Variations In Energy Demand Research Articles

Feed Restriction Reveals Distinct Serum Metabolome Profiles in Chickens Divergent in Feed Efficiency Traits.

Restrictive feeding influences systemic metabolism of nutrients; however, this impact has not been evaluated in chickens of diverging feed efficiency. This study investigated the effect of ad libitum versus restrictive feeding (85% of ad libitum) on the serum metabolome and white blood cell composition in chickens of diverging residual feed intake (RFI; metric for feed efficiency). Blood samples were collected between days 33 and 37 post-hatch. While serum glucose was similar, serum uric acid and cholesterol were indicative of the nutritional status and chicken’s RFI, respectively. Feed restriction and RFI rank caused distinct serum metabolome profiles, whereby restrictive feeding also increased the blood lymphocyte proportion. Most importantly, 10 amino acids were associated with RFI rank in birds, whereas restrictive feeding affected almost all detected lysophosphatidylcholines, with 3 being higher and 6 being lower in restrictively compared to ad libitum fed chickens. As indicated by relevance networking, isoleucine, lysine, valine, histidine, and ornithine were the most discriminant for high RFI, whereas 3 biogenic amines (carnosine, putrescine, and spermidine) and 3 diacyl-glycerophospholipids (38:4, 38:5, and 40:5) positively correlated with feed intake and body weight gain, respectively. Only for taurine, feed intake mostly explained the RFI-associated variation, whereas for most metabolites, other host physiological factors played a greater role for the RFI-associated differences, and was potentially related to insulin-signaling, phospholipase A2, and arachidonic acid metabolism. Alterations in the hepatic synthesis of long-chain fatty acids and the need for precursors for gluconeogenesis due to varying energy demand may explain the marked differences in serum metabolite profiles in ad libitum and restrictively fed birds.

Thermodynamic and economic analysis of the integration of high-temperature heat pumps in trigeneration systems

Polygeneration energy systems are proven to be a reliable, competitive and efficient solution for energy production. The recovery of otherwise wasted energy is the primary reason for the high efficiency of polygeneration systems. In this paper, the integration of a high-temperature heat pump within a trigeneration system is investigated. The heat pump uses the low-temperature heat from the condenser of the absorption chiller as heat source to produce hot water. A numerical model of the heat pump cycle is developed to evaluate the technical viability of current heat pump technology for this application and assess the performance of different working fluids. An exergy analysis is performed to show the advantages of the novel trigeneration system with respect to traditional systems for energy production. Moreover, a levelized cost of electricity method is applied to the proposed energy system to show its generic economic feasibility. Finally, actual energy demand data from an Italian pharmaceutical factory are considered to evaluate the economic savings obtainable with the integrated system, implemented in a case study. A two-level algorithm is proposed for the economic optimization of the investment. The synthesis/design problem is addressed by a genetic algorithm and the optimal operation problem is solved by a linear programming method. Results show that the integration of a high-temperature heat pump within a trigeneration system provides flexibility to cover variable energy demands and achieve valuable economic and energy performances, with global cost savings of around 40% with respect to separate production and around 10% with respect to traditional cogeneration and trigeneration systems.

Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions

The uncertainty of operating conditions such as weather and payload cause variations in the energy demand of electric city buses. Uncertain variation in energy demand is a challenge in the design of charging systems and on-board energy storages. To predict the energy demand, a computationally efficient model is required for real-time applications. We present a novel approach to predict energy demand variation with a wide range of uncertain factors. A factor identification is carried out to recognize the range of variation in the operating conditions. A computationally efficient surrogate model is generated based on a previously developed numerical simulation model. The surrogate model is shown to be 10 000 times faster than the numerical model. The surrogate model output corresponds with the numerical model with less than 1% error. The energy demand of the surrogate model varied from 0.43 to 2.30 kWh/km, which is realistic in comparison to previous studies. Successful sensitivity analysis of the surrogate model revealed the most crucial factors. Uncertainty in temperature, rolling resistance and payload contributed most to the variation in energy demand. Variation in these factors should be taken into account when predicting energy consumption and while planning schedules for a bus network.

