Search

One of the most important problems in dark fermentative hydrogen production is the need for the regulation of the ambient pH due to the formation of volatile fatty acids. For this purpose, alkaline chemicals are usually used; however, alkaline addition to the media increases salt formation, which negatively affects microbial activity. In this study, pH adjustment was performed by providing direct current as an alternative to alkali addition. Experiments were carried out batchwise and electricity was only applied to neutralize the acidic pH value. The waste paper hydrolysate was used as a substrate and the effects of applied voltage (0.25–3 V), substrate concentration (4.8–41.2 g/L at 1 V) and electrode distance (0.3–1.3 cm at 1 V) on hydrogen production performance was investigated. Voltages less than 1 V required too much time to keep the pH convenient and voltages more than 1.5 V adversely affected hydrogen production performance. Providing direct current to the fermentation media significantly reduced the adaptation phase. Conditions for optimal pH adjustment and hydrogen production were obtained at 1 V, 20 g glucose/L and 0.5 cm electrode distance. • Most convenient H 2 production was obtained at 1 V, 20 g glucose/L and 0.5 cm electrode distance. • H 2 was efficiently produced up to 29.1 g glucose/L with DC assistance. • Proper electrode distance enables sufficient electron transfer for pH regulation. [ABSTRACT FROM AUTHOR]

Graphite, an allotrope of carbon, is also a moderately good conductor of electricity. The shading pencil lead consists of graphite mixed with clay and, therefore, can conduct electricity, allowing one to construct and demonstrate different properties and the underlying concepts of circuit elements. The grade displayed on the pencil relates to the graphite-to-clay ratio, with higher B grades indicating a higher graphite-to-clay ratio. In this paper, the main objective is to show the dependence of resistance and capacitors on different physical parameters and to calculate the value of π electrically. The materials used in this activity are readily available, affordable, and suitable for demonstration purposes. The materials used to perform these activities are shading pencils of different grades, A-4 size papers, crocodile clips, and an LCR meter. [ABSTRACT FROM AUTHOR]

A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

Cogeneration is considered as one of the promising energy efficient techniques for producing electricity and useful thermal energy from a single fuel source. This paper describes a feasibility study of the implementation of different cogeneration options to a paper mill to evaluate their energy saving potentials and economic benefits. The investigated systems in this paper are gas turbine, steam turbine and combined cycle cogeneration options evaluated and compared based on energy utilisation factor and Annualised Life Cycle Cost (ALCC) analysis. The results of energy analysis show that all Combined Heat and Power (CHP) systems satisfy the thermal energy needs of the mill while the combined cycle cogeneration option generates the highest amount of electric energy equal to 10.91 MW. The gas turbine cogeneration system is shown as the most advantageous system from overall energy point of view with the highest energy utilisation factor of 78% meeting the heat and electric requirements of the mill and exporting 21,329 MWh of additional electricity to the grid. Economic feasibility results also show that an overall saving of 5.12 million US$ can be achieved by implementing the gas turbine cogeneration system to the mill making it as the best cogeneration option with the least overall ALCC of 1.34 million US$ and the maximum percentage of CO 2 emission reduction (68%) when compared with the other options. [ABSTRACT FROM AUTHOR]

This article examines the economic possibilities for the Swedish pulp and paper industry in general and the kraft pulp industry in particular of increasing the share of internally generated energy by using readily available production residues such as black liquor. It is suggested that the industry's reliance on fossil fuels and purchased electricity has decreased over time, thereby reducing the impact of external energy prices on energy investments. The results suggest that the use of biomass will only increase to the point where the existing capacity to do so is met unless heavy investment subsidies are made available. [ABSTRACT FROM AUTHOR]

We report a novel paper-based biobattery which generates power from microorganism-containing liquid derived from renewable and sustainable wastewater which is readily accessible in the local environment. The device fuses the art of origami and the technology of microbial fuel cells (MFCs) and has the potential to shift the paradigm for flexible and stackable paper-based batteries by enabling exceptional electrical characteristics and functionalities. 3D, modular, and retractable battery stack is created from (i) 2D paper sheets through high degrees of folding and (ii) multifunctional layers sandwiched for MFC device configuration. The stack is based on ninja star-shaped origami design formed by eight MFC modular blades, which is retractable from sharp shuriken (closed) to round frisbee (opened). The microorganism-containing wastewater is added into an inlet of the closed battery stack and it is transported into each MFC module through patterned fluidic pathways in the paper layers. During operation, the battery stack is transformed into the round frisbee to connect eight MFC modules in series for improving the power output and simultaneously expose all air-cathodes to the air for their cathodic reactions. The device generates desired values of electrical current and potential for powering an LED for more than 20 min. [ABSTRACT FROM AUTHOR]

Harvesting renewable mechanical energy is envisioned as a promising and sustainable way for power generation. Many recent mechanical energy harvesters are able to produce instantaneous (pulsed) electricity with a high peak voltage of over 100 V. However, directly storing such irregular high‐voltage pulse electricity remains a great challenge. The use of extra power management components can boost storage efficiency but increase system complexity. Here utilizing the conducting polymer PEDOT:PSS, high‐rate metal‐free micro‐supercapacitor (MSC) arrays are successfully fabricated for direct high‐efficiency storage of high‐voltage pulse electricity. Within an area of 2.4 × 3.4 cm2 on various paper substrates, large‐scale MSC arrays (comprising up to 100 cells) can be printed to deliver a working voltage window of 160 V at an ultrahigh scan rate up to 30 V s−1. The ultrahigh rate capability enables the MSC arrays to quickly capture and efficiently store the high‐voltage (≈150 V) pulse electricity produced by a droplet‐based electricity generator at a high efficiency of 62%, significantly higher than that (<2%) of the batteries or capacitors demonstrated in the literature. Moreover, the compact and metal‐free features make these MSC arrays excellent candidates for sustainable high‐performance energy storage in self‐charging power systems. [ABSTRACT FROM AUTHOR]

This paper examines how struggles for housing and other basic services such as water and electricity are characterised in South Africa. There has been a shift in categorising local protests from the previously accepted “service delivery protests” to other phrases such as “local political protests” or “local protests” or “community protests.” Drawing from data gathered through in-depth interviews and focus group discussions in Gugulethu and Khayelitsha, two townships in Cape Town, South Africa, I argue that these new concepts conceal the service delivery-related challenges faced by poor communities throughout South Africa. The paper’s focus on housing stems from the fact that a house often functions as a site where several services come together. While protests are nuanced and have varying reasons, they have a large service delivery component. As such, the study of protest in South Africa will benefit from research that spotlights service delivery problems. This also helps to highlight the prevalence of such protests throughout the country. [ABSTRACT FROM AUTHOR]

The electric power sector is the primary contributor to carbon emissions in China. Considering the context of dual carbon goals, this paper examines carbon emissions within China's electricity sector. The research utilizes the LMDI approach for methodological rigor. The results show that the cumulative contribution of economies scale, power consumption factors and energy structure are 114.91%, 85.17% and 0.94%, which contribute to the increase of carbon emissions, the cumulative contribution of power generation efficiency and ratio of power dissipation to generation factor are -19.15% and -0.01%, which promotes the carbon reduction. The decomposition analysis highlights the significant influence of economic scale on carbon emissions in the electricity industry, among the seven factors investigated. Meanwhile, STIRPAT model, Logistic model and GM(1,1) model are used to predict carbon emissions, the average relative error between actual carbon emissions and the predicted values are 0.23%, 8.72% and 7.05%, which indicates that STIRPAT model is more suitable for medium- to long-term predictions. Based on these findings, the paper proposes practical suggestions to reduce carbon emissions and achieve the dual carbon goals of the power industry. [ABSTRACT FROM AUTHOR]

The integration of gas and electricity networks has emerged as a promising approach to enhance the overall flexibility of energy systems. As the transition toward sustainable and decarbonized energy sources accelerates, the seamless coordination between electricity and gas infrastructure becomes increasingly crucial. This paper presents a comprehensive review of the state-of-the-art research and developments concerning the flexibility in the operation of low-carbon integrated gas and electricity networks (IGENs) as part of the whole system approach. Methods and solutions to provide and improve flexibility in the mentioned systems are studied and categorized. Flexibility is the system's ability to deal with changes and uncertainties in the network while maintaining an acceptable level of reliability. The presented review underscores the significance of this convergence in facilitating demand-side management, renewable energy integration, and overall system resilience. By highlighting the technical, economic, and regulatory aspects of such integration, this paper aims to guide researchers, policymakers, and industry stakeholders toward effective decision-making and the formulation of comprehensive strategies that align with the decarbonization of energy systems. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) energy trading has attracted a lot of attention and the number of electric vehicles (EVs) has increased in the past couple of years. Toward sustainable mobility, EVs meet the standard development goals (SDGs) for attaining a sustainable future in the transport sector. This development and increasing number of EVs creates an opportunity for prosumers to trade electricity. Considering this opportunity, this review article aims to provide an in-depth analysis of P2P energy trading of EVs using blockchain in centralized and decentralized networks, which enables prosumers to exchange energy directly with one another. The paper is aimed to provide the reader with a state-of-the-art review on the P2P energy trading for EVs, considering different blockchain algorithms that are practically implemented or still in the research phase. Moreover, the paper presents blockchain applications, current trends, and future challenges of EVs' energy trading. P2P energy trading for EVs using blockchain algorithms can be successfully implemented considering real-time scenarios and economically benefits smart sustainable societies. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a novel enzymatic fuel cell design that employs cellulose paper-based quasi-2D microfluidic system to supply biofuel to the enzymatic layer. The state of the art nanoarchitectural design, employing carbon nanotube-based papers for the bioelectrodes, allows a single cell to maintain 400mV for 16days of continuous operation in glucose solution and reach 1mA of current output. Stacks of cells connected in series show successful performance using glucose in Gatorade® resulting in stack-cell potential of 1.8V, employed to power a digital clock for 36h, continuously. These designs open the possibility for obtaining enzymatic fuel cells that can run small portable devices on easily available ubiquitous liquids while addressing environmental concerns that are prevalent in traditional fuel cells. [Copyright &y& Elsevier]

Networks are inherently uncertain and require scenario-based approaches to handle variability. In stochastic and time-dependent networks, optimal solutions cannot always be found using deterministic algorithms. Furthermore, Stochastic Time Dependent Shortest Path problems are known to be NP-hard. Emerging Quantum Computing Methods are providing new ways to address these problems. In this paper, the STDSP problem is formulated as a Quadratic Constrained Binary Optimization Problem. We show that in the case of independent link costs, the size of the problem increases exponentially. Finally, we find that using the quantum solver provides a linear computational experience with respect to the size of the problem. The proposed solution has implications for stochastic networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains, and epidemiology. [ABSTRACT FROM AUTHOR]

AbstractThis paper presents a teaching scenario designed to introduce preschool children to electricity, structured around the 5E instructional model (Engage, Explore, Explain, Elaborate, Evaluate). The study involves 18 children from an urban kindergarten with diverse backgrounds and learning needs. Over four weeks, children engage in interactive activities to understand electrical devices, electric current, static electricity, conductors, insulators, electricity generation, and safety. The curriculum integrates interdisciplinary learning, combining literacy and science, and uses hands-on experiments and digital tools to enhance engagement. Activities include constructing simple circuits, experimenting with static electricity, and role-playing. Emphasis is placed on practical applications and safety. Findings highlight the effectiveness of a constructivist approach, where children build understanding through exploration. The study underscores the need for resources, teacher preparedness, and inclusive strategies to address diverse learning needs. This scenario provides insights into effective early childhood science education, emphasizing the potential to build a strong foundation for future learning and curiosity in young children. [ABSTRACT FROM AUTHOR]

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy. [ABSTRACT FROM AUTHOR]

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder–decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM–Attention–LSTM, LSTM–Self_Attention–LSTM, and CNN–LSTM–Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R 2 , thereby validating its predictive performance. [ABSTRACT FROM AUTHOR]

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user's identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users' privacy. [ABSTRACT FROM AUTHOR]

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]

A diamagnetic diagnostic system is designed, prepared and installed at Aditya Upgrade Tokamak. Also, the raw data is acquired in a specified acquisition system with suitable front-end electronics. The entire system consists of separate loops and is first calibrated in the presence of all coils, which conduct electricity during the plasma discharge. Then, the change in the toroidal magnetic field due to the plasma is measured using the diamagnetic system, and hence, several plasma parameters including poloidal beta, stored energy, etc. are estimated. The measurements were then repeated during various tests of the tokamak plasma, and several interesting facts were observed. The present paper covers the entire concept of designing magnetic diagnostics – from basic considerations to final data interpretation. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies allocation of fuel costs and CO2-emissions to heat and power in a CHP plant producing heat to an integrated pulp and paper mill and electricity to the liberalized electricity markets (or to the mill by the market price). The CHP plant and the mill are considered to be two separate economical units and both the mill and the CHP plant perspectives are discussed. Fuel costs and CO2-emissions are allocated using the energy, exergy and market based methods. The CHP plant purchases black liquor and bark from the mill. It also purchases oil from the markets to cover the whole fuel demand of the plant. The results indicate that the marked based method can be recommended as a “neutral” cost allocation method, because with that, the energy producer is not collecting extra margins from selling steam. This means that heat pricing is cost based. The results also indicate that depending on the emission factor and efficiency of alternative energy production form, the marked based method or the exergy method allocate the lowest CO2-emissions to heat. Therefore, the use of market based method for allocating costs and CO2 emissions can be recommended in order to use one single approach in both. [Copyright &y& Elsevier]

In this paper it has been presented the results about the study with high pressure steam schemes for the energy integration of a sugar mill with a pulp paper factory. The hypothesis for the integration is that it is possible to produce heat and electricity for both factories with the biomass of the sugar cane. This study includes the diversification of the production whit an ethanol factory for the pulp. The by bibliography, energy, material and cost balances are the main subjects for the best alternative of integration with high pressure schemes. The scheme with 61 ata and 440 °C was the best technically but not in the economic sense. [ABSTRACT FROM AUTHOR]

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers' contract bidding and large consumers' electricity purchase strategy in the medium- and long-term contract transactions. [ABSTRACT FROM AUTHOR]

Definition: In young Maxwell's eyes, electricity, magnetism and fluid mechanics present analogies that let the first be mathematically grasped in a unitary way similarly to the latter. This entry sketches the first steps of James Clerk Maxwell's unitary view of electromagnetism and fluid mechanics, with long-lasting effects on understanding the physical world. In his very first paper on the subject, Maxwell interpreted Faraday's concept of lines of force of both electricity and magnetism as filaments of a flowing fluid. That is, Maxwell suggested to unify the previously distinct fields of electricity and magnetism, considering them as different aspects of the same entity, the 'electromagnetic field', which is mathematically described by four partial differential equations. In the literature there are several comprehensive works on Maxwell's mature exposition of his theory; however, the aim of this entry contribution is rather more limited. Indeed, it is restricted to examining the contributions of Maxwell's first paper towards developing the idea of electric current as the motion of an incompressible fluid. Thus, this entry highlights the strong connections of Maxwell's epistemological view with the 'mechanistic' approach of the time to every aspect of physical phenomenology. For this purpose, this entry mentions some historical context surrounding the emergence of Maxwell's innovative concepts. [ABSTRACT FROM AUTHOR]

As penetration of EVs in the transportation sector is increasing, the demand for the mandatory installation of charging infrastructure also is increasing. In addition, renewable energy and energy storage systems (ESSs) are being reviewed for use in electric vehicle charging stations (EVCSs). In this paper, we present an optimal electricity trading volume and an optimal installation capacity of ESSs to maximize the daily profit of the EVCSs equipped with solar power generation when the EVCSs are licensed to sell energy to the power supplier during a specific time period. By formulating and solving the optimization problem of the EVCSs, this paper analyzes validation results for the different useful lives of ESSs, the peak power of a PV generator, and weather conditions at the Yangjae Solar Station and the Suseo Station public parking lot, Seoul, Republic of Korea. Furthermore, this paper validates that the daily expected profit of EVCSs with the proposed method outperforms the profit of conventional EVCSs which do not utilize ESSs. [ABSTRACT FROM AUTHOR]

This paper describes a case study of an online physics laboratory course implemented for 254 engineering students at Walailak University in Thailand in response to the COVID-19 pandemic. The laboratory sequence was designed based on the United Kingdom Professional Standards Framework and covered eight experiments on fundamental concepts of motion, electricity, waves, and fluid mechanics. The experiments were conducted at students' homes using physics education technology simulations, Tracker for video analysis, smartphones, and home-based apparatuses. During the experiments, students shared their progress with classmates and lecturers through screen sharing, which facilitated exchanges of ideas and difficulties. The paper discusses the successful implementation of the online laboratory course and the challenges encountered, such as student engagement, resource limitations, and practical skill development. The insights gained from this case study may be useful for online laboratory education beyond the pandemic. [ABSTRACT FROM AUTHOR]

Our research, which aims to present the effectiveness of the European Union's internal energy market, shows the results of a recent survey conducted among representatives of national energy regulators and exchanges from eleven EU Member States. Due to the choice of the research method based on the SOFIA concept, the results constitute a new original contribution to the existing treasury of knowledge in the field of the European energy market. In the concrete case of sociological analysis, we checked how successful the Agency for the Cooperation of Energy Regulators (ACER) is in establishing and ensuring the functioning of the internal energy market in the EU. The survey examines the performance of ACER, which became operational in March 2011 and is intended to contribute to the establishment and functioning of the EU's internal energy market from the perspective of ACER, national energy regulators and energy exchanges in EU Member States. ACER's basic mission is to ensure the successful functioning of the energy market, which, as a result (of successful operation), will lead to the full integration of the internal market, and consumers will receive energy uninterruptedly from all parts of the Union. Our research also showed that ACER is crucial for the functioning of the internal market. Most representatives of national regulators and energy exchanges agree that the EU already has a functioning internal energy market and that ACER is crucial for the functioning of the internal energy market. [ABSTRACT FROM AUTHOR]

In this article, we disclose a method to fabricate the three-dimensional (3D) image on an optically rewritable (ORW) electronic paper (e-paper) by deploying the lenticular lenses array on the top of the ORW e-paper. Unlike other autostereoscopic 3D liquid crystal displays, our autostereoscopic 3D ORW e-paper is actually a green technology, which is without electrode and voltage. No electricity is needed to maintain the 3D image on the ORW e-paper. We can easily change a 3D image by erasing and rewriting with photoalignment technology. [ABSTRACT FROM AUTHOR]

Triboelectric nanogenerators (TENGs) have been investigated as a promising device to harvest mechanical energy into electricity. At present, paper-based triboelectric nanogenerators (P-TENGs) have been widely researched because paper features with lightweight, low cost, abundance, renewability and biodegradability. In this paper, the operating principles and operation modes of TENGs, the development progress and performance enhancement routes of P-TENGs are comprehensively reviewed. Additionally, the overview, challenges and opportunities of P-TENGs applications are also presented. [ABSTRACT FROM AUTHOR]

The article discusses a paper by physicists William B. Shockley and Hans-Joachim Queisser that was published in the "Journal of Applied Physics" in 1961. The paper discussed a fundamental limit on the conversion of sunlight into electrical current. It mentions that the "Shockley-Queisser limit" describes the limited efficiency of solar cells based on absorption and reemission processes.

Abstract: This paper evaluates system aspects of biorefineries based on biomass gasification integrated with pulp and paper production. As a case the Billerud Karlsborg mill is used. Two biomass gasification concepts are considered: BIGDME (biomass integrated gasification dimethyl ether production) and BIGCC (biomass integrated gasification combined cycle). The systems analysis is made with respect to economic performance, global CO2 emissions and primary energy use. As reference cases, BIGDME and BIGCC integrated with district heating are considered. Biomass gasification is shown to be potentially profitable for the mill. The results are highly dependent on assumed energy market parameters, particularly policy support. With strong policies promoting biofuels or renewable electricity, the calculated opportunity to invest in a gasification-based biorefinery exceeds investment cost estimates from the literature. When integrated with district heating the BIGDME case performs better than the BIGCC case, which shows high sensitivity to heat price and annual operating time. The BIGCC cases show potential to contribute to decreased global CO2 emissions and energy use, which the BIGDME cases do not, mainly due to high biomass demand. As biomass is a limited resource, increased biomass use due to investments in gasification plants will lead to increased use of fossil fuels elsewhere in the system. [Copyright &y& Elsevier]

The presented paper focuses on renewable energy-assisted electricity, hydrogen, and cold energy production. Solar energy is used as the energy source in the study. The organic Rankine cycle (ORC) is used for electricity generation, a proton exchange membrane electrolyzer (PEMe) for hydrogen production, and an ejector refrigeration system for cold energy production. The study is based on the exergy concept and examines thermo-economic and thermo-environmental analyses. The exergy efficiency of each equipment in the system is calculated from a holistic perspective. Considering the energy consumption values of the equipment in the whole system, the cost required for hydrogen production is calculated as 1.086 $/kg. Additionally, the exergy efficiency of the whole system is found to be 2.082 %. • Thermodynamic analysis of the solar-based hydrogen production system. • The exergy efficiency of the whole system is found as 2.082 %. • The cost of green hydrogen production is found as 1086 $/kg. [ABSTRACT FROM AUTHOR]

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system. [ABSTRACT FROM AUTHOR]

This paper tackles high computational complexity in using Euclidean distance for residential load profiles (RLPs) similarity by proposing a three-stage incremental segmented slope clustering framework. The first two stages involve static clustering, where we obtain typical residential load profiles through piecewise slope clustering. In the third stage, dynamic clustering is performed based on the slope similarity of RLPs. This method enhances clustering performance and reduces computation cost, outperforming various benchmarks, with simulation results confirming the framework's effectiveness. [ABSTRACT FROM AUTHOR]

The performance of the heat exchanger has a huge impact on the cycle efficiency and levelised cost of electricity in solar thermal power generation. This paper proposes a novel scaled structure for the cold-side channel of the printed circuit heat exchanger (PCHE) that combines good heat transfer performance with lower pressure drop. A numerical simulation is employed to investigate and compare the performance of PCHEs with scaled, zigzag, and straight channels. When using the comprehensive performance evaluation criteria (PEC) as evaluation indicator, the scaled channel demonstrate the best overall performance, with an increase in comprehensive performance by 6 % and 12 % compared to zigzag and straight channels, respectively. The above conclusions indicate that the adoption of scaled channel structures could enhance the heat transfer performance of PCHE, providing valuable insights for the development and improvement of novel PCHE and other heat exchanger's heat transfer efficiency. [ABSTRACT FROM AUTHOR]

This paper presents experimental results of a study of the occurrence of electricity emission in the process of chemical-and-thermal treatment (CTT) and their theoretical justification. Oxide materials with semiconductor properties, along with metals, can form electric fields in technological mixtures that are used in processes related to the transfer of saturating elements to the hardened surface of a part made of steel X35CrNi2-3. The contribution of these fields to the transfer of saturating elements is comparable with that of a concentration field. A design and manufacturing technique for a measuring cell is proposed to determine the magnitude of the emission current arising in the process mixture. An equation is obtained that describes the general process of solid-phase saturation of parts with the participation of concentration and electric fields during CTT. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

The non-technical losses caused by abnormal power consumption behavior of power users seriously affect the revenue of power companies and the quality of power supply. To assist electric power companies in improving the efficiency of power consumption audit and regulating the power consumption behavior of users, this paper proposes a power consumption anomaly detection method named High-LowDAAE (Autoencoder model for dual adversarial training of high low-level temporal features). High-LowDAAE adds an extra "discriminator" named AE3 to USAD (UnSupervised Anomaly Detection on Multivariate Time Series), which performs the same function as AE2 in USAD. AE3 performs the same function as AE2 in USAD, i.e., it is trained against AE1 to enhance its ability to reconstruct average data. However, AE3 differs from AE2 because the two "discriminators" correspond to the high-level and low-level time series features output from the shared encoder network. By utilizing different levels of temporal features to reconstruct the data and conducting adversarial training, AE1 can reconstruct the time-series data more efficiently, thus improving the accuracy of detecting abnormal electricity usage. In addition, to enhance the model's feature extraction ability for time-series data, the self-encoder is constructed with a long short-term memory (LSTM) network, and the fully connected layer in the USAD model is no longer used. This modification improves the extraction of temporal features and provides richer hidden features for the adversarial training of the dual "discriminators". Finally, the ablation and comparison experiments are conducted using accurate electricity consumption data from users, and the results show that the proposed method has higher accuracy. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, it has been noted that the rate of energy consumption has increased globally due to the technological progress that has taken place. The industrial sector occupies one of the first sectors in the consumption of electrical energy. Therefore, this paper aims to use the method of artificial neural networks (ANN) to model, analyze, and forecast electricity consumption in Jordan's industrial sector. In the present analysis, several factors affecting energy consumption in this sector are studied and verified, namely: the number of the industrial establishments (ES), the number of employees (EM), the electricity price (E$), the price of fuel (F$), gross output (G), structural effect (GI/GN) and the capacity utilization (CU). Several networks are executed and tested in terms of the root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The results of this study implicated that the electricity consumption levels in the Jordanian industrial segment are highly driven by the number of industrial establishments and employees followed by the gross output, among others. Additionally, the present ANN-predicted electricity consumption results are compared with literature and showed superior accuracy. Finally, this ANN model is combined with the time series analysis approach to forecast the electricity needs of the Jordanian industrial sector for the next decade. [ABSTRACT FROM AUTHOR]

Institutions have significant implications for whether and how energy systems restructure, evolve, and successfully transition. Yet, literature analyzing energy sector reforms often approach transitions from economic or technical perspectives, with much less attention to the underlying roles and influences of institutions. This paper explores the roles and influence of institutions on the speed, direction, timing, and sequence of energy transitions. A conceptual framework integrating the hierarchy of institutions with an historical institutionalist approach is developed and applied to explore transitions in Norway's electricity sector as a case study. Results show that conversion followed by layering emerge as the dominant modes of institutional change in Norway's electricity sector reform, illustrating the importance of alignment between institutions in creating the conditions for large‐scale energy transitions and the importance of boundaries to maintain alignment between levels of institutions. Governments can minimize potential gaps between transition intentions and outcomes through effective conversion and layering of institutional arrangements, but layering challenges emerge when institutional change introduces new actors or energy arenas to existing policy paradigms. [ABSTRACT FROM AUTHOR]

It is crucial to increase the resilience of the global food production and distribution systems against the growing concerns relating to factors that could cause global catastrophic infrastructure losses, such as nuclear war or a worldwide pandemic. Currently, such an event would result in the global loss of industry, including the ability to drill and refine crude oil. In such an event, the existing above-ground reserves of diesel and gasoline are likely to still be intact but would only be able to power the production and transportation of food between 158 days and 481 days with 80% confidence, where the mean is 195 days at current rates. This paper investigates a novel group of interventions in relation to the scenario of providing food under these conditions. It was found that by using a plausible combination of wood gasification, increasing vehicle utilisation rate, and reducing food consumption, the stockpile duration could increase to between 382 days and 1501 days with 80% confidence, where the mean is 757 days. This is an improvement in mean duration by a factor of 3.9. It was discovered that diesel is the limiting fuel in all scenarios due to wood gas only being a partial replacement for diesel fuel and also because of the prevalence of diesel engines in both the agricultural and trucking industries. A sensitivity analysis was completed identifying that reducing food consumption to minimum levels was the most effective method to prolong diesel reserves. The other factors that benefited from extending fuel reserves in terms of their effectiveness are reducing the lag time before gasification devices are installed, increasing the rate at which gasification devices are installed, and increasing the agricultural equipment utilisation rate. [ABSTRACT FROM AUTHOR]

To predict renewable energy sources such as solar power in microgrids more accurately, a hybrid power prediction method is presented in this paper. First, the self-attention mechanism is introduced based on a bidirectional gated recurrent neural network (BiGRU) to explore the time-series characteristics of solar power output and consider the influence of different time nodes on the prediction results. Subsequently, an improved quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the hyperparameters of the combined prediction model. The final proposed LQPSO-BiGRU-self-attention hybrid model can predict solar power more effectively. In addition, considering the coordinated utilization of various energy sources such as electricity, hydrogen, and renewable energy, a multi-objective optimization model that considers both economic and environmental costs was constructed. A two-stage adaptive multiobjective quantum particle swarm optimization algorithm aided by a Lévy flight, named MO-LQPSO, was proposed for the comprehensive optimal scheduling of a multi-energy microgrid system. This algorithm effectively balances the global and local search capabilities and enhances the solution of complex nonlinear problems. The effectiveness and superiority of the proposed scheme are verified through comparative simulations. [ABSTRACT FROM AUTHOR]

With the growing need for renewable energy, wind farms are playing an important role in generating clean power from wind resources. The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency. This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization (PSO) and artificial neural networks (ANNs). The PSO method was used to explore the solution space and develop preliminary turbine layouts, and the ANN model was used to finetune the placements based on the predicted energy generation. The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits. The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches, giving exciting prospects for developing more efficient and sustainable wind farms. The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques. Furthermore, this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency. A notable increase in power generation is observed across various scenarios. The percentage increase in the power generation ranged from approximately 7.7% to 11.1%. Owing to its versatility and adaptability to site-specific conditions, the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

This article addresses the shift of power that occurred in Ottoman Damascus as a result of the establishment of its electricity and tramway network that started operating in 1907. The company, which ought to have been public, in the ownership of Ottoman-Syrian investors, ended up being owned by a Belgian consortium. The central argument in the article is that the Damascene elite families rationally refrained from the risk of establishing the Electricity Company but paid the price of losing control over power and light in the city. An informal network of Ottoman officials and Belgian businessmen, led by the second secretary of Sultan Abdülhamid II, the Damascene İzzet Pasha, orchestrated the buyout. Therefore, Damascus's encounter with its first electric grid, which was naturally complex in the first place, was also subjected to the tensions of outsourcing a local-municipal power to an external network that the Central Ottoman Government imposed. This article is an outcome of research on the electrification of Damascus, based on the papers of the Ottoman Ministry of Public Works from the Ottoman Archive and the company's records from Belgium's National Archive, the Ottoman-Syrian press, and the British and American consulates' archives. [ABSTRACT FROM AUTHOR]