Realistic Energy Research Articles

Incorporating environmental variability in a spatially-explicit individual-based model of European sea bass✰

The northern stock of European sea bass (Dicentrarchus labrax) is a large, high value, slow growing and late maturing fish that is an important target species for both commercial and recreational fisheries. Around the UK, scientific assessments have shown a rapid eight-year decline in spawning stock biomass since 2010 attributed to poor recruitment; this was likely driven by environmental factors and high fishing mortality. Management of the stock is informed by scientific assessments in which a population model is fitted to the available data and used to forecast the possible consequences of various catch options. However, the model currently used cannot represent the spatial distribution of the stock or any effects of environmental variability. One approach that may be used to represent the effects of spatial and temporal variation in environmental drivers is with Individual based models (IBMs). In IBMs populations are represented by their constituent individuals that interact with their environment and each other. The mechanistic nature of IBMs is often advantageous as a management tool for complex systems including fisheries. Here we add to an existing IBM to produce a spatio-temporally explicit IBM of the northern stock of sea bass in which individual fish respond to local food supply and sea surface temperature. All life stages (i.e., pelagic stages, juvenile and mature fish) are modelled and individual fish have their own realistic energy budgets driven by observed dynamic maps of phytoplankton density and sea surface temperature. The model is calibrated using Approximate Bayesian Computation (ABC). After calibration by ABC the model gives good fits to key population parameters including spawning stock biomass. The model provides a mechanistic link between observed local food supplies and sea surface temperatures and overall population dynamics. Plots of spatial biomass distribution show how the model uses the energy budget to predict spatial and temporal change in sea bass biomass distribution in response to environmental variability. Our results indicate that the IBM is a promising approach that could be used to support stock assessment with scope for testing a range of spatially and temporally explicit management scenarios in addition to testing stock responses to novel environmental change.

Quaternary Organic Solar Cells Enable Suppressed Energy Loss

Multi‐component organic solar cells (OSCs) composing of more than two donor and acceptor materials have attracted increasing attention, due to the possibilities to further mitigate voltage loss (ΔVoc) for the gain of open‐circuit voltage (Voc). However, the control of phase morphology in multi‐component blend systems that critically impacts ΔVoc and the ultimate power conversion efficiency (PCE) is still a challenge. Here, we report a quaternary blend‐based strategy for non‐fullerene OSCs by using two polymer donors (PM6 and L20) along with acceptors of a non‐fullerene (Y6) and PC71BM, leading to concurrent improvements of the Voc, device fill factor and eventual PCE. The quaternary OSC exhibits the advantages of a higher charge collection efficiency, expedited charge carrier sweep‐out, and reduced charge recombination losses. The suppression on ΔVoc is attributed to the reduced radiative recombination loss below the bandgap (0.143 V) and non‐radiative voltage loss (0.216 V). These properties are linked to synergies of modified energetics and film morphology of the quaternary blends. This work demonstrates that incorporating suitable donor and acceptor guest molecules to organic binary blend systems is a highly viable approach for lowering the energy loss in organic bulk heterojunctions towards the boost of photovoltaic performance for realistic energy conversion applications.

A Matheuristic for the Electric Vehicle Routing Problem with Time Windows and a Realistic Energy Consumption Model

A Matheuristic for the Electric Vehicle Routing Problem with Time Windows and a Realistic Energy Consumption Model

Potential Energy Saving of V2V-Connected Vehicles in Large-Scale Traffic

Most studies evaluating the energy efficiency of connected and automated vehicles (CAVs) in car-following scenarios have considered a few preceding vehicles communicating with the controlled CAVs. However, considering rapidly evolving technologies in CAVs, extended vehicle-to-vehicle (V2V) connectivity over large-scale traffic needs to be considered in estimating CAVs’ energy benefits. This paper investigates the potential energy saving of V2V-connected vehicles in large-scale downstream traffic by adopting a human driver model generating stable car-following trajectories for many consecutive vehicles. The energy-efficient driving of a CAV is demonstrated based on an optimal controller minimizing the longitudinal acceleration by forecasting an immediately preceding vehicle's trajectory over a fixed prediction horizon. Various traffic scenarios are considered by applying different simulation parameters, including the distribution of vehicle time gaps, the number of connected vehicles, and prediction horizon lengths. Furthermore, a comprehensive analysis is conducted to discover the relationships between the parameters of interest and system performance, including prediction and control. Our findings from the parameter study are validated by evaluating the realistic energy consumption of a vehicle in a simulation platform operating high-fidelity powertrain models.

