Aggregate Energy Research Articles

Antarctic Shipborne Tourism: Carbon Emission and Mitigation Path

The rapidly increasing concentrations of carbon dioxide lead to a threat of global climate change. As one of the major sources of carbon emissions in the Antarctic region, shipborne tourism in the Antarctic is expanding rapidly. Consequently, the carbon emissions of shipborne tourism in Antarctica are rapidly increasing. However, there is not enough attention being paid to this issue. In this paper, a calculation model was established to calculate the carbon emissions of cruise ships from the 2003/04 season to the 2016/17 season. The evolution of the carbon emissions from Antarctic cruise ships was described. An aggregate energy efficiency index was developed to evaluate the energy efficiency performance of Antarctic vessels. The key drivers were analyzed to find the paths of carbon reduction. The results show that: (1) The emissions per passenger-trip did not show a downward trend. The total carbon emissions increased continuously with the increasing number of tourists. The total carbon emissions in the 2016/17 season was double that in the 2003/04 season. (2) The aggregate energy efficiency index of Antarctic tour vessels has not reached an advanced level yet. It is the main reason for the high value of emissions per passenger-trip. (3) Due to the oversupply of Antarctica tour ships, there is a low rate of occupancy, which accounts for the decline in aggregate energy efficiency from the 2014/15 season to the 2016/17 season. This study suggests that the administrators of Antarctica should strengthen supervision of the tourism market, control the rapid growth in the number of cruise ships, improve the aggregate energy efficiency of cruise ships and form an efficient and green Antarctic tourism management system.

Contributors and drivers of Chinese energy use and intensity from regional and demand perspectives, 2012-2015-2017

China has been experiencing significant energy use transition accompanied by emphasizing on dual control of energy (e.g. energy consumption and energy intensity). Energy use was expected to present obvious spatial characteristics and differentiated contributions across regions. A systematic energy analysis was carried out to 31 regions (including Tibet) in China for investigating the contributors to national achievements and key drivers of regional energy use. With the help of multi-region input-output model (MRIO) and structural decomposition analysis (SDA) techniques, we found that the aggregate energy use in China has been advanced effectively with a declined increasing total energy consumption and accelerated decreasing total aggregate energy intensity. From regional perspective, Eastern region contributed the most characterized by greater total embodied energy consumption and lower aggregate embodied energy intensity, such as Shandong, Hebei and Jiangsu. From demand perspective, regional energy use has been dramatically determined by investment, consumption and exports in sequence. The changes in regional energy use have been tremendously influenced by the sectoral energy intensity and domestic production structure with differentiated performance across regions. According to additive and multiplicative SDA results, two different regional energy conservation paths, i.e. efficiency- and structure-oriented improvements, were identified for China.

Fluorescence Quenching of Carboxy-Substituted Phthalocyanines Conjugated with Nanoparticles under High Stoichiometric Ratios

Background: The search of the approaches towards a photosensitizer’s conjugation with multifunctional nanoparticles is an important step in the development of photodynamic therapy techniques. Association of photosensitizer molecules with nanoparticles that perform the delivery function can lead to a change in the functional state of the photosensitizer. Methods: We studied the effects observed upon incorporation of octa- and hexadeca-carboxyphthalocyanines of zinc(II) and aluminum(III) (Pcs) into the polymer shell of nanoparticles with a semiconductor CdSe/CdS/ZnS core with various spectral and optical methods. Results: First, the interaction of Pc with the polymer shell leads to a change in the spectral properties of Pc; the effect strongly depends on the structure of the Pc molecule (number of carboxyl groups as well as the nature of the central cation in the macrocycle). Secondly, upon incorporation of several Pc molecules, concentration effects become significant, leading to Pc aggregation and/or nonradiative energy transfer between neighboring Pc molecules within a single nanoparticle. Conclusions: These processes lead to the decrease of a number of the Pc molecules in an excited state. Such effects should be taken into account during the development of multifunctional platforms for the delivery of photosensitizers, including the use of nanoparticles as enhancers of photosensitizer activity by energy transfer.

