Hasil untuk "Energy conservation"

Hunt Allcott, Todd Rogers

R. Thauer, Anne-Kristin Kaster, H. Seedorf et al.

M. Wikström, K. Krab, Vivek Sharma

This review focuses on the type A cytochrome c oxidases (CcO), which are found in all mitochondria and also in several aerobic bacteria. CcO catalyzes the respiratory reduction of dioxygen (O2) to water by an intriguing mechanism, the details of which are fairly well understood today as a result of research for over four decades. Perhaps even more intriguingly, the membrane-bound CcO couples the O2 reduction chemistry to translocation of protons across the membrane, thus contributing to generation of the electrochemical proton gradient that is used to drive the synthesis of ATP as catalyzed by the rotary ATP synthase in the same membrane. After reviewing the structure of the core subunits of CcO, the active site, and the transfer paths of electrons, protons, oxygen, and water, we describe the states of the catalytic cycle and point out the few remaining uncertainties. Finally, we discuss the mechanism of proton translocation and the controversies in that area that still prevail.

Seongmin Kim, In-Saeng Suh, Travis S. Humble et al.

Developing high-performance materials is critical for diverse energy applications to increase efficiency, improve sustainability and reduce costs. Classical computational methods have enabled important breakthroughs in energy materials development, but they face scaling and time-complexity limitations, particularly for high-dimensional or strongly correlated material systems. Quantum computing (QC) promises to offer a paradigm shift by exploiting quantum bits with their superposition and entanglement to address challenging problems intractable for classical approaches. This perspective discusses the opportunities in leveraging QC to advance energy materials research and the challenges QC faces in solving complex and high-dimensional problems. We present cases on how QC, when combined with classical computing methods, can be used for the design and simulation of practical energy materials. We also outline the outlook for error-corrected, fault-tolerant QC capable of achieving predictive accuracy and quantum advantage for complex material systems.

Guillermo Folguera

A partir del año 2015, aumentó considerablemente la demanda y el precio del litio debido a su rol central en la producción de baterías. América Latina posee alrededor del 60% de las reservas de litio a nivel mundial. Los yacimientos se ubican, principalmente, en la puna que comparten Chile, Bolivia, Perú y Argentina. La posibilidad de extraer litio de los salares es promocionada por gobiernos y empresas como una oportunidad irrenunciable. A su vez, la minería de litio es presentada como clave para la solución a la crisis climática, ante la necesidad de disminuir los gases de efecto invernadero y favorecer la transición energética. En oposición, la extracción de litio es considerada por las comunidades locales como perniciosa porque involucra la pérdida de grandes volúmenes de agua, en la medida en que el litio se encuentra disuelto en los salares y de que una gran cantidad de agua dulce es utilizada durante el proceso y por la contaminación química que genera. En este trabajo se abordará la construcción discursiva y argumentativa de la extracción de litio. La hipótesis general es que la extracción de litio se presenta como inevitable en tanto no hay otras alternativas posibles frente a la crisis climática, impidiendo su discusión en términos políticos.

Yaping Fu, Fuquan Wang, Zhengyuan Li et al.

Abstract Remanufacturing has become a mainstream sustainable manufacturing paradigm for energy conservation and environmental protection. Disassembly and reprocessing operations are two main activities in remanufacturing. This work proposes multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and random processing time. First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. Second, a reinforcement learning-based multiobjective evolutionary algorithm is devised considering problem-specific knowledge. Three search strategy combinations are formed: crossover and mutation, crossover and key product-based iterated local search, mutation and key product-based iterated local search. At each iteration, a Q-learning method is devised to intelligently choose a combination of premium strategies. A stochastic simulation is incorporated to evaluate the objective values of the searched solutions. Finally, the formulated model and method are compared with an exact solver, CPLEX, and three well-known metaheuristics from the literature on a set of test instances. The results confirm the excellent competitiveness of the developed model and algorithm for solving the considered problem.

