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In the current study, the exergoeconomic analysis of a cogeneration pulp and paper plant under bi-operating modes at different environment temperatures of 290, 295, 305, 310, and 320 K was investigated. Two operating modes of the cogeneration system, i.e., a hybrid operating mode using a power boiler and a recovery boiler and a singular operating mode using a power boiler only, were considered. Natural gas was utilized as the main fuel in the power boiler, while black liquor with heavy fuel oil was employed in the recovery boiler. The total capital investment, total operating, exergy, unit exergy, and exergy destruction costs for each operating mode were evaluated. The results indicated that the total capital investment cost was 3420.27 $/h, and the total capital investment and the operation and maintenance costs in the hybrid and singular modes were 5853.27 and 6226.67 $/h, respectively. As well, the unit exergy cost and the exergy cost of steam introduced by the power boiler were 39.31 $/MWh and 2503.69 $/h, respectively. Where these values for the recovery boiler were 34.69 $/MWh and 554.30 $/h, respectively. Furthermore, the exergy destruction costs at an environment temperature of 320 K for the power and recovery boilers were 1436.6 and 213.63 $/h, respectively. In the hybrid mode, the soda at a 10% concentration was recovered, with a rate of 32 t per hour desired for the cooking process in the pulp mill. In addition, the use of black liquor as a bio-fuel in the recovery boiler saved 14% of the consumption of natural gas. • Exergoeconomic analysis was performed for the pulp and paper industry. • Hybrid and singular operating modes for different ambient temperatures were studied. • Unit exergy cost, exergy cost, and exergy destruction cost were evaluated. • Using black liquor as a bio-fuel in RB saved about 14% of natural gas consumption. [ABSTRACT FROM AUTHOR]

Increasing the conductivity of gas diffusion layers (GDLs) is an important way to improve the output performance of polymer electrolyte membrane fuel cells (PEMFCs). However, the complex porous fiber structures of GDLs significantly enhances the difficulty of quantitatively altering their conductivity which is determined by the carbon fibers and the conduction characteristics between fibers. In addition, the microstructures of various types of GDLs are different. Thus, it is a considerable challenge to explore the conductive mechanisms of these porous materials and optimize their structures to reduce their bulk resistances. In this work, a mathematical graph theory model that applies to the through-plane (T-P) bulk resistance prediction of two types of commonly used GDLs, carbon paper and carbon felt, is established to explain their different micro conduction mechanisms in depth. In addition to the number of fiber contact points, their distribution, as well as the resistance of the carbon fibers, are all important factors affecting the T-P conductivity. Optimizing fiber density and fiber diameter can significantly improve the T-P conductivity of carbon paper. In comparison, making the structure of carbon felt more compact so that the distribution of its contact points in the T-P direction can be more uniform will be more effective for the reduction of its T-P bulk resistance. Meanwhile, the T-P bulk resistance of carbon paper can also be effectively improved by optimizing the content and distribution of the binders. A method to decline the bulk resistance of carbon paper by aggregating the binders in the in-plane (I P) direction is proposed. The simulation results show that it can reduce the T-P bulk resistance of carbon paper by about 19.9% at a compressive stress of 1.5 MPa. This study provides further guidance for optimizing the structural designs of GDLs to optimize their conduction performance. • T-P bulk resistance prediction model suitable for different GDLs is constructed. • Micro conductivity mechanisms of carbon paper and carbon felt are compared. • Increasing fiber density and diameter improves carbon paper's T-P conductivity. • Making carbon felt's structure more compact can optimize its T-P conductivity. • To promote carbon paper's T-P conductivity by optimizing the binders inside. [ABSTRACT FROM AUTHOR]

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

Highlights • Pyrolysis and gasification of different paper-polystyrene mixtures investigated. • TGA results revealed delayed polystyrene decomposition in paper waste presence. • Synergistically doubled gas yield using co-pyrolysis and 15% using co-gasification. • Energy yield via syngas increased by 30% using CO 2 co-gasification of mixtures. Abstract Co-pyrolysis and CO 2 co-gasification of paper and polystyrene blends in different mixture ratios were studied at 1173 K using a laboratory scale tube reactor and thermogravimetric analysis. The chemical composition and yield of the syngas produced was analyzed by a micro gas chromatograph to understand the influence of mixture components. Co-pyrolysis positively impacted the syngas yield exhibiting a synergistic influence on cracking reactions leading to increased gaseous yield having almost double the amounts of hydrogen yield. Co-gasification using CO 2 increased the total gas yield with enhanced synergistic conversion. This effect provided a non-linear impact on the combustible gases in the gaseous yields when compared to the separate gasification of these feedstocks. The synergistic enhancement of co-pyrolysis conversion in paper-polystyrene led to lower char present for CO 2 to react during CO 2 co-gasification that lead to lower CO during the gasification of this blended char residue. The mixtures of paper with polystyrene provided increased product gas yields and enhanced conversion with an increase in polystyrene content. The results showed the effectiveness of producing high energy density syngas from co-gasification which can alleviate material handling issues present in segregation of waste, such as plastics and biomass providing uniform valuable product from diverse waste feedstocks with minimal need for classification. [ABSTRACT FROM AUTHOR]

Hydrothermal treatment of sludge within the pulp and paper industry can have a wide range of possible reactor solid loads for different sludge types. The objectives of this work were to determine the effect of reactor temperature and solid load on the properties of sludge hydrochar and to simulate hydrothermal treatment of sludge within a pulp and paper mill. Laboratory experiments were first performed within reactor temperature and solid load ranges of 180–260 °C and 10–50%, respectively, and the effects of sludge treatment were determined by comparing parallel mill-scale simulations. Based on the results, both reactor temperature and solid load had a statistically significant effect on the solid, ash, carbon and energy yields of sludge hydrochar. Increasing solid load minimized carbon dissolution to the liquid phase and increased the solid and energy yield of the attained hydrochar. According to mill-scale simulations in a 9 m 3 batch reactor, treating primary sludge alone produced an energy surplus of 22–36 GJ through char incineration, decreasing to 12–22 GJ and 3.4–9.1 GJ for mixed and secondary sludge, respectively. Mixing primary and secondary sludge reduced the overall energy surplus by 2–8% compared with treating the sludge streams separately in two reactors. [ABSTRACT FROM AUTHOR]

