYG was estimated to be −16.3 µmol m−2 s−1 based on the ORF and CO2 flux. By using the observed ORF and CO2 flux, along with the inventory-based CO2 emission from human respiration, we estimated the average diurnal cycles of CO2 fluxes from gas and liquid fuel consumption separately for each season. Both the estimated and inventory-based CO2 fluxes from gas fuel consumption showed average diurnal cycles with two peaks, one in the morning and another one in the evening; however, the evening peak of the inventory-based gas consumption was much larger than that estimated from the CO2 flux. This can explain the discrepancy between the observed and inventory-based total CO2 fluxes at YYG. Therefore, simultaneous observations of ORF and CO2 flux are useful in validating CO2 emission inventories from statistical data.

Integration of weather forecast and artificial intelligence for a short-term city-scale natural gas consumption prediction

Ruled by the directives and regulations adopted by the European Commission, the European natural gas market is obliged to enable free competition. This process has just started in the Serbian market, and the national natural gas provider (Srbijagas) has started its restructuring. To attain the objective of natural gas market liberalization, it is essential to accurately plan capacities and make precise predictions of future peak consumption at all levels of trade. In this research, an hourly city-scale Adaptive Neuro-Fuzzy Inference System algorithm with the Gaussian membership function is developed for both heating and non-heating regimes. In order to upgrade previous studies in this field, besides standard gas consumption and weather variables, hourly weather forecast and lower calorific value were added to the model. The model output is predicted hour-ahead natural gas consumption. To measure the predicting performance of the two models proposed in this research, the Coefficient of Determination and Mean Absolute Percentage Error were used. For the considered hourly consumption forecast of heating and non-heating models, the Coefficients of Determination were 0.99, while the overall (training, testing and checking) Mean Absolute Percentage Error values were 3.0% and 3.4% respectively. Based on these performance metrics, it can be concluded that for both models, the proposed algorithm resulted in a very satisfactory forecast. An adequate prediction of future natural gas consumption would support all market stakeholders, leading to the development a cleaner and sustainable energy system.

Could electricity demand contribute to diversifying the mix and mitigating CO2 emissions? A fresh daily analysis of the French electricity system

This paper introduces an innovative line of study to the current literature, by assessing the interactions, in France, between electricity sources, and periods with differing electricity consumption; namely morning off-peak, morning peak, middle off-peak, and night peak, using high-frequency data, specifically, daily data. This paper also analyses the impact of these interactions on both the diversification of the electricity mix, and on carbon dioxide emissions. Thus, this research could help identify the conditions needed, in both supply and demand, for a successful transition towards renewable energies. A Vector Autoregressive model has been employed to detect the presence of endogeneity, and to show the interactions between electricity supply and demand. Consumption in France has been problematic for the management of a portfolio containing rigid and intermittent base load sources. High peak consumption at night has increased the necessity to deploy a flexible electricity generating source, in other words, fossil fuels, and is one of the reasons for nuclear de-activation. In comparison, high morning peak consumption is rational and desirable. Indeed, morning peaks have been satisfied by a portfolio of renewable energies, decreasing the harmful impact of electricity on the environment, and keeping electricity costs low.

PEAK PLANT MODELS IN THE ELECTRIC POWER SYSTEM MODEL OF RELIABILITY AND AVAILABILITY

The work sets out the results of the theory/methodological refinement of models whereby peak generating plants are included in the reliability and availability patterns of electricity systems while operational planning operations are taking place. Account shall be taken of the technical and energy characteristics of such generating units, as well as of the specific conditions and requirements imposed on such generating units, resulting from their location and the role in covering peak loads and consumption of the electricity system.

Correcting exaggerated drinking norms with a mobile message delivery system: Selective prevention with heavy-drinking first-year college students.

Many efficacious interventions designed to reduce college student drinking aim to correct misperceptions of peers' drinking behavior. The present study tested the efficacy of a novel delivery strategy, namely text messages, for promoting promoderation descriptive and injunctive drinking norms. Participants included 121 college students who were randomly assigned to receive daily text messages containing accurate drinking norms (experimental group, n = 61) or historical facts (control group, n = 60) for 10 weeks following a baseline assessment. Participants completed 3-month postbaseline and 6-month postbaseline follow-up assessments. The 3-month assessment revealed that promoderation text messages were effective for reducing peak consumption and alcohol consequences. Changes in descriptive norms and injunctive norms aligned with these two behavioral outcomes. The intervention group reported perceiving others as drinking less on their heaviest drinking day and perceived others as being less approving of alcohol-related consequences than the control group. The intervention group also reported more peer approval of using protective behavioral strategies. Yet intervention effects were not maintained. None of the outcome measures differed by condition at the 6-month postbaseline assessment. Thus, the intervention had short-term effects on self-reported drinking behavior as well as on perceptions of drinking norms. However, the behavioral changes were not maintained when participants were assessed in the second semester after the daily text messages intervention had stopped. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Sustainable Development with Smart Meter Data Analytics Using NoSQL and Self-Organizing Maps