Modeling Micellar Growth and Branching in Mixtures of Zwitterionic with Ionic Surfactants.

Zwitterionic surfactants are widely applied as drag-reducing or thickening agents because their aggregation patterns may drastically change in response to variations of the system composition or external stimuli, which provides controllable viscoelasticity. For predicting aggregation behavior of surfactant mixtures, classical molecular thermodynamic models have been widely used. Particularly, the results of modeling have been reported for zwitterionic/ionic surfactant mixtures. However, for solutions containing a zwitterionic surfactant, no molecular thermodynamic model has been proposed for a micellar branch. In this work we extend the classical molecular thermodynamic aggregation model to describe aggregation in the aqueous mixtures that contain a zwitterionic and an ionic surfactant. We derive analytical expressions (1) for the contribution of dipoles to the electrostatic term of the standard free energy of aggregation into micellar branches and (2) for the dipolar contribution to the persistence length of wormlike micelles. The dependence of micellar branching on the surfactant concentration is taken into account by including the population of micellar branches in the material balance equations. This model is applied to predict aggregation equilibrium in aqueous salt solutions of betaine (oleoylamidopropyl-N,N-dimethylbetaine) mixed with sodium dodecyl sulfate (SDS) and the longer tail sodium n-alkyl sulfates. We discuss the predicted properties of the aggregates and micellar networks and compare our predictions with available experimental data.

Nuclear energy consumption and energy-driven growth nexus: a system GMM analysis of 27 nuclear utilizing countries across the globe.

While the general environmental quality level continues to decline in today's global economy, aggregate energy consumption levels are often linked to countries' economic growth and environmental performances, thereby overlooking the specific roles of individual energy types. Thus, this study focuses on examining nuclear energy consumption-growth nexus in 27 selected nuclear energy-consuming countries across the globe. The system GMM estimator was applied to available post-2008 global financial crisis data spanning from 2010 to 2020 while accounting for influential factor inputs (labor and capital) within the framework of the traditional growth model. The results posit that both capital and labor significantly induce economic growth levels among the countries, while nuclear energy consumption is not a significant driver of growth levels despite some evidence of its positive roles. Hence, more investments in nuclear energy production are recommended to trigger an overall consumption level that will not only yield significant desirable economic growth impacts among the countries but also enhance possible environmental benefits in contrast to the growing environmentally detrimental fossil energy consumption among the countries.

Effects of foreign aid and energy aid inflows on renewable and non-renewable electricity production in BRICS countries.

We empirically examine the effects of overseas aggregate aid and energy aid inflows on renewable and non-renewable electricity production in selected BRICS countries (i.e., Brazil, India, China, and South Africa) from 1995 to 2015. Economic growth, foreign direct investment inflows, and trade openness are control variables in electricity production functions. The results from employing fully modified ordinary least square and dynamic OLS techniques indicate that economic growth, inflows of aggregate aid, energy aid, and foreign direct investment promote renewable electricity production, while trade openness reduces it. We also find that aggregate aid and energy aid inflows reduce the non-renewable electricity production, while economic growth, foreign direct investment inflows, and trade openness promote it. Moreover, our study is unique and adopts different panel estimators, ensuring the robustness of the research findings. Our findings suggest that the BRICS economies' march towards a sustainable environment becomes possible if policymakers, in their climate mitigation policy, encourage greater investments of overseas aggregate aid and energy aid inflows toward renewable electricity production.

Everything Stays the Same while Everything Changes

Everything Stays the Same while Everything Changes

Investigating renewable energy-climate change nexus by aggregate or sectoral renewable energy use?