• This is a case study of a small modular reactor supplying steam and electricity to a pulp and paper mill. • Resulting steam conditions in the mill are analyzed using steady-state and dynamic models. • Models of the paper mill were built in Aspen HYSYS and Dymola, a Modelica Ecosystem. • Integrated SMRs with pulp and paper mills is feasible but may present cost limitations. Small modular reactors (SMRs) are reactor designs producing less than 300 MWe and are generally planned for deployment as multimodule nuclear power plants. The possibility of factory-manufactured, flexibly sized plants expands the opportunities for nuclear power to different communities and industries, including manufacturing plants that currently utilize fossil fuels to produce both steam and electricity. This paper examines the feasibility of coupling a NuScale SMR with a midsize pulp and paper mill in the Southeastern United States. A steady-state mill model was developed in Aspen HYSYS, based on real data from the operation of the mill, and modified it to include the SMR while maintaining steam quality requirements and making as few changes as possible to existing equipment. Dynamic plant models were also developed Dymola to demonstrate possible plant conditions, using three configurations. Preliminary results suggest that, while SMR coupling is physically feasible, its economic feasibility is limited by the differences in steam and electricity demands. Because of limitations in the amount of steam the mill can take from the SMR, sizing the SMR for the plant's steam demand may result in an electricity deficit, or vice versa. Dynamic analyses show that the addition of a thermal storage system could reduce such deficits, but this entails its own challenges. Each plant must determine the best configuration and control scheme for itself, based on its electricity and heat needs, including the peak duration and intensity for both. Ultimately, an implementation of SMRs with manufacturing processes would benefit from partnering with a local utility to purchase excess electricity generated by the SMR. This will help manufacturing facilities meet their environmental and cost-savings goals, in addition to meeting the need for cost-effective baseload power across the United States. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is a high energy consuming and polluting sector, and carbon emissions emitted from this sector are worthy of attention. This article, based upon an analysis of China’s Pulp and Paper Industry (CPPI), provides estimates of each of the following: carbon emissions from energy consumption, pre-treatment sector, combustion of condensed black liquor, and methane emitted from incomplete aerobic digestion during sewage treatment of CPPI. During the study period (2005–2012), total CO 2 emissions ranged from 126.0 Mt to 155.4 Mt. Energy consumption was estimated to be the largest source of carbon emissions, however, due to the application of the local emission inventory rather than the IPCC inventory, energy consumption decreased by 4.7%, a lower percentage than was calculated in a previous study. According to this study’s estimation, the emissions caused by the recovery of biomass energy contributed 26–29% of the total CO 2 emissions. CH 4 generated from sewage treatment accounted for 9–11% of the total carbon emissions. The CO 2 intensity dropped during the study period, which reflected the improvement of energy efficiency in the pulp and paper industry. The outcome of this study provides not only detailed information about CPPI’s carbon emissions, but also a calculation framework for studying carbon emissions from pulp and paper sector in the other regions. It suggests that the local carbon emissions inventory should be used for estimating carbon emissions and to reduce the fossil fuel energy, increase energy recovery from biomass, and that promoting cleaner production is essential to achieve a low carbon development of the pulp and paper industry. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Stepwise pyrolysis at 350 and 500 °C can separate products into two fractions. • First step liquid product was acidic and had high amounts of oxygenated compounds. • Products from the second step had high hydrocarbon contents and low acidity. • Interaction between cellulose and plastics during stepwise pyrolysis was observed. • Test on real wastes and discussion of scaling-up possibilities were presented. Abstract Mixed plastics and papers are two of the main fractions in municipal solid waste which is a critical environmental issue today. Recovering energy and chemicals from this waste stream by pyrolysis is one of the favorable options to achieve a circular economy. While pyrolysis products from plastics are mainly hydrocarbons, pyrolysis products from paper/biomass are highly oxygenated. The different nature of the two pyrolysis products results in different treatments and applications as well as economic values. Therefore, separation of these two products by multi-step pyrolysis based on their different decomposition temperatures could be beneficial for downstream processes to recover materials, chemicals and/or energy. In this work, stepwise pyrolysis of mixed plastics and paper waste was performed in a batch type fixed bed reactor using two different pyrolysis temperatures. Neat plastic materials (polystyrene, polyethylene) and cellulose mixtures were used as starting materials. Then, the same conditions were applied to a mixed plastics and paper residue stream derived from paper recycling process. The condensable products were analyzed by GC/MS. It was found that pyrolysis temperatures during the first and second step of 350 and 500 °C resulted in a better separation of the oxygenated and hydrocarbon condensates than when a lower pyrolysis temperature (300 °C) was used in the first step. The products from the first step were derived from cellulose with some heavy fraction of styrene oligomers, while the products from the second step were mainly hydrocarbons derived from polystyrene and polyethylene. This thus shows that stepwise pyrolysis can separate the products from these materials, although with some degree of overlapping products. Indications of interaction between PS and cellulose during stepwise pyrolysis were observed including an increase in char yield, a decrease in liquid yield from the first temperature step and changes in liquid composition, compared to stepwise pyrolysis of the two materials separately. A longer vapor residence time in the second step was found to help reducing the amount of wax derived from polyethylene. Results from stepwise pyrolysis of a real waste showed that oxygenated and acidic products were concentrated in the liquid from the first step, while the product from the second step contained a high portion of hydrocarbons and had a low acid number. [ABSTRACT FROM AUTHOR]

[Display omitted] • New biocathode including BOD, 4-APA, and BP electrode (BP/4-APA/BOD is suggested. • The biocathode is prepared by electrografting method. • Membraneless paper type EBFCs using this biocathode are fabricated. • This paper type EBFC is well operated even in a condition of living body fluid. • OCV/MPD of this paper type EBFC are 0.612 ± 0.002 V/92.025 ± 0.892 μW cm−2. A new biocathode consisting of bilirubin oxidase (BOD), 4-aminophthalic acid (4-APA) and buckypaper (BP) electrode is suggested for enzymatic biofuel cells (EBFCs). Here, to enhance the electron transfer rate of BOD, electrografting method is imported. When this method and 4-APA are utilized, BP surface has negative charges by the polarity arrangement of 4-APA, forming BP/4-APA structure. With the modification of surface charge, T1 active site within BODs having positive charges are strongly bonded with the negative charges of BP/4-APA in neutral pH condition, while the orientation of T1 site is well aligned by electrostatic interaction. This treatment increases both reactivities of cathodic reaction and EBFC performance. To prove that, the performance of membraneless paper-type EBFCs using the new flexible BP/4-APA/BOD biocathode is investigated. According to the evaluations, their maximum power density and open circuit voltage are 92.025 ± 0.892 μW cm−2 and 0.612 ± 0.002 V with injection of 10 mM glucose which is a condition of living body fluid. Such performances are far better than those of other membraneless EBFCs and are large enough to be utilized as a power source of implantable devices for the human body. [ABSTRACT FROM AUTHOR]

To address the growing need for economically viable sludge processing technologies and associated resource recovery, a novel convective high-velocity pilot cyclone was set-up at the Swedish University of Agricultural Sciences in Umeå, Sweden. In essence this process entails feeding (⩽900 kg h −1 ) a material to a heated high-velocity air flow (approx. 13 × 10 3 m 3 h −1 ) allowing moisture removal at low temperatures. This equipment is expected to improve drying of challenging industrial sludge materials when used as a pretreatment method prior to further processing. Modeling results based on an experimental campaign on recycled paper mill sludge indicated that efficient drying with a specific energy consumption of ⩽1–1.2 kWh kg −1 H 2 O can be achieved with inlet air temperature levels ⩾40 °C coupled with respective feeding capacity of ⩽750 kg h −1. [ABSTRACT FROM AUTHOR]

[Display omitted] • A H 2 O 2 paper fuel cell with hydrogel-aided dual electrolytes has been developed. • High performance is achieved by toning the alkaline-acid dual electrolytes. • Firstly investigate the electrochemical reaction mechanisms via radical analysis. • The fuel cell shows excellent performance of ∼ 10 mW cm−2 and the best durability. Cellulose paper has been employed to develop pumpless microfluidic fuel cells. Meanwhile, hydrogen peroxide as both the fuel and oxidant has attracted attention since it is eco-friendly for producing only water. However, low open-circuit voltage (OCV) and power density of reported H 2 O 2 fuel cells due to the mixed potentials (simultaneous oxidation and reduction at the same electrode) preclude its applications. In this study, a H 2 O 2 paper fuel cell with a gel-aided dual-electrolyte configuration has been proposed for the first time to solve this problem. An ion-conductive hydrogel has been sandwiched between the independent catholyte and anolyte flows to serve as part of the electrolyte. Its OCV has been elevated to 1 V and a high peak power density of 10.2 mW cm−2 has been achieved by toning the electrolytes, which exceeds most reported H 2 O 2 fuel cells. The results demonstrate that alkaline anolyte benefits the cell performance more significantly than acidic catholyte does. The reaction mechanism at each electrode was further studied to provide insights for future development of H 2 O 2 fuel cells: the reduction at the cathode follows the Fenton reaction; as for the oxidation at the anode, it was first revealed that H 2 O 2 oxidation was facilitated by free radicals generated from H 2 O 2 in alkaline media. Besides, the radicals also help to maintain a large voltage difference between the electrodes. Furthermore, this fuel cell shows great durability and can be instantly re-activated upon refueling. This gel-aided dual-electrolyte fuel cell design can pave a way for practical applications of direct H 2 O 2 fuel cells. [ABSTRACT FROM AUTHOR]

Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill. • Pulp and paper mills with electric boilers can supply primary frequency reserves. • Electric boilers can provide up to 50 MW flexibility of 100 MW rating. • An algorithm is developed to optimize the energy & reserve bids of the mill. • Expected cost savings range between 7.4% in summer and 2.3% in winter. [ABSTRACT FROM AUTHOR]

• A paper-based Al-air battery successfully developed for miniwatt applications. • The battery can work with limited electrolyte supply without external pumping. • Low-cost and low-grade Al employed with satisfactory discharge efficiency. • The paper substrate suppresses the Al self-corrosion with alkaline electrolyte. • An efficient and modular stacking strategy for battery pack development. Conventional Al-air battery is not suitable for mini-watt applications due to bulky liquid storage and complex water management. To achieve successful simplification and miniaturization, an innovative paper-based Al-air battery is developed in this study, which employs cellulose paper as electrolyte substrate, low-grade Al foil as anode and carbon paper as cathode. Despite its simple structure and low cost, this battery achieves satisfactory power output together with high efficiency. With alkaline electrolyte, an open-circuit voltage of 1.6 V, a peak power density of 21 mW cm−2 and a specific capacity of 1273 mA h g−1 are obtained. With saline electrolyte, the battery can continuously work at 1 mA cm−2 for 5.6 h with only 3.5 mg Al. In addition, it is found that the paper-based electrolyte delivery can suppress Al corrosion in alkaline environment, enabling the direct utilization of low-grade Al resources. Moreover, the battery performance is improved by optimizing properties of its components, such as cathode catalyst, electrode area ratio and paper substrate thickness. Finally, a 5-cell battery pack is successfully developed, achieving an open-circuit voltage of 7.7 V and a stacking efficiency as high as 97%. By further scaling-up, this battery pack is successfully demonstrated for charging portable electronics. [ABSTRACT FROM AUTHOR]

In an era of increasing energy demands and environmental concerns, optimizing energy consumption within buildings is crucial. Despite the vast improvements in HVAC and lighting systems, plug loads remain an under-studied area for enhancing building energy efficiency. This paper studies smart plug active operating mode detection, plug-level load forecasting, and plug scheduling methodologies. This research leverages a unique dataset from the University of California, San Diego, consisting of readings from over 150 smart plugs in several office buildings for more than a year, notably during the post-Covid era. This dataset is made publicly available. A comprehensive literature review on plug, i.e. , appliances operating mode detection is presented. Novel unsupervised learning approaches are applied to identify plug operating modes. A pipeline integrating the detected modes with forecasting and scheduling is developed, aiming at building energy consumption reduction. Our findings offer valuable insights and promising results into smart plug management for energy-efficient buildings. • A novel year-long dataset of power readings from 169 office smart plugs is introduced. • The first review of plug load mode detection methods and terminology is presented. • The paper proposes an unsupervised method to identify plugs load operating modes. • The proposed smart plug scheduling pipeline reduces energy consumption by 20%. [ABSTRACT FROM AUTHOR]

Innovations for near real-time common European balancing markets are underway to meet the flexibility needs induced by the deployment of renewables and new market agents. Never have markets and real-time network operations been run so closely on a continental scale. Our paper investigates a filtering method for integrating congestion management and near real-time markets. Reinforcement Learning is applied to add the cost of physical delivery to bid prices to advantage/disadvantage bids that reduce/create congestion. We assess the impact of this new method on market welfare and congestion management costs and show that it brings significant efficiency gains compared to no filtering or a baseline filtering methodology. • Real-time common European balancing markets are a new source of congestion. • Filtering is a pre-market congestion management method consisting of bid screening. • A continuous formulation of filtering, that modifies the merit order, is presented. • The cost of physical delivery is added to bid prices using Reinforcement Learning. • The proposed methodology increases net Social Welfare and helps maintain security. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

• Commercial air-laid paper and carbon ink were used for moist power generation. • The voltage of 0.35 V and current of 33.9 μA were generated in the atmosphere (6 cm2). • The power generator was designed for some extreme situations such as in the wild. • Integration of generators could power wearable electronic devices in the atmosphere. Low-grade energy harvesting from the ambient environment promises to relieve the energy shortage in the remote regions or emergency needs. The reported materials employed for low-grade energy harvesting requires costly raw materials and highly technical skills, hindering the applications in isolated regions or after natural disasters. Herein, we report the use of commercial air-laid paper and carbon ink to harvest electrokinetic energy in the capillary flow. The paper-based electrokinetic power generator with size of 6 cm × 1 cm could continuously generate an output voltage of 0.35 V and current of 33.9 μA in the ambient condition (20 °C and 50% Relative Humidity). The output power could maintain for over 120 h. When connected in series and in parallel, the power generator with excellent flexibility could power some common electronic devices in the atmosphere, such as light-emitting diode, electronic calculator and watch. This work offers a rational approach to employing low-cost materials for ambient energy harvesting, thus advancing the application in portable and wearable electronic devices. [ABSTRACT FROM AUTHOR]

It is vital to perform system analysis on integrated biomass gasification in chemical recovery systems in pulp and paper and heat and power plants for polygeneration applications. The proposed integration complements existing pulp and paper and heat and power production systems with production of chemicals such as methane and hydrogen. The potential to introduce gasification-based combined cycles comprising gas turbines and steam turbines to utilize black liquors and wood pellets also merits investigation. To perform such analysis, it is important to first build knowledge on expected synthesis gas composition by gasifying at smaller scale different types of feed stock. In the present paper, the synthesis gas quality from wood pellets gasification has been compared with black liquor gasification by means of numerical simulation as well as through pilot-scale experimental investigations. The experimental results have been correlated into partial least squares models to predict the composition of the synthesis gas produced under different operating conditions. The gas quality prediction models are combined with physical models using a generic open-source modelling language for investigating the dynamic performance of large-scale integrated polygeneration plants. The analysis is further complemented by considering potential gas separation using modern membrane technology for upgrading the synthesis gas with respect to hydrogen content. The experimental data and statistical models presented in this study form an important literature source for future use by the gasification and polygeneration research community on further integrated system analysis. [ABSTRACT FROM AUTHOR]

A comparison between different metal-doped carbonaceous anodes for air-breathing microbial fuel cells (MFCs) has been carried out in this work. In order to do that, the surface of carbon paper anodes were modified with Pt, Au and Ni. The current generated was higher when using these metal-doped anodes, exerting up to 7.4 A m −2 more than when using non-doped ones. Polarization curves results in a great performance of the Ni reaching 2.92 W m −2 at the steady state, followed by 0.99 W m −2 of the Au and 0.52 W m −2 of the Pt. Additionally, from the mathematical fitting of a model to the experimental data of a polarization curve, it was observed that the mechanism that explains the better performance of the metal doped anodes was the reduction of the mass transfer limitations. In this sense, the addition of metal on the anodes increase the threshold current density causing mass transfer limitations, reducing also the significance of the mass transfer limitations when the cells are operated under conditions in which the process is diffusion controlled. [ABSTRACT FROM AUTHOR]

• Nanogel particles for CO 2 capture with lower cycles (303–348 K) are developed. • Carbon paper as supporter shows higher CO 2 capacity, adsorption and desorption rate. • Avrami's fractional order model accurately predicts the CO 2 uptake behaviors. • The CO 2 capture process is governed by film and intraparticle diffusion resistance. • Carbon paper as supporter shows lower sensible heat and desorption activation energy. Amine-containing nanogel particles with a lower cooling and heating cycles (303–348 K), which is beneficial to limit the degradation and volatile of amine, has been developed as promising absorbents for CO 2 capture. It is extremely important to understand the CO 2 capture kinetics and mechanisms for design and operation of corresponding processes. In present work, poly tetra fluoroethylene and carbon paper were used to support the GPs by filtering and loading method. The CO 2 uptake and release performance of poly tetra fluoroethylene and carbon paper supported nanogel particles was measured. It was found that carbon paper as supporter shows higher CO 2 capacity and desorption rate. A comparison of three different kinetic models shows that carbon paper as supporter gives a more enhanced kinetic behavior. Avrami's fractional order model presents the best fit to experimental data. To further investigate the mechanism of CO 2 uptake on absorbents, interparticle diffusion model, intraparticle diffusion model and Boyd's film diffusion model were also applied. Film diffusion resistance governed the mass transfer rate of CO 2 uptake on studied absorbents at the initial CO 2 uptake stages. In the following stages, intra-particle diffusion resistance plays a major role until the equilibrium is reached. The sensible heat of carbon paper as supporter is about 25% lower than that of poly tetra fluoroethylene as supporter. The desorption activation energy obtained from the Arrhenius equation is 18 kJ/mol for carbon paper as supporter, which is 84% lower than that of the typical aqueous MEA solvent. [ABSTRACT FROM AUTHOR]

With the increasing installation of battery energy storage systems, the safety of high-energy-density battery systems has become a growing concern. Developing reliable battery fault diagnosis and fault warning algorithms is essential to ensure the safety of battery systems. After years of development, traditional fault diagnosis techniques based on three-dimensional information of voltage, current and temperature have gradually encountered bottlenecks. It is necessary to adopt a proactive approach by using mulitidimensional information to advance fault diagnosis techniques. This involves integrating advanced sensing technologies, collecting multidimensional data and uncovering subtle changes in battery behavior. This paper delves into the mechanisms and evolutionary paths of battery faults, with a specific focus on the multidimensional observable signals associated with different faults for enhanced safety strategy. Furthermore, the paper provides a comprehensive overview of potential applications of different sensors for multidimensional measurement in battery fault diagnosis. It also explores the future trends and research directions of the next generation of battery fault diagnosis techniques driven by multidimensional data collection and artificial intelligence algorithms. [Display omitted] • Enhanced safety through proactive, multidimensional fault diagnosis techniques. • Integration of advanced sensing tech for precise multidimensional data collection. • Uncovering subtle battery behavior changes for improved fault detection. • Specific focus on multidimensional signals to enhance safety strategies. • Future trends in battery fault diagnosis driven by AI and multidimensional data. [ABSTRACT FROM AUTHOR]

The fluctuation of oil futures price greatly influences the market investment sentiment, which can cause credit risk and capital pressure risk of various financial institutions, and ultimately affect the idiosyncratic risk and systemic risk of financial institutions. This paper adopts MES to measure the systemic risk of Chinese financial institutions and proposes a high-dimensional time-varying parameter spillover network matrix to identify the infection route and source of systemic risk. Finally, the paper investigates the mechanism by that oil futures affect the contagion effect of systemic risk across institutions. The results show that banking financial institutions are the main exporters of risk while insurance is the main absorber, and after the outbreak of COVID-19, the risk contagion capacity of institutions generally increased, the network structure became more complex, and the risk spillover level was pro-cyclical. The empirical results show that the rise of oil futures will promote the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity, and the above process can be transmitted through the stock market, bond market, and foreign exchange market. • Banking institutions are transmitters of risk, while insurance are receivers; • Financial institutions' risk contagion network becomes more complex after COVID-19; • The systemic risk spillover level of Chinese financial institutions has obvious pro-cyclical; • Higher oil futures price promotes the (net) risk spillover capacity of institutions and inhibit the risk absorption capacity; • The above impacts involve three possible transmission mechanisms: the stock market, bond market and foreign exchange market; [ABSTRACT FROM AUTHOR]

The electricity required for air conditioning is soaring worldwide. Absorption chillers represent an alternative to classical vapor compression systems, using heat instead of electricity. However, absorption chillers powered by renewable geothermal heat have received little attention so far. This paper introduces a system using a hot geothermal fluid (typically in the range of 80–110 °C) to produce cooling through a single-effect absorption chiller, and Domestic Hot Water (DHW) through a heat exchanger. It considers a hotel located in Martinique, a French Caribbean island. The electric consumption of every subsystem has been thoroughly estimated. The originality of this paper is twice: i) the system is modelled in TRNSYS software considering dynamic conditions, ii) the system undergoes surrogate modelling and multi-objective optimization to minimize both the cost and the CO 2 content of the delivered thermal energy. Several scenarios are considered, depending upon the geothermal temperature, mass flow rate, well remoteness, and demand size. The studied system appears to be systematically more expensive than a combination of a classical vapor compression chiller and a boiler for DHW. However, it can significantly decrease the CO 2 content of the provided energy, especially in an island where most electricity is generated from fossil fuels. The proximity of the geothermal well and the use of the warm water leaving the absorption generator (here for DHW production) appear to be key factors for system relevance, along with a hotter geothermal fluid (e.g., 110 °C instead of 80 °C). • A geothermal absorption chiller in tropical climate is modelled. • The influence of design parameters on electricity consumption is investigated. • The system undergoes multi-objective optimization. • The well proximity and geothermal temperature are keys to the system relevance. [ABSTRACT FROM AUTHOR]

Electricity theft has emerged as a notable concern for smart grids, as fraudulent users illicitly access electricity from utilities without a contractual agreement or manipulate meter readings to evade bill payments. Consequently, the importance of electricity theft detection (ETD) in preserving the cost-effectiveness of smart grids has increased. In recent years, the utilization of deep learning models in electricity theft detection has surged in popularity, owing to their capacity to capture the periodic patterns within time-series data on electricity consumption. Nonetheless, deep learning techniques for electricity theft detection encounter several challenges, such as data imbalance, higher false positive rates, and the determination of periodic cycle timings. In this paper, we present an innovative approach to tackle these challenges: the Multi-Objective Evolutionary Hybrid Deep Learning (MOE-HDL) architecture, specifically designed to identify electricity theft. In MOE-HDL techniques, first, a novel multi-objective evolutionary algorithm based on ranking scheme (RMOE) is proposed to optimize architectural model parameters and the detection window size, with precision and recall as conflicting objectives. Next, an optimized dynamic detection window is generated to effectively capture the periodic patterns in consumption data and convert the one-dimensional data into a grid structure. Finally, a hybrid deep learning model is designed with the help of optimized model parameters to identify abnormal patterns in electricity consumption data. We conducted a comprehensive set of experiments, comparing our approach with eight baseline methods for electricity theft detection. The experimental results demonstrate that the proposed MOE-HDL approach outperforms these alternatives in terms of performance. • In this paper, a novel multi-objective evolutionary hybrid deep learning approach was developed for theft detection in smart grids. • Precision and recall are two conflicting yet effective objective functions designed to optimize the model parameters and detection window. • A dynamic detection window is obtained through optimization to transform electricity consumption data. • The hybrid deep learning model is designed according to the model parameters obtained through evolutionary multi-objective optimization. [ABSTRACT FROM AUTHOR]

CaO-CaCO 3 thermochemical energy storage is a promising technology for solar energy utilization and storage. Alkaline papermaking waste (APW) from paper mills, which is mainly composed of CaCO 3. Herein, TiO 2 /MnFe 2 O 4 co-modified APW was synthesized. The energy storage capacity of TiO 2 /MnFe 2 O 4 co-modified APW was studied during CaO-CaCO 3 thermochemical energy storage cycles in a duel fixed bed reactor. TiO 2 /MnFe 2 O 4 co-modified APW mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. The introductions of TiO 2 and MnFe 2 O 4 significantly improve the cyclic stability and optical absorption property of APW in 30 cycles. The co-modified APW (the mass ratio for CaO:TiO 2 :MnFe 2 O 4 = 100:5:5) possesses high sintering resistance, energy storage, and optical absorption capacities in 30 cycles. The energy storage conversion and capacity of the co-modified APW are 0.72 and 2288.57 kJ/kg after 30 cycles, respectively. The optical absorptance of the co-modified APW reaches 49%, which is 9.25 times as high as that of APW. CaTiO 3 and CaMnO 3 effectively mitigate the sintering of CaO grains. CaMnO 3 and Ca 2 Fe 2 O 5 significantly increase the optical absorptance of APW. The co-modified APW possesses stronger basicity and more oxygen vacancies, which is favourable for the carbonation reaction. Thus, TiO 2 /MnFe 2 O 4 co-modified APW is a good energy storage material. • TiO 2 /MnFe 2 O 4 co-modified papermaking waste is used for CaO-CaCO 3 energy storage. • The composite mainly consists of CaO, MgO, CaTiO 3 , CaMnO 3 , and Ca 2 Fe 2 O 5. • The composite has high and stable energy storage and optical absorption capacities. • The energy storage density of the composite is 2288.57 kJ/kg after 30 cycles. • The composite has high sintering resistance, stronger basicity, and oxygen vacancy. [ABSTRACT FROM AUTHOR]

In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low-carbon transformation of the power sector. [Display omitted] • Design a cooperation mode of new energy power stations and shared energy storage. • Divid the shared energy storage into physical energy storage and virtual energy storage. • Propose a two-stage robust optimization model with improved uncertainty interval. • Construct an entropy weight modified Shapley value-based benefit allocation strategy. • Select a new energy base as the simulation system for verifying the effectiveness and applicability. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan. • The scheduling model of VPP consider capacity degradation of ESSs, carbon trading, and DR strategies. • A lease mechanism for coal-fired units based on the combination of carbon credits and prices is proposed. • This paper proposes ISBDR strategies for reduceable loads. • The ESSs capacity degradation model adopted takes into account DOD and SOC. • A multi-time scale scheduling strategy of VPP is adopted to deal with multiple uncertainties. [ABSTRACT FROM AUTHOR]

Access to decentralized energy solutions is critical for the development of rural communities in Sub-Saharan Africa (SSA). One form of decentralized energy technology, agrivoltaics (AV), has gained attention in the last decade as a means to tackle energy poverty in off-grid communities. The success of renewable energy (RE) projects of any kind, however, depends on existing challenges and risks that may be faced at different stages. Therefore, the aim of this paper is to identify and analyze the main risk factors when implementing agrivoltaics on a community level in SSA. Based on the PESTLE framework, the paper develops a first risk classification system for international agrivoltaic projects in rural farming communities in SSA. By making use of a mixed-methods approach, the research enables the prioritization of potential risks in terms of their occurrence and significance; furthermore, it discusses mitigation strategies. Results indicate that there are various multifaceted risks associated with agrivoltaics, primarily including financial challenges and regulatory barriers. In addition, the study emphasizes the importance of stakeholder involvement as well as a need for long-term community engagement and capacity building to ensure a successful implementation and sustainability of agrivoltaic projects. • Development of the first risk classification system for international agrivoltaic projects in rural communities in SSA. • A mixed-methods approach was used for identifying, classifying and rating potential risks. • Financial risks and regulatory barriers appear to be highly critical. • In addition, stakeholder involvement and long-term community engagement were considered essential. [ABSTRACT FROM AUTHOR]

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper. • A network partition method based on affinity propagation algorithm considering the photovoltaic uncertainty is proposed. • The traditional probability density function (PDF) of PV was updated by the random simulation method. • LinDistflow equation is used to achieve the conversion of the active power loss minimization model. • The ADMM algorithm with penalty parameter adaptive control is used to realize the distributed control of the ADN system. •.The voltage/var control schemes of OLTC, CBs and PV inverters are presented through multi-time scale scheduling. [ABSTRACT FROM AUTHOR]

With the increasing installed capacity of energy storage and the rapid accelerating process of electricity marketization, grid-side independent energy storage are beginning to generate profit by participating in the ancillary service market and reducing the strain on the grid. Although energy storage are currently involved in only one auxiliary service, their low utilization and low revenue can make it difficult to recover costs quickly. A method is presented in this article for optimizing peak modulation (PM) and optimizing frequency modulation (FM) in the auxiliary services market by dynamically partitioning independent energy storage partitions. This paper focuses on developing a method for secondary power allocation that is combined with two-level nested optimization. For two different markets and two different methods, separate power allocations are required. The outer layer optimization depends on the inner layer optimization results to produce the optimal primary allocated power, and the inner layer optimization is constrained by the outer layer optimization results to produce the optimal secondary allocated power. Additionally, the method can not only be applied to fixed type energy storage, but also to pure PM stations and pure FM stations; this paper also proposes an improved method for energy storage containing two batteries and providing two auxiliary services. According to the results of the study, the proposed strategy has a better economic value as well as better engineering value as compared with a single auxiliary service and two different coordination methods. • Proposes a twice power allocation combined with a two-layer nested optimization method. • Proposed a method of prioritizing participation in different ancillary service markets. • The method considers multiple influencing factors at different time scales. • Proposed a method of prioritizing participation in different ancillary service markets. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study on power control strategies for Modular-Gravity Energy Storage (M-GES), a novel, high-performance, large-scale energy storage technology with significant research and application potential. Addressing the current research gap in M-GES power control technology, we propose two corresponding compensation modes and several power control strategies for Hybrid M-GES and M-GES. The effectiveness of these strategies is validated through simulations on the MATLAB/Simulink platform, including tests using sinusoidal power and feasibility verification based on a natural California load curve. Additionally, we analyze the normal and abnormal operating conditions of M-GES. In conclusion, the characteristics of the proposed control strategies are summarized, providing a guideline for further research. [Display omitted] • Modular-gravity energy storage (M-GES) power control system studied. • Two compensation modes and four control strategies are systematically studied and validated. • The center switching method with hysteresis control (CSM-H) excels in continuous compensation mode. • The center switching method with hysteresis control and dead zone (CSM-HD) excels in overlap compensation mode. • Based on the power control system proposed in this paper, the M-GES plant performs highly in natural power systems. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

With deepening decarbonization and increased Renewable Energy Sources (RESs) integration, the power system's inertia has declined, affecting the network's ability to balance power at the distribution level. Concurrently, the proliferation of prosumers presents a regulatory opportunity for Distribution System Operators (DSOs), despite the complexity introduced by their high number and varied behaviors. This paper introduces a new co-scheduling model optimizing prosumers' capacities through Virtual Power Plants (VPPs) in local networks, enhancing DSO oversight and facilitating prosumer participation in regulation markets. The proposed model concurrently schedules energy provision alongside voltage and frequency regulation capacities. Recognizing prosumers' behavioral uncertainties, Data-Driven Distributionally Robust Optimization (DDRO) is employed to ensure adequate capacity for VPP engagement. Importantly, the paper outlines a mechanism allowing DSOs to partner with multiple privately-owned VPPs, ensuring privacy through an adaptive Alternative Direction Method of Multipliers (ADMM) method. This method avoids the exchange of sensitive information, ensuring confidentiality and scalability. Consequently, VPPs can proficiently manage scheduling and communicate their regulation capacities. The operator then dispatches control signals based on regulation needs and network flow. Results from the IEEE 33 bus test system confirm the model's efficacy in enhancing voltage support and frequency regulation, and generating revenue for both VPPs and prosumers. • This paper proposes a new co-optimization strategy for distribution-level VPPs. • A distributed coordination approach between DSO and VPPs is presented. • An adaptive consensus ADMM is developed to model the communications. • The uncertain nature of prosumer behavior is addressed by a DDRO model. • The effectiveness of the developed approaches is extensively illustrated. [ABSTRACT FROM AUTHOR]

Timely monitoring of abnormal vibration of machinery in highly harsh environments is essential to ensure the safe operation of mechanical systems. This paper uses a self-charging power module to harvest energy to provide sustainable power for monitoring multidirectional ultra-low frequency mechanical vibrations. The power generation unit consists mainly of a spherical electromagnetic triboelectric hybrid nanogenerator (SETE-HNG). It is equipped with advanced sensing functions to support deep learning to identify signals with different directions of vibration, different frequencies, and different amplitudes of vibration, thereby enhancing the high-precision perception function. The accuracy of the prediction results is as high as 98.6622%, 100%, and 99.3333%, respectively. A Power Management Circuitry (PMC) has been meticulously crafted to maximize the utilization of vibrational energy. It efficiently charges a 40 mAh lithium polymer battery to 3.3 V in just 26 min, all without the requirement of an external power source. This advancement facilitates self-powered Global Positioning System (GPS) tracking of vibrational signals. Moreover, the stored energy is harnessed to energize a microcontroller and a low-power Bluetooth module. This enables real-time monitoring of mechanical vibrations via a mobile phone. The design presented in this paper is a testament to the potential of self-powered multidirectional mechanical vibration monitoring, contributing significantly to the safety and efficiency of mechanical systems. The design in this paper enables self-powered monitoring of abnormal vibrations in mechanical systems in extremely harsh environments, thus ensuring the safe operation of equipment. [Display omitted] • Large-scale vibration energy harvesting provides an innovative and efficient method. • SETE-HNG has multidirectional ultra-low frequency mechanical vibration monitoring and energy harvesting functions. • A power management circuit is designed to store energy efficiently. [ABSTRACT FROM AUTHOR]

Thermal networks require thermal energy storage (TES) provisions for balancing thermal energy sources with variable consumer demand. Harvesting ice is an economical option for latent heat TES systems in cooling networks given the wide availability of the storage medium. This paper presents an artificial intelligence (AI) based model to monitor the state-of-charge (SoC) and the outlet temperature of the heat transfer fluid (T o) of an ice tank under fluctuating operating conditions. The AI model is a non-linear autoregressive network with exogenous inputs (NARX) that was trained and tested with datasets obtained from experimental measurements of a practical ice tank and a physics-based model of the tank. The NARX model was sensitised with physics-informed attributes to recognise different heating and cooling zones. The model exhibits a high accuracy in predicting the operating conditions of the ice tank when benchmarked against both experimental measurements of a practical tank and outputs from the physics-based model. For instance, it achieves R 2 values of 0.9943 and 0.9842 for SoC and T o , with root mean square errors of 1.73% for SoC and 0.3161°C for T o. The NARX model is 86% faster than its physics-based counterpart and its implementation requires limited computational resources—making it suitable as a standalone estimator for the TES operation and the accelerated simulation of energy systems containing latent heat TES units. Furthermore, given the limited availability of NARX models in open-source libraries, the presented NARX model and relevant datasets have been made available alongside this paper to contribute to open-science in energy research and the broader AI community. [Display omitted] • The state of a TES unit is continuously quantified with a non-intrusive NARX model. • The NARX model trained on physics-based data replicates the real system's response. • Heating zone-wise segregation of prediction tasks boosts the model's accuracy. • The NARX model is 86% faster than a physics-based model built for fast computation. • Limited public availability of a NARX model is addressed by code and data sharing. [ABSTRACT FROM AUTHOR]

Extant research has highlighted the pros and cons of the use of (a) competition and (b) positive (peer) pressure as two mechanisms for invoking peer effects to bring about long-term behavior change. However, these mechanisms have been studied separately and in different contexts. In this paper, we compare the differential impacts of competition versus positive pressure-induced pro-environmental behavior change in the same target population. This study spans a seven-year horizon between 2013 and 2019 (pre-COVID) during which the workforce of about 2500 employees in the Stephan P. Clark Government Center in Miami, Florida, was split into four groups - one control and three treatment groups. Each treatment group was subjected to (a) a healthy inter-group competition through tracking, benchmarking, and comparing monthly group level electricity saving, and (b) positive pressure through the appointment of champions who encouraged pro-environmental behavior among their group members. Studying these mechanisms concurrently in the same population allows for a nuanced comparison of their differential impacts on pro-environmental behavior change. • In this paper, we compare the differential impacts of competition-induced spillover and supervision-induced peer pressure in affecting pro-environmental behavior change in the same target over a seven-year analysis horizon. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • The study spans 2013-2019 during which the workforce of about 2500 employees in a building was split into four groups (one control and three treatment) and was subjected to both peer pressure and healthy competition. • These results have implications for the design of pro-environment campaigns that could leverage peer effects through fostering an environment of intragroup pride and healthy inter-group rivalry sprinkled with intermittent supervisory interventions for making long-term impacts. [ABSTRACT FROM AUTHOR]

The evaluation of communities' vulnerability to prolonged power outages offers valuable insights for prioritizing improvements in infrastructure resilience, thereby alleviating societal consequences. This study proposes a data-driven approach aiming at developing a Social Vulnerability Index (SoVI) to prolonged power outages leveraging three county-level datasets in California including (1) demographic features, (2) power outage factors, and (3) backup power factors. Furthermore, the study conducts a sensitivity analysis on three distinct datasets under two scenarios (Scenario 1: the current SoVI in 2022, Scenario 2: the prediction of SoVI in the year 2030). The results of Scenario 1 indicate that the counties with more affected customers, the number of power outages, and less education attainment tend to be more vulnerable to power outages in 2022. Scenario 1 reveals that the number of affected customers and power outages are the primary features influencing around 29% and 18% of counties, while educational attainment, public Electric Vehicles (EVs) chargers, and homes with rooftop photovoltaic (PV) substantially impact approximately 32%, 11%, and 8% of counties, respectively. However, in Scenario 2, crucial factors affecting the anticipated SoVI in 2030 include public EV chargers, houses with rooftop PV, power outages, and adults living alone. In contrast to Scenario 1, the prevalence of adults living alone has emerged as a notable factor impacting SoVI in 2030, while both scenarios underscore the pivotal role of EV chargers in influencing SoVI concerning power outages. The proposed SoVI facilitates informed policy decisions and infrastructure improvements in energy resilience, resource allocation, and disaster preparedness, contributing valuable insights for targeted interventions in these domains. • A data-driven approach was proposed to quantify the social vulnerability index (SoVI) to power outages. • This paper applies Principal Component Analysis (PCA) to identify the most correlated features from county-level datasets. • The paper conducts a sensitivity analysis to predict SoVI in2030 and evaluate how counties' dynamics change over time. • This paper provides insights into how policymakers can reduce the social vulnerability to power outages in California. [ABSTRACT FROM AUTHOR]

The carbon dioxide (CO 2) emissions have reached their highest levels since measurements began in recent years. This is widely regarded as one of the primary factors contributing to the increasing frequency of extreme climate events. Consequently, accurate and reliable CO 2 emission data becomes the benchmark in shaping policies and setting targets aimed at reducing emissions, which are essential for carbon emission-related research. Among all industries, the electric power sector takes the highest proportion of CO 2 emissions, making the carbon emission accounting for this sector a crucial component of overall carbon emissions accounting. Therefore, the objective of this paper is to provide a comprehensive review of the applied technologies and research progress in electricity carbon emissions accounting. The paper begins by introducing the fundamental concept of electricity carbon emission accounting. Electricity carbon emissions are categorized into two types: direct carbon emissions from electricity production and indirect carbon emissions from electricity consumption. Then, the current applied approaches for accounting for these two types of emissions are reviewed separately. After analyzing the shortages of existing applied carbon emission accounting approaches, the paper proceeds to review the research on the improved approaches of direct and indirect carbon emission accounting. Finally, the paper outlines the future development prospects of carbon emission accounting technology. • Application status of carbon emission accounting approaches in the electric power industry is analyzed. • Direct carbon emission accounting approaches based on big data technologies are introduced. • Research exploration in electricity indirect carbon emission accounting is reviewed and compared. [ABSTRACT FROM AUTHOR]

The medium- and long-term operation strategy of the hybrid power system needs to adapt to the continuous changes in the market. This paper presented a medium-term (monthly) operational simulation model and nested it into a long-term (yearly) optimization model. It was solved by the genetic algorithm (GA) to maximize yearly revenue. The planned delivered-electricity to power process constraints were present to improve the optimization efficiency. Using penalty price factor (α) and threshold of exempted punishment for the supply shortage δ to characterize the punishment for supply shortage risk. This framework was applied for a new hybrid power system so-called LCHES-WP (large-scale cascade hydropower energy storage system, wind power, PV power). It can participate in the electricity trading market with a bundled output pattern. The real cascade hydropower stations in Qinghai Province, China, are selected as the case study. In the baseline scenario, the current time of use (TOU) electricity price and punishment level (α = 0.05, δ = 5%) were considered. It reveals that (1) the revenue (baseline scenario, 6.15351 billion CNY) in the bundled output pattern is higher by 45.6% than in the separate output pattern. (2) the economic and technical indicators are sensitive to α , but not to δ. α increases make the punishment stricter, supply shortage risk was significantly reduced due to the probability of the shortage event, the maximum monthly supply shortage ratio and punishment electricity are reduced by up to 22.5 percentage points, 26.7 percentage points, and 1880.1 GWh, respectively. The yearly revenue is reduced by up to 4.7%. the reason is the yearly curtailment is increased and the penalty amount does not change dramatically with the changes in punishment. (3) The α = 0.6 and α = 1.8 can be regarded as demarcation point and upper boundary point for the case of hybrid power system, respectively. It is proposed that when planning a capacity scheme, stakeholders should consider that the demarcation point is higher than the current α. In general, this paper provides a reference for the medium- and long-term operation optimization of the LCHES-WP hybrid power system in the future. • Electricity settlement rule in medium and long term electricity trading is considered. • The monthly operational simulation model was nested in the yearly optimization model. • Bundled output pattern can rise by 45.6% of revenue than the separate output pattern. • The supply shortage risk is significantly reduced by increasing the punishment level. • The penalty amount does not change obviously with the changes in the punishment level. [ABSTRACT FROM AUTHOR]

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]

In the recent Comment on our paper ‘Life cycle environmental impacts of UK shale gas’ ( Applied Energy 134 (2014) 506–518), Westaway et al. ( Applied Energy , available online 20th March 2015) allege that we exaggerated the potential impacts of shale gas extraction in the UK. They first take an issue with our inclusion of worst case scenarios despite our clear declaration in several places in the paper that this is indicative of what could happen within the confines of a very ill-defined and highly variable future reality. Secondly, Westaway et al. claim that key assumptions in the modelling reflect illegal practices that would not occur, when in fact this is an exaggeration of the legal situation and these practices are still viable. This rebuttal addresses some of the claims made by Westaway et al. while welcoming further open and impartial discourse in this area. [ABSTRACT FROM AUTHOR]

• A digital hydrostatic drive solution for large utility wind turbine was proposed. • Two digital encoding schemes for a 2.5 MW hydrostatic wind turbine were developed. • A digital hydrostatic wind turbine control based on kw2 control law was proposed. • Digital hydrostatic solution improves energy production of hydraulic wind turbine. Gearbox failure is one of the major factors causing the increased downtime in modern wind turbines. This increases the turbine maintenance cost and therefore the turbine cost of energy. A hydrostatic transmission not only improves the turbine reliability through its "soft" transmission, but also eliminates the use of power converter through its continuous variable transmission function. A hydrostatic wind turbine usually employs a fixed displacement pump to drive a variable displacement motor. The variable displacement motor runs at partial displacement when the wind speed is below the rated wind speed, leading to low drivetrain efficiency. Moreover, large variable displacement motors for large utility wind turbine are not commercially available. Therefore in this paper a digital hydrostatic transmission solution for large utility wind turbine has been proposed. The large variable displacement motor was replaced by combining several fixed displacement motors and a small variable displacement motor with some digital encoding scheme. Two encoding schemes have been proposed for digital hydrostatic transmission. The modeling and design of the digital hydrostatic wind turbine were presented. A hydrostatic wind turbine control based on kw2 control law has been proposed. A dynamic simulation model of the digital hydrostatic wind turbine has been developed. The proposed digital hydrostatic drive solution has been compared with a conventional hydrostatic solution in a commercial 2.5 MW wind turbine. Simulation studies verified the feasibility and the engineering practice of the proposed digital hydrostatic drive solution. [ABSTRACT FROM AUTHOR]

• Digitalization become a new driving force to improve energy-saving and emission reduction performance (EEP). • The relationship between digitalization and EEP has spatial and temporal heterogeneity. • There is a synergy between digitalization and industrialization. • Some constructive policy suggestions are proposed from the perspective of digital development. In the digital era, enterprises actively use digital technology to enable resource conservation and pollution reduction, which has important practical significance. However, the existing research has not fully clarified the link between digitalization and energy-saving and emission reduction. This paper measures the digital development level and energy-saving and emission reduction performance (EEP) of 271 Chinese cities from 2003 to 2019 and empirically explores the impact of digitalization on EEP. The empirical results are: (1) digitalization can significantly improve EEP in Chinese cities, and become a new driving force to save energy and reduce emissions in the new era. After solving the endogeneity problems and replacing the explained variables, the conclusion remains robust. (2) The relationship between digitalization and EEP has significant spatiotemporal heterogeneity. Digitalization helps to promote EEP in China's eastern cities, but it has not shown a positive effect in western cities. The positive effect of digitalization began to emerge after 2011. (3) There is a synergy between digitalization and industrialization, which enables cities to save energy and reduce emissions. This study helps clarify the relationship between digitalization and EEP, which provides important insights into how digitalization can empower cities' energy-saving and emission reduction. [ABSTRACT FROM AUTHOR]

Climate policies such as carbon tax and green finance policy are critical measures to promote low-carbon transition worldwide. These policies are, however, likely to interact with each other and create unintentional consequences. This paper adopts a stock-flow consistent model to theoretically explore the effects of policy interactions. Specifically, commercial banks, as the key credit creators and critical players in providing source of finance, are included. Based on the numerical simulations of the dynamic model, we find that both carbon tax and green finance policies render positive impacts on low-carbon transition. In addition, green credit incentives, if imposed simultaneously with carbon tax, can enhance the positive effects. Although low-carbon transition could generate higher net government revenues, our results show that banking sector tends to expose to higher financial risks when firms' debt-equity ratio rises. Further analysis shows that subsidies on green loans can support green transition and bring positive consequences to debt rollover and social welfare, indicating the needs for active government intervention to improve policy effectiveness. The framework proposed in this paper provides a useful way to understand policy dilemmas in pursuing low-carbon transition and give important implications to form optimal policy mix. • A stock-flow consistent model is developed to examine climate policy interactions. • We show that climate policy-mix can generate both positive and negative effects. • Green finance policies can enhance green investment and induce higher financial risks. • Active government intervention such as green subsidies can ease financial distress and improve policy effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers an active distribution network (ADN) that can benefit from price-responsive demands, inverter-based photovoltaic (PV) generating units, and battery energy storage systems (BESSs) to participate in the real-time energy market. However, a challenging issue arises due to the coupling of energy offerings in the market and Volt-Var schedules of system devices. This paper develops an integrated framework of energy management system and Volt-Var optimization (EMS-VVO) in the ADN. This application is in bidirectional communication with the energy market and electricity assets. In the first stage and some minutes before each hour, the EMS-VVO unit makes the offering decisions in the energy market, as well as the schedules of demands and slow-acting devices (i.e., on-load tap changers (OLTCs) and capacitors) under uncertainties in the market prices, PV power production, and power loads. In the second stage, when the actual values of available PV productions and the updated market prices are known during the power delivery, this application controls the voltage in minutes by dispatching the fast-acting devices (i.e., PV generating units and BESSs) throughout the distribution network in minutes. A number of comparative out-of-sample simulations through a 69-node test system demonstrate how the offering and controlling decisions of the ADN can be modified under different PV penetration levels, voltage violation penalties, demand flexibility levels, and reactive power capabilities of fast-acting devices. • Scheduling model couples energy management system and Volt-Var optimization. • ADN profitably offers in real-time energy market through the scheduling model. • Voltage violation decreases by a higher penalty factor at the expense of lower profit. • Voltage violation is more crucial without reactive power support of fast-acting devices. • More flexible demands can result in a higher profit from offering in the energy market. [ABSTRACT FROM AUTHOR]

Blending hydrogen into the existing natural gas pipeline network is regarded as the most potential transportation mode on the utility-scale. However, the impact of hydrogen on the operation performance of the pipeline remained unclear. This paper analyzes the economic and environmental performance of natural gas pipeline under different hydrogen blending ratios and different hydraulic boundary conditions. In order to reduce the operation costs and carbon emissions in each condition, this paper establish a non-linear optimization model to get the new operation plan of the pipeline system. Further, in order to characterize different boundary conditions of the system, four scenarios, namely, the baseline scenario (BS), the setpoint of flow rate scenario (SF), the setpoint of pressure (SP), and the setpoint of heat flow rate (SH) are proposed in this paper. Two studied cases demonstrated that: (1) the original compressor for natural gas can adapt to a hydrogen blending without change the type of compressor. (2) The optimization model has significant potential in reducing the economic cost and carbon emissions of the system with an average decrease of 11.48%. (3) For every 1% of hydrogen added, the annual operating cost of the system is reduced by $0.73 million (3.29%) in SF, 0.017 million (0.08%) in SP, or increased by $1.67 million (7.50%) in SH. Moreover, the annual carbon emission is reduced by 0.38 kt (3.53%) in SF, 0.047 kt (0.44%) in SP, or increased by 0.76 kt (7.14%) in SH. However, when the hydrogen mixing ratio is more than 8%, the contrary trend may occur. (4) The maximum blending ratio at different points of the multi-source pipeline network are physically interdependent. The paper proposed an optimization model for the 2-E analysis of the pipeline and can provide theoretical guidance for the further application of this transportation mode. • Hydraulic impact of hydrogen on natural gas pipeline is estimated. • Factors that constrain further hydrogen blending are analyzed. • A power optimization model is proposed for the given hydrogen blending ratio. • Pareto set of hydrogen blending are obtained for multi-source pipeline. [ABSTRACT FROM AUTHOR]

With the implementation of energy saving and carbon reduction, the quantitative analysis of solar energy spectral characteristics has been paid more and more attention. Currently, the international test standards provide a spectral distribution with a large span between different wavebands, which makes it difficult to meet the needs of special spectral wavebands. In this paper, based on the principle of spectral splitting, the spectral distribution of solar radiation models (SDSR models) is proposed, and the differences in the spectral distributions of natural outdoor and artificial indoor light sources are evaluated. Firstly, the differences in PV module output power between the outdoor light sources and indoor artificial light sources are assessed in terms of inclination angle and solar radiation, respectively. Secondly, based on that difference, the influencing factors of the spectral distribution of solar radiation are investigated, and the SDSR models of different wavelengths is established based on spectral analysis results. Finally, based on the established SDSR models, the spectral distribution modified models (SDM models) for different inclination angles under artificial light sources is proposed. By comparing the calculated values of the SDM models with the measured data, the calculated values of the model are close to the measured data at different incidence angles. The SDM models proposed in this paper shortens the gap between the spectral distribution of the artificial light source and the outdoor natural light source, and reveal the influence mechanism of the difference in the output power of indoor and outdoor PV modules from the perspective of the spectral distribution. The results can provide guidance for the spectral distribution of solar radiation and the quantitative modification of the artificial light source. • The difference in the output power of PV modules between solar radiation and artificial light sources is evaluated. • The effect of inclination angle and orientation on the spectral distribution of solar radiation is investigated. • The spectral distribution of solar radiation model (SDSR model) at different wavelengths is developed. • The spectral modification models for different wavelengths of of artificial light sources are developed. [ABSTRACT FROM AUTHOR]