The smart metered electricity consumption data and high dimensional questionnaires provide useful information for designing the tariffs aimed at reducing electricity consumption and peak. The volume of data generated by smart meters for a sample of around four thousand residential consumers requires Not only Structured Query Language (NoSQL) solutions, data management and artificial neural network clustering algorithms, such as Self-Organizing Maps. In this paper, we propose a novel methodology that handles a large volume of data and extracts information from electricity consumption measured at 30 min and from complex questionnaires. Five three-level Time-of-Use tariffs are altered and investigated to minimize the consumers’ payment. Then, input data analysis revealed that the peak consumption is influenced by a segment of consumers that can be targeted to flatten the peak. Based on simulations, more than 23% of the peak consumption can be reduced by shifting it from peak to off-peak hours.

An engineering approach to quantify geomechanical safety factors in UGS programs

Abstract. Underground Gas Storage (UGS) has become one of the most widely used practices to cope with seasonal peaks in energy consumption. The planning of any new UGS facility, or its upgrading to increase the working gas volume and reservoir performance, must be supported by an evaluation of possible induced effects on the environment. From a geomechanical point of view, storage activity results in a cyclic change in stress and deformation in the reservoir rock and the surrounding formations. The main environmental issues to be accounted for when natural fluid pore pressure is planned to be exceeded are the following: (a) the differential displacements at the land surface possibly mining the integrity of ground structure; (b) the integrity of the reservoir and caprock; (c) the possible reactivation of faults, if the target reservoir is located in a faulted basin; and (d) the vertical upheaval and land subsidence that can impact on the surface drainage network in low lying coastal areas. We present an original methodology for evaluating the geomechanical safety of UGS activities using an approach derived from what is traditionally applied in the structural design of buildings. A safety factor, a margin of security against risks, is defined for each of the geomechanical issues listed above. First, a 3D FE-IE numerical model is developed to reproduce the stress and displacement due to the UGS program under evaluation. Then the reservoir pressure is increased until the “failure” condition is reached allowing to evaluate how far the project designed condition is from the above limit. The proposed approach is applied to Romagna, a depleted gas reservoir in Northern Italy converted to UGS, with the aim of investigating the safety of the project to increase the reservoir pressure up to 120 % pi, where pi is the original reservoir pressure before the start of primary production. The 3D geomechanical model has been developed using recent 3D seismic data, land displacements by InSAR, lab tests on reservoir and caprock samples, in-situ Modular Formation Dynamic Tester (MDT) stress tests, and large background information acquired from other UGS reservoirs located in the same sedimentary basin. The analysis outcome has revealed that the investigated scenario is safe, with safety factor larger than 1, in the range from 1.2 to 4. The most critical condition (the smallest safety factor) has been obtained in relation to the mechanical integrity of the reservoir formation, under very conservative conditions (cohesion = 0, friction angle = 30∘).

Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis

Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization ability for unseen consumption profiles. The conducted experiments on the smart meter data of several UK houses demonstrated that if the models are fed with sufficient historical data, they can be generalized to a satisfactory level and produce quite accurate results even if they only use past consumption values as the predictor variables. Furthermore, among the four applied models, the ones based on deep learning and ensemble techniques, display better performance in predicting daily peak load consumption than those of others.

Cost effective energy consumption in a residential building by implementing demand side management in the presence of different classes of power loads

ABSTRACT In the present scenario, in order to meet the growing electricity demand, demand side management (DSM) is one among the various approaches used in smart grid. By applying DSM at the customer side, various benefits such as reduction in electricity cost, reduction in peak demand, and improvement in load factor can be achieved. This paper proposes a new architecture, namely, Intelligent Universal Load Management System (IULMS) to implement DSM based on load shifting approach for a residential building in the presence of different classes of loads. In this work, the objective is to minimize the cost of electricity consumption and there are several constraints imposed by the device types operating in the system and it is solved by Binary Grey Wolf Optimization algorithm. The objective function is non-linear in nature because the price of electricity varies each hour. A real-time system involving residential building users of Agrini Apartment in Madurai, Tamilnadu, India is discussed. Further, the potential of DSM is analyzed. The minimization of electricity consumption cost, peak demand, and Peak to Average Ratio (PAR) are validated for each test case.

Effectiveness of an Ecological Momentary Intervention for Reducing Risky Alcohol Consumption Among Young Adults: Protocol for a Three-Arm Randomized Controlled Trial.