Alan R. Vincelette

Protection of the environment and its life forms has become a significant concern among philosophers and theologians alike in recent years. There is disagreement, however, over the best way to formulate the grounds of this concern. Some philosophers and theologians favor an instrumental or anthropocentric approach, claiming that adequate preservation of wildlife is warranted solely on the basis of benefits provided to humans, whether couched in terms of the satisfaction of material, medicinal, recreational, or psychological needs. Others claim that wild nature should be preserved for its own sake, due to its life forms possessing intrinsic value. How best to articulate and defend the intrinsic value of wildlife, however, has been much disputed. This paper first compares the adequacy of anthropocentric and non-anthropocentric approaches to environmental ethics. It concludes that a non-anthropocentric theory of the intrinsic value of living creatures is best suited to motivate care for and action on behalf of the environment, and, in addition, most accurately reflects the basis of human concern for the environment. This paper next goes on to examine the philosophical underpinnings required for a theory of the intrinsic value of nature. It argues that an objective account of the intrinsic value of nature, founded on some form of <i>non-naturalist ethics</i> or <i>minimal theism</i>, seems necessary to account for the intrinsic value of nature (in contrast with a purely subjective or naturalist approach). In particular, a sacramental view of nature wherein creation issues from a creator who is goodness itself seems ideal for grounding the intrinsic value of wildlife, along with motivating humans to contribute energy and resources to their conservation and even to sacrifice some of their interests in order to do so. This being the case, rather than being a hindrance to environmental ethics, religion, if properly formulated, can be a most helpful ally.

Ünal Çamdalı

Thermodynamics is the most basic energy science. It is derived from the words thermal (heat) and dynamic (motion). Although its laws have existed since the beginning of the universe, the development of thermodynamics as a science was with the invention of steam engines in England. These laws, as known, are the Zeroth, First, Second and Third Laws of Thermodynamics. These are four macro laws, and they were determined based on observation. They are not the product of theoretical thought. Zeroth Law reveals the basic structure of temperature measurement, based on the principle that if two different systems are in thermal equilibrium separately with a third system, there must be thermal equilibrium between them. The First and Second Laws are the fundamental laws regarding energy. The first law, also known as the law of conservation of energy, and the second law, also known as the law of entropy, along with explaining the principles of energy conversion; also make a significant contribution to understand the functioning mechanism of the universe. The Third Law states that as chemically homogeneous and perfectly crystalline substances approach absolute zero temperature (273 C, 0 K), their entropy (or entropy changes) will also approach zero; in other words, it states that there can be no disorder or movement in the substances in question at this temperature. It is also important that thermodynamics science has a wide range of applications, from technical fields to philosophy, therefore it is known by large masses. Because its laws are among the most fundamental laws of the universe, in other words it is universal. Laws are noteworthy for the establishment, operation and analysis of engineering and many other systems, as well as for understanding the order of the universe. Additionally, some cosmologists strive to explain the order and functioning mechanism of the universe by making use of the laws of thermodynamics. Moreover, the laws of thermodynamics also give clues about the existence of the universe. Entropy, defined based on the second law of thermodynamics, is a phenomenon that gives the numerical magnitude of the disorder or complexity of a system. The more disordered or complex a system is, the greater its irreversibility and entropy will be. There is also a structure of the law that affects every system everywhere in the universe. It is not dependent on time and space. Time and space are literally subject to this law. The law has been in effect since the beginning of the universe, and it will continue to exist if the universe exists. Because universe means system; system means mechanism; mechanism means a structure that works according to the sovereignty of laws. Energy is needed to sustain life. Even though energy is not destroyed, the use of resources means that they move from a certain potential to a dead state (environment) because of the law of entropy. This process is valid for all systems in the universe. While all energies are processed through this process, living creatures in nature experience a similar process as they move from life to death. Eventually, all energy sources in the universe will go to the environment and become dead. This situation can be described as the entropy apocalypse of the universe. The concept of the apocalypse also reflects chaotic conditions such as noise and turmoil. Sources state that a cosmically stagnant situation will occur before the apocalypse. This is the cosmic dead state of the universe. In this study, an attempt was made to establish a relationship between entropy and doomsday by using the laws of Thermodynamics. In this context, the fact that the end of the universe is similar to the point where the entropy apocalypse will occur, as expressed in the doomsday scenario in religious literature (including the Religion of Islam), is tried to be explained on the plane of science and religion.