Effects of residence time on life cycle assessment of bioenergy production from dairy manure

This study simulated the effect of time on bioenergy production from dairy manure and associated variation in energy demand and GHG emission in Ohio and Hawaii. Increasing the residence-time (RT) decreases the bioenergy-production in a nonlinear-fashion, for both the states (Ohio and Hawaii). Using the main scenario in Hawaii, the highest bioenergy production for 30 days RT was 11.2 × 104 GJ. Life-cycle-assessment of produced bioenergy showed that energy requirement and GHG emission of the produced-bioenergy (GJ) varied from 0.65 to 0.67 and 28–35 kg CO2/GJ bioenergy respectively. Year-round bioenergy production through main scenario was more advantageous with respect to bioenergy production and GHG emission. Decreasing nitrogen concentration for algal biomass production increased the bioenergy production significantly with a reduced energy demand and marginally increased GHG emission. Hence, the LCA model predicted that running a biorefinery for short residence time, and using diluted waste, could provide bioenergy with reduced energy requirement and GHG-emission.

Mitochondrial Morphologies Driven by Energy-Consuming Cell Sites in a Spatially and Time-Resolved Quality Model.

Mitochondria are the energy plants of eukaryotic cells. Mitochondrial network morphologies are essential for the energy supply of eukaryotic cells. However, the associated dynamics are not yet fully understood. They behave as a dynamic network that adapts to the cell's environment and its energetic needs. Various processes such as mitochondrial fission and fusion, mitochondrial recycling, repair mechanisms, and oxidative stress influence the state of the mitochondrial network. Here, we introduce a novel time-dependent and spatially resolved quality model on mitochondrial morphology. The interplay between the mitochondrial network and energy-consuming cell sites is modeled by biophysical interactions of quality-dependent mitochondrial clusters in the presence of adenosine triphosphate (ATP) consumers represented by Mie potentials. Mitochondria are modeled as simplified ballistic particles that move within the cytoplasm of a virtual cell, and connect and divide by inelastic collisions. With this model, we investigate the coupling of mitochondrial dynamics with oscillating cell functions, representing diverse global states of the energetic architecture in the cell. Our simulations based on a generalized cell reveal a perinuclear condensation of mitochondria during phases of high-energy demand. Furthermore, quality-increasing mechanisms disclose the benefits of high mitochondrial masses. Simulations reveal that varying energy demands modeled by oscillations of ATP consumers alter the morphology of the network. Phases of high-energy consumption lead to interconnected network structures and perinuclear condensation of mitochondria. The model explains quality-increasing benefits of high mitochondrial masses.

Large scale complementary solar and wind energy sources coupled with pumped-storage hydroelectricity for Lower Silesia (Poland)

Photovoltaics and wind generation are currently perceived to be a viable option for reducing the environmental impact of energy sources while simultaneously showing significant potential to reduce dependence on conventional fuels and to increase local energy security. However, the stochastic and weather-driven nature of solar and wind energy, as well as varying energy demand, makes energy systems which are based on renewables unsuitable from the perspective of power supply reliability. As a solution, they can be integrated into the power system by, for instance, energy storage in the form of pumped-storage hydroelectricity or rapidly up- and down-ramping gas power plants.This paper introduces a mathematical model for simulating and optimising the operation of a large scale solar–wind hybrid coupled with pumped-storage on a district level considering a simplified approach to incorporate grid-related cost. The model assumes spatial and temporal variability of energy generation from photovoltaics and wind turbines as well as spatial distribution of energy demand. For the purpose of including grid-related cost, we introduce a local consumption index (LCI) as a measure for decentral generation and optimise the power system with respect to different CO2 prices. We show that the introduction of the LCI facilitates the increase of renewables shares and that PV and wind are already cost-competitive at low CO2 prices.

Techno-economic comparison of the levelised cost of electricity generation from solar PV and battery storage with solar PV and combustion of bio-crude using fast pyrolysis of biomass

The strong growth of intermittent electricity generation from solar PV and wind is leading to a greater need for energy storage at grid scale. In this work a techno-economic model has been constructed to calculate the levelised cost of electricity for two systems that can meet an arbitrary energy demand curve: (1) solar PV and battery storage and (2) solar PV with combustion of bio-crude and bio-gas from biomass. The analysis is performed for conditions prevalent in Queensland, Australia where over a gigawatt of new solar PV capacity is being constructed in 2018. The battery storage assumes lithium-ion batteries and costs derived from the recently constructed Hornsdale Power Reserve in South Australia. A variable energy demand curve is assumed in the work. The model shows that the parameters with the most impact on the LCOE for the solar PV and battery system are the solar yield, and total installed costs of the battery and solar PV unit. Assuming, battery costs of 750 AUD/kWh, solar PV costs of 1.6 AUD/W and a project capacity of 240 MWh/d, the LCOE of the solar PV and battery system was calculated to be 170 AUD/MWh. Using total installed costs forecast for the near future, the LCOE is expected to be in the range 150–185 AUD/W for the variable energy demand curve, and over 200 AUD/MWh if a constant supply of power is required. The parameters with the most impact on the LCOE for the solar PV and bio-crude system are the solar yield and total installed cost of the biomass pyrolysis and bio-crude combustion unit. For a 240 MWh/d project scale with variable energy demand, the LCOE for the solar PV and bio-crude system is estimated to be 116 AUD/MWh. Variations in feedstock cost and project scale showed that the LCOE is in the range of 104–125 AUD/MWh. The main conclusion from this work, is that integration of solar PV and the production and combustion of bio-crude and bio-gas using fast pyrolysis of biomass, leads to competitively priced dispatchable renewable energy that is forecast to be cheaper than using solar PV and batteries for the foreseeable future. It has also been found that the combination of solar PV and bio-crude combustion leads to lower LCOEs than using bioenergy alone, due to the rapidly decreasing costs of large scale solar PV. While the solar PV and bio-crude system analysed in this work will likely be a niche solution, in areas with substantial biomass resources, it offers a credible starting point for the development of larger scale bioenergy value chains, with the longer term goal of converting lignocellulosic biomass materials into renewable transportation fuels and chemicals.

Energy demand and the context-dependent effects of genetic interactions underlying metabolism

Genetic effects are often context dependent, with the same genotype differentially affecting phenotypes across environments, life stages, and sexes. We used an environmental manipulation designed to increase energy demand during development to investigate energy demand as a general physiological explanation for context‐dependent effects of mutations, particularly for those mutations that affect metabolism. We found that increasing the photoperiod during which Drosophila larvae are active during development phenocopies a temperature‐dependent developmental delay in a mitochondrial‐nuclear genotype with disrupted metabolism. This result indicates that the context‐dependent fitness effects of this genotype are not specific to the effects of temperature and may generally result from variation in energy demand. The effects of this genotype also differ across life stages and between the sexes. The mitochondrial‐nuclear genetic interaction disrupts metabolic rate in growing larvae, but not in adults, and compromises female, but not male, reproductive fitness. These patterns are consistent with a model where context‐dependent genotype‐phenotype relationships may generally arise from differences in energy demand experienced by individuals across environments, life stages, and sexes.

Design of Biomass Combined Heat and Power (CHP) Systems based on Economic Risk using Minimax Regret Criterion

It is a great challenge to identify optimum technologies for CHP systems that utilise biomass and convert it into heat and power. In this respect, industry decision makers are lacking in confidence to invest in biomass CHP due to economic risk from varying energy demand. This research work presents a linear programming systematic framework to design biomass CHP system based on potential loss of profit due to varying energy demand. Minimax Regret Criterion (MRC) approach was used to assess maximum regret between selections of the given biomass CHP design based on energy demand. Based on this, the model determined an optimal biomass CHP design with minimum regret in economic opportunity. As Feed-in Tariff (FiT) rates affects the revenue of the CHP plant, sensitivity analysis was then performed on FiT rates on the selection of biomass CHP design. Besides, design analysis on the trend of the optimum design selected by model was conducted. To demonstrate the proposed framework in this research, a case study was solved using the proposed approach. The case study focused on designing a biomass CHP system for a palm oil mill (POM) due to large energy potential of oil palm biomass in Malaysia.

Adoption of Smart Grid Framework Which Tracks Power Consumption of Household Appliances in Zimbabwe

The aim of this study was to design a smart grid framework which tracks power consumption of household appliances. The study explored technology being used with current traditional power grid to transmit and distribute electricity from power generation sources until power reaches the final consumer. The challenges and problems of the current traditional grid were unearthed in this study to bring best solution to mitigate the current power shortage problems. Smart grid model which supports real-time electricity power usage monitoring and controlling capability is proposed as an alternative measure to control unnecessary power usage for domestic and commercial purposes. Real-time power usage monitoring, distributed power generation, varying energy demand, remote detection and faults identification, and bi-directional communication are cross-cutting technology and drivers of the implementation of smart grid. The researchers recommended energy authorities to engage with power customers to craft a policy that govern remote monitoring and controlling of electrical appliances’ power consumption to ensure complete adoption of smart grid.

Neuronal Stimulation Triggers Neuronal Glycolysis and Not Lactate Uptake

Proper brain function requires a substantial energy supply, up to 20% of whole-body energy in humans, and brain activation produces large dynamic variations in energy demand. While local increases in cerebral blood flow are well known, the cellular responses to energy demand are controversial. During brain excitation, glycolysis of glucose to lactate temporarily exceeds the rate of mitochondrial fuel oxidation; although the increased energy demand occurs mainly within neurons, some have suggested this glycolysis occurs mainly in astrocytes, which then shuttle lactate to neurons as their primary fuel. Using metabolic biosensors in acute hippocampal slices and brains of awake mice, we find that neuronal metabolic responses to stimulation do not depend on astrocytic stimulation by glutamate release, nor do they require neuronal uptake of lactate; instead they reflect increased direct glucose consumption by neurons. Neuronal glycolysis temporarily outstrips oxidative metabolism, and provides a rapid response to increased energy demand.

Dynamic analysis of the dual-loop Organic Rankine Cycle for waste heat recovery of a natural gas engine

Natural gas internal combustion engines for electric generating are important primary movers in distributed energy systems. However, more than half of the energy is wasted by exhaust, jacket water and so on. Therefore, it is very meaningful to recover the waste heat, especially the exhaust heat. The DORC (Double loop ORC) is regarded as a suitable way to recover exhaust heat and it can produce electric required by users all the year around. As the waste heat recovery system of the engine, it often works under different working conditions owing to the varying energy demand of users. However, there is few study on the part-load performance of the DORC under different working conditions. Consequently, the dynamic math model of the DORC for waste heat recovery of a natural gas engine with 1000kW rated power is established by Simulink in this work. With the PID control of the system, the static performance and dynamic behavior of the DORC under five typical engine working conditions are simulated and analyzed. Besides, the effects of the mass flow rate of the HT (high temperature) cooling water which is the connection between the two loops on the DORC performance are researched as well. The results illustrate that the DORC can improve the efficiency of the combined system quite well from 100% to 60% engine working condition, showing good working condition adaptability. Besides, enlarging the mass flow rate of the HT cooling water can enhance the output power of the DORC system, but not very obviously.

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP) is proposed and has been verified for a test case of building microgrids (BMGs). Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP) generators. In addition, the BMGs are also connected to an external energy network (EEN), which contains a large CHP, an adsorption chiller (ADC), a thermal storage tank, and an electric heat pump (EHP). By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter) of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

Multi-Linear Probabilistic Energy Flow Analysis of Integrated Electrical and Natural-Gas Systems

The deep interdependence between electrical and gas systems entails a potential threat to the security (or reliability) of both systems. It is imperative to investigate the impacts of massive uncertainties on the overall secure and economical operation of both systems. In this paper, a probabilistic energy flow framework of integrated electrical and gas systems is initially proposed considering correlated varying energy demands and wind power. Three aspects of couplings between electrical and gas systems are considered: gas-fired generators, electric-driven compressors, and energy hubs integrated with power to gas (P2G) units. Furthermore, a multilinear method is specially designed to produce a deterministic energy flow solution for each sample generated by Monte Carlo simulation (MCS). Finally, test results have verified that the proposed multilinear MCS method prevails over the nonlinear MCS. In addition, P2G effectively benefits the operation of both electrical and gas networks.

Multi-objective design optimization of distributed energy systems through cost and exergy assessments

In recent years, distributed energy systems (DESs) have been recognized as a promising option for sustainable development of future energy systems, and their application has increased rapidly with supportive policies and financial incentives. With growing concerns on global warming and depletion of fossil fuels, design optimization of DESs through economic assessments for short-run benefits only is not sufficient, while application of exergy principles can improve the efficiency in energy resource use for long-run sustainability of energy supply. The innovation of this paper is to investigate exergy in DES design to attain rational use of energy resources including renewables by considering energy qualities of supply and demand. By using low-temperature sources for low-quality thermal demand, the waste of high-quality energy can be reduced, and the overall exergy efficiency can be increased. The goal of the design optimization problem is to determine types, numbers and sizes of energy devices in DESs to reduce the total annual cost and increase the overall exergy efficiency. Based on a pre-established DES superstructure with multiple energy devices such as combined heat and power and PV, a multi-objective linear problem is formulated. In modeling of energy devices, the novelty is that the entire available size ranges and the variation of their efficiencies, capital and operation and maintenance costs with sizes are considered. The operation of energy devices is modeled based on previous work on DES operation optimization. By minimizing a weighted sum of the total annual cost and primary exergy input, the problem is solved by branch-and-cut. Numerical results show that the Pareto frontier provides good balancing solutions for planners based on economic and sustainability priorities. The total annual cost and primary exergy input of DESs with optimized configurations are reduced by 21–36% as compared with conventional energy supply systems, where grid power is used for the electricity demand, and gas-fired boilers and electric chillers fed by grid power for thermal demand. A sensitivity analysis is also carried out to analyze the influence of energy prices and energy demand variation on the optimized DES configurations.

Enhanced oxidative capacity of ground squirrel brain mitochondria during hibernation.

During hibernation, thirteen-lined ground squirrels (Ictidomys tridecemlineatus) regularly cycle between bouts of torpor and interbout arousal (IBA). Most of the brain is electrically quiescent during torpor but regains activity quickly upon arousal to IBA, resulting in extreme oscillations in energy demand during hibernation. We predicted increased functional capacity of brain mitochondria during hibernation compared with spring to accommodate the variable energy demands of hibernation. To address this hypothesis, we examined mitochondrial bioenergetics in the ground squirrel brain across three time points: spring (SP), torpor (TOR), and IBA. Respiration rates of isolated brain mitochondria through complex I of the electron transport chain were more than twofold higher in TOR and IBA than in SP (P < 0.05). We also found a 10% increase in membrane potential between hibernation and spring (P < 0.05), and that proton leak was lower in TOR and IBA than in SP. Finally, there was a 30% increase in calcium loading in SP brain mitochondria compared with TOR and IBA (P < 0.01). To analyze brain mitochondrial abundance between spring and hibernation, we measured the ratio of copy number in a mitochondrial gene (ND1) vs. a nuclear gene (B2M) in frozen cerebral cortex samples. No significant differences were observed in DNA copies between SP and IBA. These data show that brain mitochondrial bioenergetics are not static across the year and suggest that brain mitochondria function more effectively during the hibernation season, allowing for rapid production of energy to meet demand when extreme physiological changes are occurring.

A hybrid-learning based broker model for strategic power trading in smart grid markets

Smart Grid markets are dynamic and complex, and brokers are widely introduced to better manage the markets. However, brokers face great challenges, including the varying energy demands of consumers, the changing prices in the markets, and the competitions between each other. This paper proposes an intelligent broker model based on hybrid learning (including unsupervised, supervised and reinforcement learning), which generates smart trading strategies to adapt to the dynamics and complexity of Smart Grid markets. The proposed broker model comprises three interconnected modules. Customer demand prediction module predicts short-term demands of various consumers with a data-driven method. Wholesale market module employs a Markov Decision Process for the one-day-ahead power auction based on the predicted demand. Retail market module introduces independent reinforcement learning processes to optimize prices for different types of consumers to compete with other brokers in the retail market. We evaluate the proposed broker model on Power TAC platform. The experimental results show that our broker is not only is competitive in making profit, but also maintains a good supply-demand balance. In addition, we also discover two empirical laws in the competitive power market environment, which are: 1. profit margin shrinks when there are fierce competitions in markets; 2. the imbalance rate of supply demand increases when the market environment is more competitive.

A generalised model of electrical energy demand from small household appliances

Accurate forecasting of residential energy loads is highly influenced by the use of electrical appliances, which not only affect electrical energy use but also internal heat gains, which in turn affects thermal energy use. It is therefore important to accurately understand the characteristics of appliance use and to embed this understanding into predictive models to support load forecast and building design decisions. Bottom-up techniques that account for the variability in socio-demographic characteristics of the occupants and their behaviour patterns constitute a powerful tool to this end, and are potentially able to inform the design of Demand Side Management strategies in homes.To this end, this paper presents a comparison of alternative strategies to stochastically model the temporal energy use of low-load appliances (meaning those whose annual energy share is individually small but significant when considered as a group). In particular, discrete-time Markov processes and survival analysis have been explored. Rigorous mathematical procedures, including cluster analysis, have been employed to identify a parsimonious strategy for the modelling of variations in energy demand over time of the four principle categories of small appliances: audio-visual, computing, kitchen and other small appliances. From this it is concluded that a model of the duration for which appliances survive in discrete states expressed as bins in fraction of maximum power demand performs best. This general solution may be integrated with relative ease with dynamic simulation programs, to complement existing models of relatively large load appliances for the comprehensive simulation of household appliance use.

Reprint of Feasible path toward 40–100% renewable energy shares for power supply in France by 2050: A prospective analysis

This paper explores the conditions under which renewable energy sources (RES) penetration could jeopardize power system reliability, as well as which flexibility options could help integrate high levels of RES. For this purpose, we used an energy-planning model from the TIMES family, which provides a realistic representation of power systems and plausible options for their long-term development, completed by a thermodynamic description of power systems to assess their reliability. We applied this model to the case of France and built contrasted scenarios, from 0% to 100% renewable energy penetration by 2050. We also tested different assumptions on Variable Renewable Energy (VRE) production, imports, demand flexibility and biomass potential. We show that high renewable energy penetration would need significant investments in new capacities, new flexibility options along with imports and demand-response, and that it is likely to deteriorate power system reliability if no technologies dedicated to this issue are installed.

Performance Evaluation of the ZIP Model-Phaselet Frame Approach for Identifying Appliances in Residential Loads

This paper presents the analysis and development of a new approach to monitor and update the on–off status of appliances in residential loads (RSLs). The proposed approach is structured to employ power meter readings $P$ to determine the values for the magnitude $\vert \bar{S}\vert $ and phase $\theta$ of the apparent power. The value of $\vert \bar{S}\vert $ , associated with a value of $P$ , is determined using Newton iterations, where a value of $\theta$ is calculated using six phaselet frames during each iteration. Once the iterations converge, the values of $P$ and $\theta$ are used to construct the ZIP model (polynomial model) for the RSL, from which $P$ is provided. The constructed ZIP model provides the values for the constants $K_{pf}$ and $K_{qf}$ that relate the change in frequency to the active and reactive power demands of the modeled load. The obtained values of $K_{pf}$ and $K_{qf}$ are compared to standardized values that are defined for each appliance in an RSL. The ZIP model-phaselet frame approach is implemented as an algorithm for monitoring the on–off status of appliances in RSLs. The algorithm for the proposed approach is developed without a need to collect data for training. Test results show simple implementation, good accuracy, and insensitivity to variations in energy demands.