BackgroundRecent research has investigated the utility of mobile phone–delivered interventions for reducing risky single-occasion drinking, also known as binge drinking. In the past five years, focus has been placed on ecological momentary interventions (EMIs), which aim to deliver intervention content in correspondence to real-time assessments of behavior, also known as ecological momentary assessments (EMAs).ObjectiveThis study aims to assess the effect of a fully automated, tailored, mobile phone–delivered EMI termed Mobile Intervention for Drinking in Young people (MIDY) on young people's risky single-occasion drinking behavior.MethodsWe will use a three-armed randomized controlled trial design to determine the impact of MIDY on peak consumption of alcohol among young people. A list of mobile telephone numbers for random digit dialing will be generated, and researchers will telephone potential participants to screen for eligibility. Participants will be randomized into one of three intervention groups. For 6 weeks, EMI, EMA, and attention control groups will complete hourly EMA surveys on their mobile phones on Friday and Saturday nights. EMI participants will receive personalized feedback in the form of text messages corresponding to their EMA survey responses, which focus on alcohol consumption, spending, and mood. EMA participants will not receive feedback. A third group will also complete EMA and receive feedback text messages at the same time intervals, but these will be focused on sedentary behavior and technology use. All groups will also complete a short survey on Saturday and Sunday mornings, with the primary outcome measure taken on Sunday mornings. A more detailed survey will be sent on the final Sunday of the 6-week period, and then again 1 year after recruitment.ResultsThe primary outcome measure will be an observed change (ie, reduction) in the mean peak number of drinks consumed in a single night over the 6-week intervention period between the EMI and attention control groups as measured in the weekly EMA. We expect to see a greater reduction in mean peak drinking in the EMI group compared to that in the attention control group. As a secondary aim, we will assess whether mean peak drinking is reduced in the EMA group compared to the attention control group. We will use a random-effects mixed-modeling approach using maximum-likelihood estimation to provide estimates of differences in peak drinking across time periods between those receiving the intervention (EMI) and attention control participants. An intention-to-treat approach will be taken for the analysis. Individuals and study groups will be modeled as random and fixed factors, respectively.ConclusionsThis study extends our previous work investigating the efficacy of a mobile EMI (MIDY) for reducing risky drinking among young adults in Australia, and will add to the expanding literature on the use of mobile interventions for reducing risky alcohol consumption.Trial RegistrationAustralian New Zealand Clinical Trials Registration (ANZCTR): ACTRN12617001509358p; http://www.anzctr.org.au/ACTRN12617001509358p.aspxInternational Registered Report Identifier (IRRID)DERR1-10.2196/14190

Energy Consumption in Residential Buildings: Comparison between Three Different Building Styles

More than one-third of the electricity generated in the world is being consumed in the residential sector. This study aims to model, simulate, and estimate electrical energy consumption in three different building styles. That is in order to compare and contrast energy consumption categories and their related social and architectural aspects for an unaddressed region that have its particular weather conditions and its special social and environmental aspects. The simulation is done by detailed modeling of the buildings using EnergyPlus. The results demonstrate that water heating systems account for almost one-fifth of the annual energy consumption. Cooling loads were found to be more than 5 times the heating loads. The peak of energy consumption was recorded to be in July, while the lowermost recorded in April and in November. The Apartment style requires the lowest annual energy consumption by an amount of 10 kWh/m2 per person followed by the Duplex house with 13 kWh/m2 per person, while the Single-Story house comes with the highest energy consumption of 18 kWh/m2 per person. These represent local power consumption of 69, 79, and 90 kWh/m2, respectively. On average, the water heating, space cooling, plus interior lights consume about 60% of total energy requirements with a mostly equal share for each, while the equipment has the maximum share of 35% of the total, leaving about 5% for the rest. The results of this study may be used as a reference line in the future for the calculations of energy savings in similar regions.

An ensemble stacked model with bias correction for improved water demand forecasting

ABSTRACT Water demand forecasting is an essential task for water utilities, with increasing importance due to future societal and environmental changes. This paper suggests a new methodology for water demand forecasting, based on model stacking and bias correction that predicts daily demands for groups of ~120 properties. This methodology is compared to a number of models (Artificial Neural Networks – ANNs, Generalised Linear Models – GLMs, Random Forests – RFs, Gradient Boosting Machines – GBMs, Extreme Gradient Boosting – XGBoost, and Deep Neural Networks – DNNs), using real consumption data from the UK, collected at 15–30 minute intervals from 1,793 properties. Results show that the newly proposed stacked model that comprises of RFs, GBMs, DNNs, and GLMs consistently outperformed other water demand forecasting techniques (peak R2 = 74.1%). The stacked model’s accuracy on peak consumption days further improved by applying a bias correction method on the model’s output.