H. Patrick Roberts, Lori Erb, Lisabeth Willey et al.

Turtle populations are declining globally, yet limited attention has been directed toward understanding the conservation status of species perceived to be widespread and common. The goal of this study was to contribute to the understanding of the conservation status of the eastern box turtle (Terrapene carolina), a wide-ranging terrestrial generalist, in the northeastern United States (Maine to Virginia) by (1) characterizing relationships between occurrence and anthropogenic land use and (2) estimating the extent of land-use driven habitat impairment for the region. We used a regional dataset of occurrence records combined with pseudo-absences to develop species distribution models to first estimate the potential distribution in the northeastern United States and then predict habitat suitability within that distribution. We observed a strong positive relationship between probability of occurrence and canopy cover (within 180 m) and a strong negative relationship with hay/pasture fields (360 m), cultivated crops (180 m), impervious surface (360 m), and forest loss (since 2000; 1440 m). We estimate that approximately 51% of eastern box turtle habitat in the northeastern United States may be impaired by land use, with the majority of impairment predicted from Pennsylvania and Delaware south to Virginia. This study, in combination with previous long-term studies documenting population declines, suggests that greater attention to the conservation status of the eastern box turtle is warranted.

Ning Qi, Kaidi Huang, Zhiyuan Fan et al.

This paper studies the long-term energy management of a microgrid coordinating hybrid hydrogen-battery energy storage. We develop an approximate semi-empirical hydrogen storage model to accurately capture the power-dependent efficiency of hydrogen storage. We introduce a prediction-free two-stage coordinated optimization framework, which generates the annual state-of-charge (SoC) reference for hydrogen storage offline. During online operation, it updates the SoC reference online using kernel regression and makes operation decisions based on the proposed adaptive virtual-queue-based online convex optimization (OCO) algorithm. We innovatively incorporate penalty terms for long-term pattern tracking and expert-tracking for step size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of dynamic regret without using prediction information. Numerical studies based on the Elia and North China datasets show that the proposed framework significantly outperforms the existing online optimization approaches by reducing the operational costs and loss of load by around 30% and 80%, respectively. These benefits can be further enhanced with optimized settings for the penalty coefficient and step size of OCO, as well as more historical references.

Chun Fu, Hussain Kazmi, Matias Quintana et al.

Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.

Renan Lima Baima, Iván Abellán Álvarez, Ivan Pavić et al.

In response to the European Commission's aim of cutting carbon emissions by 2050, there is a growing need for cutting-edge solutions to promote low-carbon energy consumption in public infrastructures. This paper introduces a Proof of Concept (PoC) that integrates the transparency and immutability of blockchain and the Internet of Things (IoT) to enhance energy efficiency in tangible government-held public assets, focusing on curbing carbon emissions. Our system design utilizes a forecasting and optimization framework, inscribing the scheduled operations of heat pumps on a public sector blockchain. Registering usage metrics on the blockchain facilitates the verification of energy conservation, allows transparency in public energy consumption, and augments public awareness of energy usage patterns. The system fine-tunes the operations of electric heat pumps, prioritizing their use during low-carbon emission periods in power systems occurring during high renewable energy generations. Adaptive temperature configuration and schedules enable energy management in public venues, but blockchains' processing power and latency may represent bottlenecks setting scalability limits. However, the proof-of-concept weakness and other barriers are surpassed by the public sector blockchain advantages, leading to future research and tech innovations to fully exploit the synergies of blockchain and IoT in harnessing sustainable, low-carbon energy in the public domain.