Investigating the effect of renewable energy on the drivers of climate change correctly is significant as it is the basic source of climate change mitigation. In the extant literature, its effect on climate change has been estimated predominantly by regressing aggregate rather than sectoral renewable energy use either on aggregate greenhouse gas emissions or the components of greenhouse gases (GHGs) like carbon dioxide emissions. Against this backdrop, the paper investigates if we should estimate the nexus (i) by the causal effects running from aggregate or sectoral renewable energy use to GHG emissions and (ii) by the causal effects running from renewable energy consumption to aggregate GHG emissions or to its components like carbon dioxide (CO2) emissions. To this end, the paper introduces negative and positive (functional) complementarity between sectoral renewable energy consumptions in reducing or increasing GHG emissions, takes 20 OECD countries from 1990 to 2019, and uses augmented and non-augmented auto-regressive distributed lag approach and vector error correction mechanism. The study finds substantial differences among the results coming out of (i) regressing aggregate and sectoral renewable energy consumption on GHG emissions and (ii) regressing renewable energy consumption on aggregate GHG emissions and on CO2 emissions. The paper suggests regressing sectoral rather than aggregate renewable energy consumption on the components of aggregate GHG emissions like CO2 emissions rather than on aggregate GHG emissions to produce workable, specific, and conclusive policy alternatives.

Estimating technological quality parameters of bread wheat using sensor-based Normalized Difference Vegetation Index

This multi-environment study aimed to investigate the usability of the sensor-based Normalized Difference Vegetation Index (NDVI) for estimating bread wheat technological quality properties. The relationships between the technological quality properties and NDVI Zadoks (ZD) 31 stage readings were evaluated by grouping the trials as low and high-yielding environments (LYE and HYE). According to the results, the quality parameters and grain yield were higher in the HYE compared to the LYE group. Generally, the increase in the grain protein content (GPC), macro SDS sedimentation (MSDS) volume, and GlutoPeak aggregation energy (AGGEN) value differences became evident after 80 kg ha−1 nitrogen dose in the LYE, while, significant effects were observed after 40 kg ha−1 dose in the HYE. The equations developed in this study can be used for the estimation of GPC, and technological quality properties in the NDVI ZD31 stage. The R2s of the regression equations developed for the actual and predicted technological quality parameters of bread wheat were 0.216 for GPC, 0.295 for MSDS, and 0.226 for AGGEN values (p < 0.01; n = 240). As a result of this study, the reading value of 0.758 was found as a critical NDVI ZD31 threshold for the recommendation of nitrogen to obtain high technological quality.

Stochastic shaping of aggregator energy and reserve bids to ensure network security

Rising market participation of DER through aggregators increases the risk of network constraint violation at the distribution level. Existing approaches to network-secure DER coordination often require centralised resource management/oversight, or involve complex iterative negotiations between prosumers and the distribution service operator (DSO). In response to industry needs for readily-implementable solutions, we propose a straightforward network-secure bid curtailment approach performed by DSOs, allowing for separation of DSO and aggregator roles while factoring aggregator preferences communicated through bids. In extension to previous work, our approach directly maximises expected aggregator benefit in the market by factoring forecast market prices and bid prices into the objective. We mitigate risks of inaccurate market forecasts using a price-probabilistic stochastic program, bringing benefit to within 1% of the perfect information case in our simulations using real data. We demonstrate a 9% improvement in aggregator benefit compared to related approaches, with further gains available through real-reactive power co-optimisation.

Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring

From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as energy disaggregation, tries to decompose aggregated energy consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial Intelligence (AI) have been proposed commonly used in these models, which can be expensive to run on a server or prohibitive when the target device has limited capabilities. AI-based models are typically computationally expensive and require a lot of storage. It is not easy to reduce the computing cost and size of a neural network without sacrificing performance. This study proposed an efficient non-parametric supervised machine learning network (ENSML) architecture with a smaller size, and a quick inference time without sacrificing performance. The proposed architecture can maximise energy disaggregation performance and predict new observations based on past ones. The results showed that employing the ENSML model considerably increased the accuracy of energy prediction in 99 percent of cases.

Spectral information from photon statistics in x-ray radiography and computed tomography

Measuring spectral information requires distinguishing photons of different energies, typically achieved using filters or advanced detectors sensitive to individual photons. We prove that when the arrival of photons follows a Poisson distribution (i.e., is shot-noise dominated), the variance and mean of the total photon energy collected over a fixed interval provide independent information about the photon energy spectrum. This immediately leads to several striking corollaries. First, the variance data can be used in the same way as the mean data in both x-ray radiography and tomography. Second, the ratio of the variance data and mean data provides an indication of the average energy of the photon spectrum. Third, the variance data, when compared to the mean data, reveal information analogous to using a higher-energy spectrum. These corollaries show that spectral information can be recovered even from common x-ray detectors which only measure aggregate photon energy. As a demonstration, we performed beam-hardening correction in computed tomography using variance data for both simulations and experiments. We also achieved quantitative tomographic reconstruction in simulation and recover accurate attenuation coefficient values in addition to material density and atomic number.

Exploring the Interplay between Disordered and Ordered Oligomer Channels on the Aggregation Energy Landscapes of α-Synuclein.

The abnormal aggregation of α-synulcein is associated with multiple neurodegenerative diseases such as Parkinson's disease. The hydrophobic non-amyloid component (NAC) region of α-synuclein comprises the core of the fibril in vitro and in vivo. In this work, we study the aggregation landscape of the hydrophobic NAC region of α-synuclein using a transferrable coarse-grained force field, the associative memory water-mediated structure, and energy model (AWSEM). Using structural similarity, we can group metastable states on the free energy landscape of aggregation into three types of oligomers: disordered oligomers, prefibrillar oligomers with disordered tips, and ordered prefibrillar oligomers. The prefibrillar oligomers with disordered tips have more in-register parallel β strands than do the fully disordered oligomers but have fewer in-register parallel β strands than the ordered prefibrillar oligomers. Along with the ordered prefibrillar species, the disordered oligomeric states dominate at small oligomer sizes while the prefibrillar species with disordered tips thermodynamically dominate with the growth of oligomers. The topology of the aggregation landscape and observations in simulations suggest there is backtracking between ordered prefibrillar oligomers and other kinds of oligomers as the aggregation proceeds. The significant structural differences between the ordered prefibrillar oligomers and the disordered oligomers support the idea that the growth of these two kinds of oligomers involves kinetically independent parallel pathways. In contrast, the overall structural similarity between the fully ordered prefibrillar oligomers and the prefibrillar oligomers with disordered tips implies that two channels can interconvert on slower time scales. We also evaluate the effects of phosphorylation on the aggregation free energy landscape using statistical mechanical perturbation theory.

Optimal strategy of electricity and natural gas aggregators in the energy and balance markets

This paper presents a stochastic two-stage model for energy aggregators (EAs) in the energy and balancing markets to supply electricity and natural gas to end-users equipped with combined heat and power (CHP) units. The suggested model takes into account the battery energy storage (BES) as a self-generating unit of EA. The upper and lower subproblems determine the optimal energy supply strategy of EA and consumption of consumers, respectively. In the lower subproblem, the McCormick relaxation is used to linearize the cost function of the CHP unit. To solve the proposed model, the two-stage problem is transformed into a linear single-stage problem using the KKT conditions of the lower subproblem, the Big M method, and the strong duality theory. The performance and efficiency of the proposed model are evaluated using a case study and three scenarios. According to the simulation results, adding CHP units to the energy-scheduling problem of BES-owned aggregators increases the profit of EA by 5.96% and decreases the cost of consumers by 1.57%.

Insight into Hofmeister effects on aggregation of 2:1 and 1:1 type clay minerals

Abstract2:1 and 1:1 types of clay minerals are ubiquitous in soil, and their different surface properties result in distinct environmental behaviours. In this study, dynamic light scattering (DLS) technology was adopted to determine the aggregation kinetics of 2:1 type (Montmorillonite [Mt]) and 1:1 type (Kaolinite [Ka]) clay minerals in different electrolytes. Hofmeister effects on their aggregation characteristics and mechanisms were explored. The results showed that the activation energy and critical coagulation concentration (CCC) for Mt and Ka aggregation followed Hofmeister sequences of Na+ &gt; K+ &gt; Cs+, Mg2+ &gt; Ca2+ &gt; Zn2+ ≈ Cd2+ &gt; Cu2+&gt; Pb2+. The analyses of physical insights into those differences revealed that the observed Hofmeister sequence was produced from the different non‐classical polarisation of cations and surface O atoms in the strong electric field of clay, which led to non‐electrostatic force adsorption of cations at the surface (or in the Stern layer). In this study, the effective charge coefficient γ was employed to characterise the intensity of the non‐electrostatic adsorption forces of cations arising from the non‐classical polarisation, and the γ values followed Na+ &lt; K+ &lt; Cs+, Mg2+ &lt; Ca2+ &lt; Zn2+ = Cd2+ &lt; Cu2+ &lt; Pb2+. For alkali and alkali earth metal cations with same valence, the more the electron layers, the ‘softer’ the electron cloud, the stronger the non‐classical polarisation effects in the electric field and the stronger ability to shield the electric field around the particle, which finally lead to the larger γ and lower CCC for clay minerals aggregation. For heavy metal cations, different s/p/d empty orbitals provided the basis for the difference in polarisation‐induced coordination between heavy metal cations with surface O atoms on clay. The results also showed that, the Hofmeister effects on Mt/Ka aggregation were different, it was from the differences in Hamaker constant and surface charge density for the two clay minerals.Highlights Mt and Ka aggregations exhibited different Hofmeister effects. Repulsive force among Mt particles was much larger than Ka. Cationic polarisation played critical role in particle interactions of Mt. Polarisation‐induced covalent adsorption of cations was observed at Mt surface.

Assessing the energy security of European countries in the resource and economic context

Research background: In recent years, much attention in the literature has been paid to the economic and environmental conditions of energy development as a key sector for the development of national economies. The issue of availability of individual energy resources and related energy security is receding into the background, most often due to the strong globalization of economies and the associated assumption of free international flow of goods and services, and thus practically unlimited possibilities of buying on the international commodity markets and energy exchanges. Nevertheless, the importance of energy security increases significantly in crisis situations. Purpose of the article: Bearing in mind the circumstances indicated above, the main objective of this article is to assess the energy security of European countries carried out in the context of resource and economic conditions. Methods: The article proposes its own methodological approach to the assessment of energy security based on the analysis of the energy mixes of 32 European countries and the availability of their own internal energy sources. In the process of classifying the studied economies into homogeneous groups in terms of resource and economic determinants (GDP per capita), principal component analysis was used. Findings &amp; value added: The theoretical and methodological added value of the article results from the development of the author's approach to the assessment of energy security using individual and aggregated energy sufficiency index. Empirical added value is related to the results of energy security assessment in European countries, which can be used in shaping energy policies. Therefore, most of the 32 European countries assessed have a low level of energy security, which is due to the dominant use of non-renewable energy resources in their energy mixes. Countries with a high level of energy security have their own non-renewable resources or use alternative energy carriers in the form of renewable sources, hydropower or nuclear energy.

High‐Performance and High‐Stability all‐Polymer Photomultiplication‐Type Organic Photodiode Using an NDI‐Based Polymer Acceptor with Precisely Controlled Backbone Planarity

AbstractIt is shown that the performance and the operational stability of an all‐polymer photomultiplication‐type organic photodiode (PM‐OPD) can be significantly enhanced by realizing near‐ideal spatial isolation of polymer acceptor via a synthetic approach. A series of new naphthalenediimide‐based D–A polymer acceptors, PNDI–Ph, PNDI–Tol, and PNDI–Xy, with different degrees of backbone planarity are synthesized. By introducing benzene, toluene, and p–xylene as the donor units, increasing intramolecular torsional angle is expected. Thus, 2D grazing‐incidence X‐ray diffraction reveals the highest paracrystalline disorder in the PNDI–Xy thin film. Furthermore, PNDI–Xy has the lowest surface energy resulting in the smallest surface energy difference with matrix donor polymer, poly(3‐hexylthiophene‐diyl) (P3HT). When combined with P3HT, the less aggregated and low surface energy nature of PNDI–Xy results in near‐ideal spatial isolation. Consequently, the all‐polymer PM‐OPD yielded a high external quantum efficiency of 770 000% with specific detectivity of 3.06 × 1013 Jones. The physics behind the success of PNDI–Xy in PM‐OPD is discussed in conjunction with temperature‐dependent current density‐voltage analyses and drift‐diffusion simulations. Furthermore, the use of polymer acceptor enables the resulting PM‐OPD to retain its performance for 24 h, with significantly improved operational stability.

An energy‐based approach to study the aggregation/agglomeration phenomenon in polymer nanocomposites: Dispersion force against inter‐particle cohesion

AbstractIn this study, a novel method was proposed to predict the size and content of the nanoparticle aggregates/agglomerates in polymer nanocomposites based on the characteristics of the mixer, cohesion energy between nanoparticles and their content in the system. The internal energy equilibrium was evaluated by calculating the induced mixing energy by the mixer, aggregation, and agglomeration energies. An analytical model was designed to predict the tensile modulus of the system based on dispersion content and size of the aggregates/agglomerates. In addition, the tensile modulus of the aggregates/agglomerates was defined by developing Wacke's empirical equation. Two sets of PP and PA6 nanocomposite samples, containing 1–3 vol% of silica nanoparticles, were prepared and tested to verify the theoretical results. The applied nanoparticles were subjected to surface modification processes to maximize their compatibility with their surrounding polymer matrix. Different tests, including FE‐SEM, contact‐angle and tensile, were used to determine the mechanical/physical characteristics of the samples. The predicted results for the size of the aggregated/agglomerated domains were very close to those defined using FE‐SEM tests. On the other hand, the predicted tensile moduli based on the content, size and mechanical characteristics of the aggregated/agglomerated domains were acceptably accurate considering the tensile test results.

Efficient Privacy-Preserving and Secure Authentication for Electric-Vehicle-to-Electric-Vehicle-Charging System Based on ECQV

The use of Electric Vehicles (EVs) is almost inevitable in the near future for the sake of the environment and our plant’s long-term sustainability. The availability of an Electric-Vehicle-Charging Station (EVCS) is the key challenge that owners are worried about. Therefore, we suggest benefiting from individual EVs that have excess energy and are willing to share it with other EVs in order to maximize the availability of EVCSs without the need to rely on the existing charging infrastructure. The Internet of Electric Vehicles (IoEV) is gradually gaining traction, allowing for a more efficient and intelligent transportation system by leveraging these capabilities between EVs. However, the IoEV is considered a trustless environment, with untrustworthy trading partners such as data sellers, buyers, and brokers. Data exchanged between the EV and the Energy AGgregator (EAG) or EV/EV can be used to analyze users’ behavior and compromise their privacy. Thus, a Vehicle-to-Vehicle (V2V)-charging system that is both secure and private must be established. Several V2V-charging systems with security and privacy features have been proposed. However, even if the transmitted communications are entirely anonymous, anonymity alone will not prevent the tracking adversary from reconstructing the target vehicle’s route. These systems frequently fail to find a balance between privacy concerns (e.g., trade traceability to achieve anonymity, and so on) and security measures. In this paper, we propose an efficient privacy-preserving and secure authentication based on Elliptic Curve Qu–Vanstone (ECQV) for a V2V-charging system that fulfils the essential requirements and re-authentication protocol in order to reduce the overhead of future authentication processes. The proposed scheme utilizes the ECQV implicit-certificate mechanism to create credentials and authenticate EVs. The proposed protocols provide efficient security and privacy to EVs, as well as an 88% reduction in computational time through re-authentication, as compared to earlier efforts.

Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering.

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.