Studies of Molecular Dynamics and Electronic Structure in Cubic-SiC by Using Density Functional Tight Binding Approach

The radiation damage in Silicon-Carbide and its result are presented in this research. The Density Functional Tight Binding (DFTB) approach is used to perform molecular dynamics simulations to implement the DFTB+ code (Elstner et al., 1998). This methodology shows the making and breaking of chemical bonding as well as describes the realistic total energy for the larger systems. Repulsive potentials have been developed to prevent the atoms to stay close to each other during the model of high energy collisions also correctly describe the configurations during the atomic separation within the typical range. The extent and nature of damages are characterized within the collision event up to 10KeV. The band structure of SiC has been studied using minimal basis (sp) as applied in DFTB+. The value of band gap shows that cubic SiC is a large band gap semiconductor material. This value is comparable with the given in literature. Density of states is also calculated by using the tight binding approach. The peaks of spectrum have been compared with the experimental values found in literature.

Forecasting nonadiabatic dynamics using hybrid convolutional neural network/long short-term memory network.

Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging, especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network/long short-term memory (CNN-LSTM) framework for predicting the long-time quantum behavior, given only the short-time dynamics. This scheme takes advantage of both the powerful local feature extraction ability of CNN and the long-term global sequential pattern recognition ability of LSTM. With feature fusion of individually trained CNN-LSTM models for the quantum population and coherence dynamics, the proposed scheme is shown to have high accuracy and robustness in predicting the linearized semiclassical and symmetrical quasiclassical mapping dynamics as well as the mixed quantum-classical Liouville dynamics of various spin-boson models with learning time up to 0.3ps. Furthermore, if the hybrid network has learned the dynamics of a system, this knowledge is transferable that could significantly enhance the accuracy in predicting the dynamics of a similar system. The hybrid CNN-LSTM network is thus believed to have high predictive power in forecasting the nonadiabatic dynamics in realistic charge and energy transfer processes in photoinduced energy conversion.

Realistic energy deposition and temperature heating in molecular clouds due to cosmic rays: a computation simulation with the GEANT4 code employing light particles and medium-mass and heavy ions

ABSTRACT In the interstellar medium, Galactic and extragalactic cosmic rays (CRs) penetrate deeper in the molecular clouds (MCs) and promote inside several physical and physicochemical changes due to the energy deposition, including gas and grain heating, and triggering also molecular destruction and formation. In this work, in an attempt to simulate, in a more realistic way, the energy delivered by CRs in a typical MC (mass ∼5400 M☉ and size ∼106 au; mainly composed of H atoms), we combine the energy deposition of light particles and heavy ions, with the new calculations considering the medium-mass ions (3 ≤ Z ≤ 11). To execute the calculation, the Monte Carlo toolkit GEANT4 was applied to get the energy deposition rate per mass from many kinds of secondary particles, used in nuclear and hadron physics. The energy deposition by its induced cascade shower within the MC was characterized, as well as the relative energy deposition for all members of the medium-mass group. The results show that the incoming protons are the dominant source in the energy deposition and heating of the cloud, followed by alphas and electrons, with the medium-mass-ion and heavy-ion groups each contributing roughly 8 per cent. The current model also shows a temperature enhancement of up to 10 per cent in the external layers of the cloud (reaching 22.5 K) with respect to the previous calculations where only light particles were considered. However, neither heavy nor medium-mass ions contribute to the temperature enhancement in the deep core of the cloud.

Arranging university semester date to minimize annual CO2 emission: A UK university case study

SummaryExisting methods of reducing carbon emissions on campus often require substantial investment, and the potential opportunities for carbon dioxide and energy savings in universities with existing infrastructure have not been considered in much detail. This work fills this gap by considering an indirect and soft demand response strategy, i.e., semester arrangement. To identify the optimal operational strategy of a realistic campus-level multi vector energy system (MES) in Scotland based on CO2 emissions, an original tool is presented. Two conclusions can be drawn safely from the case study. Firstly, changing the operational mode of the university could significantly reduce CO2 emissions. Secondly, considering the difference between average emission factor (AEF) and marginal emission factor (MEF) in the power grid, the different operational modes will bring different electricity/heat demands and also affect carbon emissions. The work opens up a new perspective for worldwide university operators who are considering reducing CO2 emissions.

High Capacitive Performance Achieved in NaNbO3‐Based Ceramics via Grain Refinement and Relaxation Enhancement

Lead‐free antiferroelectric (AFE) energy storage material NaNbO3 has received increasing attention because of the advantages of low density, low cost, and environmental friendliness. Nevertheless, square P–E hysteresis loops and low electric breakdown strength suppress the realistic energy storage applications of the pristine NaNbO3 ceramic. Herein, the Ba0.6Sr0.4TiO3 relaxor ferroelectric is introduced into the NaNbO3 AFE ceramic, aiming to enhance energy storage performances. (1–x)NaNbO3–xBa0.6Sr0.4TiO3 ceramics with different doping levels are manufactured via the conventional solid‐state processing route. A remarkable recoverable energy storage density (W rec ≈ 3.04 J cm−3) and a high efficiency (η ≈ 88.83%) are achieved synchronously in the ceramic with x = 0.20, under an applied electric field of 320 kV cm−1, benefiting from the enhanced relaxor behavior and reduced grain size caused by the introduction of Ba0.6Sr0.4TiO3. Furthermore, excellent frequency stability (5–1000 Hz) and temperature stability (20–150 °C) along with outstanding fatigue resistance (up to 2 × 105cycles) are also achieved. Moreover, the ceramic displays outstanding charge–discharge capabilities such as big current density (C D ≈ 1096.23 A cm−2), high power density (P D ≈ 106.7 MV cm−3) and fast discharge speed (t 0.9 ≈ 142 ns). These results demonstrate that the Ba0.6Sr0.4TiO3 modified NaNbO3‐based ceramics could be competitive candidates for high power systems.

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

PurposeThe current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters.Design/methodology/approachA novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy.FindingsThe proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time.Originality/valueThis paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

Carbon_in_WaterDSS: A new tool to determine GHG emissions in quasi-real time for irrigation systems

Climate change is a reality, and irrigated agriculture is a significant human activity that contributes to greenhouse gas (GHG) emissions and, therefore, to global warming. Traditionally, the carbon footprint associated with GHG emissions has been computed by fixed electrical energy transformation rates (kgCO2e kWh−1). However, this transformation rate is not constant because it depends on a country's power generation mix, which combines of the energy sources supplying a country (coal, nuclear, wind, photovoltaic, hydraulic, natural gas, etc.). Fixed transformation rates have been traditionally employed to estimate the carbon footprint that derives from irrigated agricultural activity. Nevertheless, these fixed rates can be inaccurate for not considering variations over time. Thus, in this work, a new decision support system called Carbon_in_WaterDSS was developed in Python and applied to a real-world scenario to determine a dynamic and realistic energy transformation rate and to compute an accurate carbon footprint. Different time periods were considered according to irrigation stakeholders' water management criteria, crop water requirements, energy costs and pricing electricity tariff periods. The results show that the energy transformation rate and, therefore, the carbon footprint value vastly vary during the irrigation season and over for a day (from 0.066 kgCO2e kWh−1 to 0.490 kgCO2e kWh−1), unlike the values found in other works. This work also highlights how farmers choose the most economical energy periods to use conventional electricity. However, this hourly choice criterion is not the option that generates the lowest carbon footprint value in most analysed years.

A probabilistic approach to include the overall efficiency of gas-fired heating systems in urban building energy modelling

Urban building energy modelling has an essential role in the estimation of energy demand at urban or neighbourhood scales. However, current modelling methods have limitations in reproducing realistic gross energy usage. Although it is theoretically possible to simulate all components of the heating system in detail, such an extensive approach significantly increases the computational effort, prohibiting a large scale probabilistic analysis. As an alternative, this paper presents a simplified data-driven approach to estimate the overall efficiency for the six most occurring gas-fired heating system configurations in Flemish single-family dwellings. For all configurations, efficiencies of emission, distribution, production, control and storage components are taken into account, of which the efficiency of the production unit is modelled most in detail as it includes the load-dependency. The approach is applied to a sample of 20 dwellings reflecting realistic variation in size, insulation quality and occupancy schedules. For all dwellings and the different heating systems the resulting annual production efficiency, and monthly heating systems’ efficiency as a function of gross energy demand are shown based on the 25th, 50th and 75th percentile.

Numerical assessment of self-sufficiency of residential buildings in Belgium by using heat pumps, photovoltaic panels and energy storages

Residential buildings claim a significant share of the total energy use worldwide. In order to have more realistic energy performance predictions, increased attention is paid to the analysis of the building’s energy use through comprehensive, transient detailed numerical simulations. In this article, the self-consumption and self-sufficiency values of three detached residential buildings are assessed through numerical models made in the programming language Modelica and software tool Dymola. The three buildings have the same structure and different space heating energy demands of 15 kWh/m2year, 30 kWh/m2year and 45 kWh/m2year. The energy use of the buildings coincides with the occupancy profile where domestic hot water use dominates over the space heating demand provided by an air to water heat pump. The discrepancy between renewable energy production and energy consumption is mitigated by means of thermal load shifting and electrical energy storage. In this research, the self-consumption and self-sufficiency of the studied buildings have been analysed as a function of the economically favourable energy storage sizing. For the use of an electrical battery with the installed capacity of 2.5 kWh and thermal energy storage of 250 l, the self-sufficiency results to be 40%, 38.5% and 37% for the three buildings respectively at the specific simulated energy demand conditions.

Backscatter-Assisted Wireless Powered Communication Networks Empowered by Intelligent Reflecting Surface

Intelligent reflecting surface (IRS) has recently been emerged as an effective way for improving the performance of wireless networks by reconfiguring the propagation environment through a large number of passive reflecting elements. This game-changing technology is especially important for stepping into the Internet of Everything (IoE) era, where high performance is demanded with very limited available resources. In this paper, we study a backscatter-assisted wireless powered communication network (BS-WPCN), in which a number of energy-constrained users, powered by a power station (PS), transmit information to an access point (AP) via backscatter and active wireless information transfer, with their communication being aided by an IRS. Using a practical energy harvesting (EH) model which is able to capture the characteristics of realistic energy harvesters, we investigate the maximization of total network throughput. Specifically, IRS reflection coefficients, PS transmit and AP receive beamforming vectors, power and time allocation are designed through a two-stage algorithm, assuming minimum mean square error (MMSE) receiver at the AP. The effectiveness of the proposed algorithm is confirmed via extensive numerical simulations. We also show that our proposed scheme is readily applicable to practical IRS-aided networks with discrete phase shift values.

A new optimization strategy for wind/diesel/battery hybrid energy system

HOMER software is a powerful tool for modeling and optimization of hybrid energy system (HES). The main two default control strategies in HOMER are load following (LF) and cycle charging (CC) strategies. In these strategies, the decision to use the generator or battery at each time step is made based on the lowest-cost choice. Therefore, these strategies are difficult to be implemented in practice especially in countries with continuous fuel price fluctuations. In this study, a new dispatch strategy based on HOMER-MATLAB Link Controller for an isolated wind/diesel/battery HES is proposed to overcome the limitations of the default HOMER strategies. A detailed technical, economic, and greenhouse gas emission analysis is presented for the system under LF, CC, and the proposed dispatch strategies. Besides offering more realistic optimization, the results show that the proposed strategy offers the best economic and environmental performance with a net present cost of $56473 and annual CO2 emissions of 6838 kg. Furthermore, the sensitivity analysis reveals that the proposed strategy is not affected by the fuel price variation, in opposite to LF, and CC strategies which is affected dramatically by this variation. The findings are of paramount importance towards more realistic and efficient energy management strategies.

Distribution of natural beta dose to individual grains in sediments

In luminescence dating using single grains of quartz, statistical protocols are used to compute the most appropriate dose from the distribution of palaeodoses. The distribution of palaeodoses arises due to, (1) heterogeneity (at grain level) in bleaching at the time of deposition, (2) heterogeneous distribution of beta emitters present as randomly distributed feldspars (Mayya et al. in Radiat Meas 41:1032–1039, 2006) and, (3) heterogeneous distribution of grainsizes (Guérin et al. in Radiat Meas 47:778–785, 2015). Mayya et al. (Radiat Meas 41:1032–1039, 2006) demonstrated that random distribution of feldspar grains(with up to 14% stoichiometric K) and shorter range of beta particles (~ 2.3 mm in quartz) lead to significant variation in dose received by individual grains of quartz. This study improves upon Mayya et al. (Radiat Meas 41:1032–1039, 2006) by, (1) using a more realistic energy deposition function that was estimated using Monte Carlo simulations and (2) computing the effects of porosity of the sediment and beta straggling on the dose distribution function. It additionally concludes that effects of beta straggling are small and can be ignored. Ages based on new calculations led to improved concordance with control ages.

Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network

Energy benchmarking of buildings has an important role in improving energy performance by establishing a reference for the energy efficiency of the building stock. The simulation of archetypes followed by a generalisation model has been widely used to obtain benchmarks. However, even though archetypes summarise the main features of the building stock, the uncertainties must be accounted for in the modelling process. Moreover, testing the response of the benchmarking model using the actual building stock data supports the reliability of the method. This paper aims to propose an innovative framework to reduce the uncertainty of archetypes for benchmarking buildings. A standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes. An Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings. Also, the simulation outcomes were used to determine energy end-uses according to the climatic zones. The framework proposition is presented alongside with a practical application. The result is an unprecedented benchmarking model: the archetype considers more variation in parameters of the building stock with higher uncertainties. Additionally, the modelling process showed to be robust for combining different datasets, and the ANN achieved high-performance metrics. Conclusion indicates the potential of using the framework for other typologies. Moreover, the framework demonstration is used for a school stock in Brazil, showing a trend to the inefficiency while a specific case study was explored, showing the potential of the method to find faults in the building energy use.

Second class currents, axial mass, and nuclear effects in hyperon production

We study the properties of the Cabibbo suppressed quasielastic production of $\Lambda$ and $\Sigma$ hyperons in antineutrino interactions with nuclei, including the effects of modified form factor axial mass, the second class current and SU(3) flavour violations. The hyperon and nucleon are subjected to the nuclear potential and the outgoing hyperon can undergo final state interactions. The hyperon potential has a significant effect on their production through absorption. We predict a significant enhancement of $\Lambda$ production compared with other hyperon production channels through $\Sigma \to \Lambda$ conversions. We produce predictions for several experiments by combining realistic neutrino energy distributions with suitable nuclear targets.

Learning the structure of wind: A data-driven nonlocal turbulence model for the atmospheric boundary layer

We develop a novel data-driven approach to modeling the atmospheric boundary layer. This approach leads to a nonlocal, anisotropic synthetic turbulence model which we refer to as the deep rapid distortion (DRD) model. Our approach relies on an operator regression problem that characterizes the best fitting candidate in a general family of nonlocal covariance kernels parameterized in part by a neural network. This family of covariance kernels is expressed in Fourier space and is obtained from approximate solutions to the Navier–Stokes equations at very high Reynolds numbers. Each member of the family incorporates important physical properties such as mass conservation and a realistic energy cascade. The DRD model can be calibrated with noisy data from field experiments. After calibration, the model can be used to generate synthetic turbulent velocity fields. To this end, we provide a new numerical method based on domain decomposition which delivers scalable, memory-efficient turbulence generation with the DRD model as well as others. We demonstrate the robustness of our approach with both filtered and noisy data coming from the 1968 Air Force Cambridge Research Laboratory Kansas experiments. Using these data, we witness exceptional accuracy with the DRD model, especially when compared to the International Electrotechnical Commission standard.

Energy-Efficient Neural Network Inference with Microcavity Exciton Polaritons

We propose all-optical neural networks characterized by very high energy efficiency and performance density of inference. We argue that the use of microcavity exciton polaritons allows one to take advantage of the properties of both photons and electrons in a seamless manner. This results in strong optical nonlinearity without the use of optoelectronic conversion. We propose a design of a realistic neural network and estimate energy cost to be at the level of attojoules per bit, also when including the optoelectronic conversion at the input and output of the network, several orders of magnitude below state-of-the-art hardware implementations. We propose two kinds of nonlinear binarized nodes based either on optical phase shifts and interferometry or on polariton spin rotations.