D. F. Iliasov, A. Y. Ivanov, E. O. Kuznetsova

The relevance of the conducted research is determined by the need to control and forecast the costs of enterprises to maintain shutdown nuclear energy facilities in a save condition in order to solve budget planning problems. In the near future, an increase in operating expenses is expected due to an increase in the number of such facilities. To solve the problem econometric forecasting methods are used.Purpose of the research. This study is aimed to develop a methodology for estimating the costs of maintaining nuclear and radiation hazardous facilities in a safe condition and the safe storage of radioactive waste, considering the specifics of accounting at nuclear industry enterprises and forecasting such costs, taking into account plans for shutting down and decommissioning of facilities and removing accumulated radioactive waste from storage facilities in the future.Materials and methods. In the research there was developed the methodology of isolation from the overall structure costs, associated with maintaining shutdown facilities and storage facilities for radioactive waste of nuclear industry enterprises. A cumulative estimation of such costs has been carried out for facilities whose operation for the intended purpose has currently been discontinued. Based on obtained retrospective data, a correlation-regression model with structural changes was developed to predict the costs of maintaining nuclear energy facilities in a safe condition which are expected to be shut down in the future. The developed model allows considering the specifics of facilities, their level of radiation hazard and overall characteristics, the average level of wages at enterprises and the cost of resources.Results. Based on the plans for the shutdown and decommissioning of nuclear energy facilities and the disposal of radioactive waste from storage facilities, the costs of maintaining such facilities in a safe condition with a planning horizon up to 2050 have been forecasted. The results of calculations show that the costs for nuclear energy facilities (except for nuclear power plant units) will increase to 10 billion rubles per year by 2035 (in 2022 prices) and will significantly decrease after 2035 - up to 7.5 billion rubles per year. The reduction will be due to the transfer to a nuclear and radiation safe state of large facilities of radiochemical plants, as well as the conservation of sites for the placement of industrial uranium-graphite reactors. The costs of maintaining the storage facilities of accumulated radioactive waste in a safe condition will be reduced annually (almost linearly) as they are transferred to the disposal of the removed radioactive waste and the conservation of special radioactive waste facilities. By 2035, costs will be reduced from the current level of 5 billion rubles to 4.3 billion rubles per year.Conclusion. The results of the study confirm the high importance of the implementation of the Federal Target Program “Ensuring Nuclear and Radiation Safety for 2016-2020 and for the period up to 2035”, under which it is planned to decommission about 75 stopped nuclear and radiation hazardous facilities and transfer about 155 thousand cubic meters of radioactive waste to disposal.

Constantin Udriste, Ionel Tevy, Paun Antonescu

The optimal control currently decides the minimum energy consumption within the problems attached to subways. Among other things, we formulate and solve an optimal bi-control problem, the two controls being the acceleration and the feed-back of a Riemannian connection. The control space is a square, and the optimal controls are of the bang–bang type. The third component of the optimal solution is the maximum value function, as a solution of the Hamilton–Jacobi–Bellman PDE. The examples of energy optimal trajectories refer to the lines of the Bucharest subway.

Jean-Paul Caltagirone

The decay of the kinetic energy of a turbulent flow with time is not necessarily monotonic. This is revealed by simulations performed in the framework of discrete mechanics, where the kinetic energy can be transformed into pressure energy or vice versa; this persistent phenomenon is also observed for inviscid fluids. Different types of viscous vortex filaments generated by initial velocity conditions show that vortex stretching phenomena precede an abrupt onset of vortex bursting in high-shear regions. In all cases, the kinetic energy starts to grow by borrowing energy from the pressure before the transfer phase to the small turbulent structures. The result observed on the vortex filament is also found for the Taylor–Green vortex, which significantly differs from the previous results on this same case simulated from the Navier–Stokes equations. This disagreement is attributed to the physical model used, that of discrete mechanics, where the formulation is based on the conservation of acceleration. The reasons for this divergence are analyzed in depth; however, a spectral analysis allows finding the established laws on the decay of kinetic energy as a function of the wave number.

Fu-Hsuan Chen, Hao-Ren Liu

This manuscript focuses on analyzing the growth dynamics of the Central Taiwan Science Park (CTSP) and Silicon Glen in Scotland with a specific emphasis on their approaches to energy, environmental conservation, and economic management. The objective is to provide insights into their sustainable development strategies. In terms of energy, CTSP addresses Taiwan’s energy security and green transformation challenges, while Silicon Glen concentrates on Scotland’s wind energy generation technologies. Both regions prioritize the advancement of renewable energy sources and smart grid technologies. In the realm of environmental conservation, both CTSP and Silicon Glen prioritize environmental protection and sustainability by implementing rigorous environmental monitoring measures. Regarding economic management, CTSP and Silicon Glen serve as vital technology industry hubs in Taiwan and Scotland, respectively, attracting a multitude of high-tech and startup enterprises. This growth is facilitated through various means, including policy support, access to research resources, and robust infrastructure. This manuscript presents a comparative analysis of these two industrial parks, focusing on their environmental and economic management strategies. It aims to elucidate the principles underpinning the sustainable development and economic growth of industrial parks, offering valuable insights to decision-makers and stakeholders involved in the planning of sustainable industrial parks.

Yang Li, Meng Han, Mohammad Shahidehpour et al.

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and infinity-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

Susan Babinec, Ian Baring-Gould, Amy N. Bender et al.

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. This study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all-diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

Kaiyu Sun, Tianzhen Hong

Sho Cremers, Valentin Robu, Peter Zhang et al.

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O'Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower.