Search

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]

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]

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]

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]

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]

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]

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]

• 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]

[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]

• 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]

• 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]

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]

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]

• 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]

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]

Nanowired α-MnO 2 material supported on carbon fiber paper (α-MnO 2 /CFP) is synthesized using a facile hydrothermal method for supercapacitor electrode. The α-MnO 2 /CFP material is characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and BET for its composition, structure/morphology, and BET surface area. The electrochemical properties of the α-MnO 2 /CFP electrode are studied by cyclic voltammetry (CV), galvanostatic charge/discharge and electrochemical impedance spectroscopy (EIS) in 1 M Na 2 SO 4 solution. The porous α-MnO 2 /CFP material electrode shows a high specific capacitance of 251 F g −1 at the current density of 1 A g −1 , and also exhibits a good rate capability and an excellent cycling stability. It is testified that even after 3000 cycles, the capacitance retention of the porous α-MnO 2 /CFP material electrode can still maintain over 98.9% of the capacitance at the high current density of 4 A g −1 , demonstrating its excellent cycling stability. The high electrochemical performance of the porous α-MnO 2 /CFP material nanowire electrode may be attributed to the 2 × 2 tunnel structure, high active surface area, and high porosity of the of α-MnO 2 material. [ABSTRACT FROM AUTHOR]

The collection of sawmill residues is an important logistic activity for the pulp and paper industry, which uses the biomass as a source of energy. We study a vehicle routing problem for a network composed of a single depot and several sawmills. The sawmills serve as potential suppliers of biomass residues to the depot, which in turn processes and distributes the residues to the pulp and paper mills. This problem consists of identifying the best daily routing schedule for a fixed number of trucks. The objective is to maximize the ratio of energy returned on energy invested, while satisfying a minimum daily amount of dried biomass residues. There are several random components in the problem, including the availability and moisture content of the biomass residues. We use a combination of scenario analysis and heuristics to solve this stochastic vehicle routing problem. A performance comparison of the proposed method reveals an estimated daily energy savings of 6 GJ over the benchmark method. [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]

In response to the obstacles posed by finite resources and environmental issues, Sarawak has transitioned its focus towards sustainable and green energy to establish a resilient and eco-friendly energy landscape. Generally, Sarawak is blessed with abundant green energy resources; however, constructive green energy integration methods were absent. Proper green energy integration is necessary due to its intermittent properties and geographical dispersion. Therefore, the paper proposes a novel methodology using Geographical Information System tools incorporating geographical databases, fuzzy logic operations and Traveling Salesman Problem-Binary Integer Programming algorithms to integrate green energies focusing on optimal power line routing design. The methodology begins with clustering the green energies based on geographical divisions. Then, it considers three influential factors (distance, elevation difference, and average ground flash density) and transforms them into matrix data for each cluster. Fuzzy logic operations optimize the trade-off among these factors and form fuzzy values. Following this, the Traveling Salesman Problem-Binary Integer Programming formulation creates pairs of green energy, distance vectors, equality constraints, and binary bounds. The optimization process constructs and eliminates multiple subtours to generate an optimal single loop with minimal value, representing the optimal power line routing design. Rigorous analyses, comparison, and validation demonstrate that the proposed method consistently outperforms ordinary Traveling Salesman Problem-Binary Integer Programming, ranking top 1 across all clusters. Further evaluation against the state-of-the-art fuzzy Traveling Salesman Problem algorithms reveals that the proposed model secures the lowest fuzzy values with extremely low computation times across all clusters. Furthermore, the paper presents an innovative way to consider the ground flash density factor in green energy integration. The comprehensive algorithms and coding provided are valuable assets for researchers and investors to explore and conduct in-depth research on this prospect. Finally, this paper provides valuable directions for regional development, especially in harnessing and integrating green energies to boost economic and state infrastructure. • Innovative and novel methodology for optimal green energy integration. • Clustering green energy locations with optimal power line routing design. • Incorporation of ground flash density for enhanced green energy efficiency. • Proposed methods VS ordinary TSP-BIP and other fuzzy TSP algorithms. • Valuable complete algorithms for research, investment, and regional development. [ABSTRACT FROM AUTHOR]

This paper focuses on operating a microgrid when uncertain islanding events and net load are stochastic, from the perspective of a microgrid operator. To derive effective and reliable decisions that can adapt when these uncertain factors sequentially reveal in a given planning horizon, multistage stochastic optimization models that address the dynamics and probabilistic nature of uncertainty can be used. However, since they suffer from the curse of dimensionality in that the number of scenarios needed grows exponentially with regard to the number of time periods, it is not practical to use the models directly. To overcome this drawback, this paper proposes scalable optimization approaches that efficiently derive good-quality solutions that are adaptable to sequential realization. In particular, integrated optimization models are presented to deal with both uncertain factors based on the combination of the proposed models for each of the two, which is scalable compared to the standard multistage model used in the literature. Numerical experiments demonstrate that practical-sized instances can be efficiently solved using the proposed models, whereas the standard multistage model in the literature, despite well-known decomposition methods being applied, cannot provide solutions within a reasonable time. The results also demonstrate the effectiveness of the solutions from the proposed models compared with the standard multistage model. • Operating microgrids under stochastic islanding and net load is studied. • Scalable optimization models are proposed for each uncertain factor and integrated. • The solutions from the proposed models are adaptable to sequential realization. • The proposed models show computational benefits over the standard multistage model. • The effectiveness of the solutions from the proposed models is also demonstrated. [ABSTRACT FROM AUTHOR]

The inherent randomness and volatility of wind power generation present significant challenges to the reliable and secure operation of the power system. Therefore, it is crucial to have interpretable wind power forecasting (WPF) to ensure seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving WPF performance and ignore the interpretability of the model, resulting in ambiguous forecasting. In this paper, the interpretable feature-temporal transformer (IFTT) for short-term wind power forecasting with multivariate time series is presented. The model uses an encoder-decoder architecture to effectively integrate historical information and future prior information from multiple variables. The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) effectively realize the interpretability of temporal information and multi-variable inputs while extracting important features. The Auxiliary Forecasting Network (AFN) plays a key role in providing pseudo-future wind speed predictions, which serve as an essential input for the model's decoder, and enhancing forecasting accuracy through multi-task learning. Experimental results on multiple datasets in different geographical locations show that the proposed algorithm is superior to various advanced methods. Besides, the interpretability of the IFTT model offers valuable insights for ensuring the safety of wind power utilization and supporting informed risk decision-making. • This paper introduces the interpretable feature-temporal transformer (IFTT) model, which enhances wind power forecasting by effectively integrating historical information and future prior information from multiple variables. • The designed decoupled feature-temporal self-attention (DFTA) module and variable attention network (VAN) ensure the interpretability of temporal information and multi-variable inputs, allowing for the extraction of important features. • Experimental results on multiple datasets in different geographical locations demonstrate the superior performance of the proposed IFTT algorithm compared to various advanced methods, highlighting its potential to improve the accuracy of WPF. • The interpretability of the IFTT model provides valuable insights for ensuring the safe and reliable utilization of wind power, enabling informed decision-making and risk assessment in the context of wind power integration into the grid. [ABSTRACT FROM AUTHOR]

The offshore wind industry is advancing with larger turbines (10 MW+) into sites in deeper waters (>60 m), necessitating innovative floating substructures. However, Floating Offshore Wind (FOW) must still be proven to be cost-efficient, with special attention paid to developing installation, Operations and Maintenance (O&M) and decommissioning strategies that will ensure FOW is competitive. Simulation is an efficient way to assess the viability of new technologies and the innovative operations required to install and maintain them. This paper presents a novel tool that simulates the installation of fixed/floating offshore wind farms across an hourly time-series of Metocean data, producing a total estimation of costs as part of the total capital expenditure (CAPEX). The paper validates the model by simulating installation of Hywind Scotland, the FOW farm, and comparing results with published data. Given the lack of real-world cost data and experience, this paper then seeks to provide a well-defined case-study for future researchers to develop further in other regions and with different technologies. The tool is applied to a theoretical large commercial 1GW FOW farm commissioned in 2035 at a representative site in the Celtic Sea. This considers a promising location for FOW development outside the existing demonstration/early commercial FOW projects, which are primarily focused on the North Sea. Results indicate a CAPEX (including installation) of €3492/kW, which is in line with industry expectations. Sensitivity analysis applies a learning rate to reduce platform costs, and varies farm capacity, demonstrating the potential economies of scale when increasing farm size. Simulation identifies weather operational limits and cable installation duration as key contributors to installation time and costs. This quantifies where cost-savings can be found and optimisation should focus. Further modelling considers the scale of their impact on results. A final Levelised Cost of Energy assessment estimating an LCoE range of €52.3–64.59/MWh, placing the FOW farm in the context of global economic targets. • Novel installation tool for fixed/floating offshore wind farms. • Tool validated for floating wind against installation data for Hywind Scotland. • Provides reference case-study with full input details to promote re-use. • Installation costs are sensitive to weather operational limits and durations. • Results: LCoE of €52.3–64.59/MWh for floating wind farms in the Celtic Sea, 2035. [ABSTRACT FROM AUTHOR]

The transformation of power systems towards greater flexibility and renewables is exacerbating the balancing challenge between flexibility and stability requirements for pumped storage (PS) units, as frequent ramping of PS stations for load balancing intensifies the operational switching. However, the flexible regulation and stable operation of units are often independently analyzed in previous research, making it difficult to achieve coordinated optimization for the PS flexibility and stability of PS units. Therefore, this paper develops a novel soft-linking coupling model between the power system unit commitment and pump-turbine governing system by utilizing the coupled relationship of parameters between the system and units (output power and water head). Moreover, it introduces new operational indicators for units, including the frequency of load changes and speed change rate. The improved Random Entropy TOPSIS method is employed to construct a comprehensive evaluation system for the flexibility and stability of PS units. Finally, a case study is conducted on a hydro-wind-photovoltaic complementary system to investigate the influence of wind/solar capacity ratio, unit's flexible parameters, and reservoir operation modes on the flexibility and stability of units. The comprehensive assessment shows that in the scenarios where the wind/solar capacity ratio is 1:5, the highest score of 85.99 (S4, ramp rate of 0.7 and power output range 0.5) exceeds the lowest scoring scenario (S1, ramp rate 0.75 and power output range 0.4) by 19.17. Despite 8.7% higher flow variance in the highest score scenario compared to the lowest scenario; the flexibility deficit rate, torque variance, and frequency stabilization time exhibit better performance with reductions of 3.91%, 4.8%, and 4.3%, respectively. This paper provides a foundational model for the coordination of flexible regulation and stable operation of PS units. [Display omitted] • A novel coupled model integrates flow, power output, and water head as soft-linking conversion parameters. • The wind/solar capacity ratio greatly affects flexibility, while unit flexibility and reservoir operation types exhibit minimmal impact. • Higher flexible units employ power output adjustments for increased flexibility. • Improving the unit flexibility paraments results in stability challenges in hydro and mechanical systems, and increases fluctuations in frequency regulation. • Scenario4 achieves a balanced coordination between system flexibility, hydro system, mechanical system, and frequency regulation. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed state estimation-based centralized control design problem for Internet-of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks. • Resilient control algorithm is developed for IoT-enabled microgrid system model. • The model considers noise, inaccurate measurement in sensors and deception attacks. • Estimated state is embedded in controller to obtain desired microgrid performance. • Algorithm illustrates the ability to resist deception attacks, noises and gains. [ABSTRACT FROM AUTHOR]

This paper addresses the integration of electric vehicle (EV) fleets into industrial smart grids to increase operational flexibility. It focuses on an extended multi-objective optimization problem that minimizes two primary objectives: (i) the electricity expenditure of a company using its employees' EV batteries as temporary distributed energy storage, and (ii) the costs associated with the degradation of EV batteries, given the additional usage from the company's perspective. In this paper, the utilization of an EV fleet is simulated at the individual car level over a one-year period. These optimization problems were balanced by using real-time electricity prices and the effective demand response (DR) of the company's electrical load. The company utilized the EVs as battery storage to offset fluctuating electricity prices, while compensating EV owners with free electricity for the costs incurred through degradation of their batteries. The extent to which the company could compensate EV owners while maintaining the viability of vehicle-to-grid (V2G) services in a non-residential scenario was explored. The results established an equilibrium point at which the financial benefits for the company resulting from V2G services was maximized against the negative financial impact of increased battery degradation for EV owners. The results showed that there is a potential mutual benefit between the company and EV owners, even if the company provided EV owners with free charging (based on a percentage of their battery capacity) for each day of their attendance. This mutually beneficial zone ranged from 3%–10% of the battery capacity for AC charging and 6%–17% for DC charging. Optimal Pareto values indicated an economic trade-off that benefited both stakeholders, with DC charging proving significantly more profitable for the company than AC charging (between 257.5% to 38.1% depending on the amount of free charging provided). The findings emphasize the need for an equitable pricing mechanism considering the different characteristics of EVs based on the operational and financial benefits for both parties to create a balanced pricing framework for V2G. • In-depth study on optimization of vehicle-to-grid and electric vehicle battery degradation using MILP. • Mutual benefit for the company and EV owners in an industrial V2G environment. • DC charging was 257.45% to 38.1% more profitable for the company than AC charging. • Comparing the profitability of different EVs in price-based demand response. • Necessity of a fair pricing mechanism for EV owners participating in V2G. [ABSTRACT FROM AUTHOR]

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ± 10 % variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector. • Introduced an agent-based model to simulate residential consumer enrollment in DR. • Utilized economic and noneconomic factors to identify consumer decision-making to enroll. • Introduced different enrollment contract types in DR for consumers. • Analyzed different parameters influencing consumer enrollment rates. [ABSTRACT FROM AUTHOR]

The discussion of fairness is gaining considerable attention in the context of Local Energy Systems (LES). This is partially motivated by the energy transition, which has put more attention on technologies and production closer to the end-user. In other words, we are evolving towards a more user-centric approach, which requires dealing with fairness and justice due to the users' participation in the loop. Their willingness to participate depends on the perceived fairness regarding the systems they use. The current literature started to adopt the concept of fairness within LES but without a universally accepted approach. This review highlights the work described in the literature by thoroughly analyzing 80 scholarly papers addressing fairness in LES. The main objectives are twofold: to provide a definition of fairness within this domain and to examine how this concept is currently shaped and interpreted. The analyzed studies are categorized according to their applicability to LES, fairness and justice interpretation (e.g., equality, meritocracy), and use of post-method indicators to evaluate fairness performance. The work explores the identified gaps in the existing literature and outlines guidelines and prospective research directions for future studies addressing fairness issues in LES. [Display omitted] • The study explores fairness in LES, focusing on user-centric approaches. • The paper reviews 80 research papers on fairness in LES. • Literature-based interpretations of fairness in LES are identified and categorized. • The paper identifies gaps and proposes future research directions on fairness in LES. [ABSTRACT FROM AUTHOR]

Prudent and sensible deployment of local storage may unlock the potential of mitigating the adverse impact, especially addressing the challenge of uncertainty from independent renewable energy deployment. This paper designs a photovoltaic battery system (PVBS) and a gas turbine hydrogen tank system (GTTS) as the local energy communities to mitigate the uncertainty in the integrated electric-gas systems (IEGS). Firstly, this paper quantified the contribution of the co-located energy storage and remodeled the output of PVBS and GTTS as approximate beta distributions, assessing the local effects of uncertainty mitigation. Subsequently, the steady-state energy flow models incorporated with uncertainty variables of IEGS are reformulated into the chaotic mathematical counterpart via the generalized polynomial chaos method (GPC), highlighting the global performance of novel designs. In the subsequent step, the independent random variables are factorized from chaotic equations leveraged by the improved Galerkin method and remodeled into a high-dimensional deterministic equation. Based on this, the probabilistic distributions of state variables with higher order uncertainty parameters for IEGS, such as bus voltage, natural gas node pressure, etc., are sequentially computed via the Newton method. Furthermore, this paper performed a comparative simulation in the IEGS 4–12 system and IEGS 118–96 system and empirically tested the feasibility and validity of the novel design in uncertainty mitigation. Through the quantitative instantiation, enhancing the co-located energy storage capacity, increasing the outlet pressure of compressors, and decreasing the coupling level between subsystems may mitigate the uncertainty within IEGS. Derived GPC algorithms improve the computational speed and accuracy than other methods for the computation of multiple uncertainty variables in IEGS. This exploration delves into the local energy communities in various operating conditions and offers valuable suggestions for sizing and control strategies for the novel design in IEGS. • Scalable combinations of paired energy storage solutions are developed. • Utilization of generalized polynomial chaos is extended to an integrated energy system. • Optimal capacity and sizing of multi-type storage are proposed. • Best configurations of energy interfaces and compressors are presented. [ABSTRACT FROM AUTHOR]

With the rapid advancement of distributed energy resources (DERs), artificial intelligence, and smart meter technologies, traditional consumers are undergoing a paradigm shift towards 'prosumers'. In this context, peer-to-peer (P2P) energy trading emerges as an effective approach to enhance local energy utilization. Nevertheless, the inherent intermittency and forecasting challenges associated with renewable energy resources may magnify uncertainties in the markets, and pose a potential threat to destabilize the markets. To address this challenge, this paper presents a method to assess the economic impacts of forecasting errors and introduces a metric, the bill deviation index. Additionally, the consequences of forecasting errors on market outcomes are examined based on the mathematical model of three different pricing mechanisms. Our findings indicate that forecasting errors can lead to significant financial discrepancies, the magnitude of which is closely related to the pricing mechanisms and their dependency on energy quantity. The paper further underscores the role of variability in clearing price, balancing cost, and the supply–demand relationship in determining the economic fallout of forecasting errors. It concludes by providing insights for managing energy trading in markets marked by high forecasting errors and suggests strategies to mitigate the associated economic risks. • An economic impact assessment method considering pricing calculation principles is proposed. • The influence of forecasting errors on market outcomes is analyzed through three pricing mechanisms. • A P2P energy trading model is developed to evaluate crucial variables under various market conditions. [ABSTRACT FROM AUTHOR]

With the changes in energy structure and system configuration, the uncertainty on both the supply and demand sides of the power system will significantly increase. The approach of increasing reserves to cope with the uncertainty is becoming unsustainable both technically and economically. It is necessary to accurately characterize system flexibility and propose flexibility-oriented dispatching optimization strategies. Based on this, this paper conducts research on the evaluation system for flexibility supply-demand balance and the coordinated optimization dispatch strategy of generation-load-storage resources. Firstly, a flexibility demand-supply balance evaluation system is proposed, which covers the quantification of flexibility demand, the quantification of flexibility supply capability, and the design of flexibility metrics. Secondly, a two-stage optimization dispatching model for generation-load-storage resources is constructed. In stage 1, the optimization obtains the operating power of generation, load and storage resources to meet the load demand, while in stage 2, the flexibility supply potential is considered to optimize the redispatch power of the flexibility resources to meet the flexibility demand. Finally, simulations are conducted in a typical industrial park with generation, load, and storage resources, and the results show: 1) The quantification of flexibility demand based on the polytope form is more accurate, ensuring that the obtained flexibility supply-demand balance evaluation results can effectively support system flexibility dispatching; 2) Compared with the traditional dispatching strategy that consider reserve capacity, the operational cost of the dispatching strategy considering flexibility supply-demand characteristics in this paper decreased by 3.31%, environmental costs decreased by 4.56%, and the penalty cost for flexibility deficiency decreased by 17.37%. The research results can leverage the synergistic effect of generation -load-storage resources, achieving a scientific balance between the economy, low-carbon, and adequacy of the generation-load-storage system, and provide a scientific approach for considering flexible supply-demand balance in the power system dispatching, supporting the stable and sustainable operation of the high-penetration new energy system. • A comprehensive evaluation system for flexible supply-demand balance is designed; • The flexibility demand quantification model based on the polytope form is constructed; • A two-stage coordinated optimization dispatching model for generation-load-storage resources is constructed; • The two-stage dispatching power and costs of the generation-load-storage resources in 24 period are examined. [ABSTRACT FROM AUTHOR]

Renewable clean energy sources, such as wind, solar, and wave energy, are currently gaining prominence and are being extensively researched. Given the escalating significance of clean energy, hybrid systems have gradually become an effective solution to address energy and environmental issues. In order to maximize the advantages of wind, wave energy, and photovoltaic (PV), this paper proposes a hybrid wind-wave-PV system (HWWPS) by combining wind turbines, PV panels, and wave energy converter (WEC) to achieve higher energy production and efficiency. To further enhance power output, this paper focuses on the influence of system spatial array optimization on power output and adopts an algorithm to establish a strategic layout. Through the integration of the chaos algorithm, exponential asynchronous factor, and the sine-cosine mechanism, the original slime mould algorithm (SMA) algorithm is enhanced to the exponential slime mould algorithm (ESMA), which has better optimization capability and is particularly suitable for determining the optimal power configuration. Simulations are conducted on hybrid systems consisting of three, seven, and thirteen buoys, respectively, which compare the ESMA are compared with the other six algorithms. The results verify the advantages of ESMA over the other algorithms, and demonstrates the superiority becomes more prominent as the system size increases. • A new hybrid wind-wave-PV system is established for the first time; • An exponential slime mould algorithm is proposed; • The comparison of the six algorithms highlights the ability of the exponential slime mould algorithm to optimize the array layout of new hybrid wind-wave -PV system. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid vehicles (FCHVs) are significant for achieving zero carbon emissions. Connected FCHVs can leverage traffic information to collaboratively optimize cruise and power allocation control, enhancing various performance aspects. For urban driving scenarios, this paper introduces a multi-strategy series control architecture for longitudinal cruise and power allocation control in connected FCHVs. However, particle swarm optimization (PSO) algorithms face challenges in high-dimensional decision and objective spaces when optimizing multiple strategies. Additionally, manually preset PSO parameters hinder particle evolution from dynamically adapting to unknown multi-objective spaces, thereby limiting the development of multiple performance metrics. To address this issue, this paper proposes a Q-learning multi-objective PSO (QMOPSO) algorithm. This algorithm tackles high-dimensional optimization challenges by improving population initialization distribution and subpopulation division, and enables particles to dynamically adjust exploration strategies, thereby maximizing multiple objective performances. The results indicate that compared to a control scheme optimized with PSO under predefined driving conditions, the multi-strategy series control framework optimized with the QMOPSO algorithm improves tracking stability by 50.20%, driving comfort by 1.77%, fuel economy by 6.10%, and reduces power source degradation by 2.04% in urban driving scenarios. Compared to PSO and multi-objective PSO algorithms, the QMOPSO algorithm demonstrates superior trade-offs. This research provides a collaborative optimization solution for FCHVs in connected environments. • A cruise and power allocation integrated control is designed for connected FCHVs. • Multi-strategy multi-objective co-optimization is performed under urban scenarios. • QMOPSO algorithm is proposed to address the PSO preset parameter adaptation flaw. • Control strategy with QMOPSO improves four different performance metrics. [ABSTRACT FROM AUTHOR]

Considering the dynamic nature of market conditions, this paper introduces a state-dependent futures hedging optimization model and methodology. This approach dynamically adjusts the traditional model-driven hedging strategy, effectively balancing the pursuit of returns with the imperative of risk mitigation. Empirical evidence shows that integrating Hidden Markov Model (HMM) with machine learning techniques, as demonstrated in this study, improves the accuracy of market state forecasts. Compared to the traditional model-driven hedging strategy, the innovative state-dependent hedging strategy introduced here significantly enhances the return-to-risk ratio, and revenue, without increasing hedging risks. Moreover, the hedging portfolio developed under this strategy achieves an average hedging efficiency of 0.76, highlighting the effectiveness of the proposed methodology. Additional robustness tests indicate that this market state-dependent hedging optimization strategy is promising under various conditions, including different position adjustment ratios, volatility benchmarks, evaluation periods, types of crude oil, and transaction costs. The research conducted in this paper not only contributes to and expands traditional hedging theories but also provides a practical risk management solution for market participants. • A state-dependent hedging strategy is proposed. • A state prediction model combining HMM and machine learning is established. • The hedging model effectively balances the pursuit of returns with the imperative of risk mitigation. [ABSTRACT FROM AUTHOR]

Building external walls and fire insulation are important components of a low carbon economy. Once a fire occurs in the building, the ejected flame from the opening of the building can easily ignite the building external wall insulation materials and further resulting the catastrophic large-scale facade fire. The paper investigates experimentally the temperature evolution within fire compartment, the external flame merging behavior and the structure characteristic (height/area) ejected through multiple-openings comprehensively for various numbers of openings and separation distances (with a fixed overall opening size) and fire heat release rates with a total of 272 conditions. A rectangle reduced-scale fire compartment and an external facade was designed with various multi-opening conditions (numbers of openings, separations). Results show that the upper-part temperature within fire compartment first increases and then changes a little around 1000 K, it is correlated well by the heat release rate and heat loss per unit time for over-ventilated conditions, while it correlated well with the opening factor for under-ventilated condition. Regardless of the number of openings, it only depends on the overall opening size. The external flame merging probability increases (from 0 to 1) with increasing number of openings and fire HRR, which correlated well as a function of the non-merging flame height from a single opening (or the flame upward velocity) and the separation distances between the adjacent two openings. The external flame height decreases (about from 1.0 m to 0.3 m) with increasing number of openings. A new characteristic length scale, considering the multiple rectangle fire sources at the neutral plane was proposed to describe the external flame height. The external flame area is further estimated based on the flame height and the merging probability. The experimental data and correlations obtained in this paper contribute a fundamental basis for quantifying the characteristics of the building fire, selection of building external wall insulation materials and building energy saving. [Display omitted] • Investigated external flame structure characteristics and merging behaviors through multiple-openings. • Revealed temperature evolution inside the compartment with HRR for various opening numbers. • Proposed a general dimensionless model of flame merging probability for multiple-openings. • Proposed a new characteristic length scale to reflect external flame height from multiple-openings. [ABSTRACT FROM AUTHOR]

As a direct reference tool to reflect the operational status and development level of national industry, industrial statistics hold significant value for numerous systematic studies. Nevertheless, it is crucial to recognize that the quality of these statistics can be compromised by the common occurrence of missing value. This issue poses a substantial challenge for analyzing and utilizing industrial statistics, impeding progress in tasks reliant upon them. Given the severity of the missing value problem in industrial statistical databases and the limitations of existing literatures on missing value imputation in terms of research objects and modeling approaches, this paper proposes a novel missing value imputation modeling approach for single-indicator panels of industrial statistics based on the idea of data-characteristic-driven (DCD). Accordingly, taking the inter-provincial "monthly power generation" data from China as an example, the imputation model was constructed and its validity was tested under different imputed objects (Jiangsu and Jilin), different missing types (continuous and discrete), and different missing rates (5%, 10% and 20%) respectively. The results indicate that the proposed DCD modeling approach in this paper exhibits excellent efficacy. The imputation model, constructed based on the data characteristic of the imputed object, demonstrates clear advantages in handling missing value with different missing types and rates. This is evident in its superior consideration of numerical accuracy, directional accuracy, and imputation stability, resulting in an outstanding comprehensive imputation effect. • A novel DCD modeling approach for missing value imputation of industrial statistics is proposed. • A data characteristic recognition path of single-indicator panel data is constructed. • The match between data characteristic and model mechanisms improves imputation performance. [ABSTRACT FROM AUTHOR]

This paper presents a newly developed methodology aimed at assessing at national level the techno-economic potential of district heating (DH) based on renewables and excess heat sources. The novelty of the model lies in the use of an optimization approach to match heat demand and heat sources at large scale level, while keeping a high degree of spatial detail. Areas suitable for DH adoption are identified by minimizing heat delivery costs, and therefore by choosing the most economical technology between district heating and the alternative individual solution. The optimization approach, usually applicable at limited analytical scope because of the computational burden, is here adapted to large scale analysis through the introduction of novel methodological elements with which the network topology is simulated nationwide. The methodology applies to preliminarily identified maps of available heat sources and eligible heat demand, with the quantification of the latter including retrofitting and low connection rate scenarios. It then consists in two steps: connecting elements in a graph through triangulation and routing algorithms and optimizing connections to minimize the overall heat delivery costs, either by adopting district heating or individual heating systems. The whole methodology is based on open-source data and tools for broad applicability. The paper presents the elaborated methodology together with the application of the entire model to Italy. The outcome is a map of the potential district heating systems identified with significant spatial detail nationwide. A four-fold expansion is envisaged, covering 12% of the national heat demand with renewables- and excess heat- based district heating. [Display omitted] • Novel methodology to assess techno-economic potential of district heating at large scale level. • Spatially detailed model based on open-source data and tools - replicable and adaptable model. • Optimization algorithm on delivered heat cost minimisation with no thresholds set a priori. • 38 TWh of district heating potential in Italy, equivalent to a four-fold expansion to 12% of the civil sector heat demand. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) is the most promising future energy source owing to its low operating temperature, high energy efficiency, high power density, and environmental friendliness. In this paper, a comprehensive PEMFC system control-oriented model with parameter identification is developed, which includes various subsystems and components such as the hydrogen supply subsystem, air supply subsystem, thermal management subsystem. To investigate temperature control strategy by the thermal management subsystem, the energy balance equation is used for the PEMFC stack, water tank and radiator to reflect the transient temperature variation during the operation. Four typical temperature control strategies are implemented on the thermal management subsystem and their performances are evaluated in terms of temperature profiles, overshoot and integral absolute error criterion (IAE). Besides, this paper also considers the parasitic power of the fan under different control strategies. It is found that among the four strategies, Case 4 has the best performance on the controlling both water pump and fan with the PID. To improve the control effect further, fuzzy control is introduced to optimize the PID controller of the pump, and the radial basis function (RBF) neural network is introduced to optimize the PID controller of the fan. The results demonstrate that Fuzzy-PID and RBF-PID can achieve a better control effect with 22.66% decrease in IAE of T st (stack temperature) and 77.56% decrease in IAE of T in (inlet cooling water temperature). • This paper establishes a comprehensive control-oriented PEMFC system model for control research. • The influence of water content λ is considered in the parameter identification of semi-empirical equations. • Fuzzy control and RBF neural network are introduced to optimize the temperature control effect. • The parasitic power of fan is investigated. [ABSTRACT FROM AUTHOR]

Discrete manufacturing systems (MSs) are prevalent across various sectors such as electronic production, food processing, and apparel manufacturing. However, their operation raises critical concerns regarding significant energy consumption and carbon emissions. Implementing local electricity and carbon allowance sharing among MSs presents a potential solution to these issues, which is explored in this paper. The main contribution of this paper lies in achieving distributed electricity and carbon allowance sharing among MSs while addressing challenges related to algorithmic and physical feasibility. Firstly, a local electricity and carbon allowance sharing problem for MSs is formulated, which involves numerous binary decision variables due to the operation of discrete manufacturing facilities and energy storage, rendering the problem challenging to solve in a distributed manner. To tackle this challenge, we propose an alternating optimization procedure (AOP)-based distributed method to solve the problem while ensuring algorithmic feasibility. Secondly, the second-order cone relaxation program (SOCP)-based power flow model is identified cannot guarantee the exactness of the distribution system model when conducting local electricity sharing. We tackle this challenge by employing a convex-concave procedure (CCP)-based feasibility recovery procedure (FRP) to recover the exactness of the SOCP relaxation, thereby ensuring physical feasibility. The numerical results demonstrate that conducting local electricity and carbon allowance sharing can effectively reduce energy costs and carbon emissions for MSs. Moreover, compared with the alternating direction method of multipliers (ADMM), the proposed distributed method can guarantee both algorithmic and physical feasibility when solving the problem. • Enabling electricity and carbon allowance sharing among manufacturing systems. • A local electricity and carbon allowance sharing problem is formulated. • An alternating optimization procedure-based distributed method is proposed. • A convex-concave procedure-based feasibility recovery procedure is introduced. • Guaranteeing algorithmic and physical feasibility when solving the problem. [ABSTRACT FROM AUTHOR]

Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells. Our approach incorporates Partial Convolution, Switchable Atrous Convolution and novel data augmentation techniques to address the challenges of varying defect sizes, complex backgrounds. The Partial Convolution component effectively manages the irregularities in PV cell images, reducing false detections. On the other hand, the Switchable Atrous Convolution enhances the model's adaptability to different defect scales and spatial resolutions, leading to improved localization and classification performance. We evaluate our model on a large-scale dataset of PV cell images, demonstrating its superiority over existing methods in terms of detection accuracy, speed, and robustness. Our proposed framework offers a practical and reliable solution for real-time anomaly detection in PV cells, facilitating timely maintenance and maximizing the performance of solar energy systems. The integration of these advanced convolution techniques into the YOLOv7 model not only improves detection capabilities but also paves the way for further research and development in the field of deep learning-based anomaly detection. • The PSA-YOLOv7 proposed in this paper exhibits better interpretability than the baseline model. • The PSA-YOLOv7 shows better robustness under common corruptions. • PSA-YOLOv7 has improved anomaly detection performance while ensuring real-time capability. [ABSTRACT FROM AUTHOR]

Increasing integration of distributed solar photovoltaic (PV) into distribution networks could result in adverse effects on grid operation. Traditional model-based control algorithms require accurate model information that is difficult to acquire and thus are challenging to implement in practice. This paper proposes a surrogate model-enabled grid visibility scheme to empower deep reinforcement learning (DRL) approach for distribution network voltage regulation using PV inverters with minimal system knowledge. In contrast to existing DRL methods, this paper presents and corroborates the adverse impact of missing load information on DRL performance and, based on this finding, proposes a surrogate model methodology to impute load information utilizing observable data. Additionally, a multi-fidelity neural network is utilized to construct the DRL training environment, chosen for its efficient data utilization and enhanced robustness to data uncertainty. The feasibility and effectiveness of the proposed algorithm are assessed by considering DRL testing across varying degrees of observable load information and diverse training environments on a realistic power system. • A visibility-enhanced surrogate has been introduced, empirically demonstrating its effectiveness in enhancing deep reinforcement learning (DRL) performances. • A multi-fidelity neural network is employed as a surrogate model within the training environment, improving data utilization efficiency while ensuring the effectiveness of DRL training. • Realistic test feeders have been used for testing, further validating the feasibility of the proposed method in practical scenarios. [ABSTRACT FROM AUTHOR]

The construction of a virtual power plant (VPP) represents a powerful approach for harnessing the adjustable capacity of flexible resources. VPP actively participates in transmission network dispatching by aggregating these flexible resources. However, existing practical implementations and research cannot describe the influence of renewable energy's inherent unpredictability on the dispatching boundaries of VPPs. This shortfall leads to a lack of organic coordination between flexible resources and the power transmission network. Therefore, this paper suggests a way to characterize the dispatching boundaries of VPPs based on the concept of flexible operation region (FOR), aiming to better incorporate the flexible resources of decentralized access and lessen the issue of insufficient computing power caused by unified optimization. Initially, an analytical model for the VPP's FOR is presented, followed by dimensionality reduction of the FOR to the VPP's gateway. Subsequently, participation in transmission network scheduling is facilitated using an equivalent dimensionality reduction model. Based on standard example testing, the proposed method's effectiveness and accuracy have been verified in this paper. • This paper proposes a method to characterize the dispatching boundary of VPP based on the FOR. • Constructs the static voltage security region (SVSR) related to each node. • Thermal security region (THSR) related to each branch in the cluster in the power injection space. • This paper proposes a strategy to identify and eliminate the ineffective constraint • The boundary construction method of VPP is applied to the cooperative optimal dispatching. [ABSTRACT FROM AUTHOR]

Power system flexibility can be improved effectively, if the advantages of the peak shaving ability of molten salt solar tower power (STP) plant can be developed and utilized. In this paper, the heat transport and load response characteristics of the molten salt STP plant in the regulation process are studied, aiming at serving the development of the regulation method in the rapid peak regulation process. The steam generation system and turbine system models of a 50 MW STP station are established. Two load regulation methods and operation modes are used to investigate the inertia and delay effects of heat transfer and evaporation, as well as load response characteristics during the actual regulation operation, including steam generation system following (SF), turbine following (TF), constant pressure operation (CP), and sliding pressure operation (SP). The results show that when the plant load is reduced by 10% Pe at the rate of 3% Pe /min, the heat transfer of salt/water and the evaporation process of water in the whole regulation process have a 250 s delay compared with the load. The larger heat storage characteristics of the plant help to improve the load response speed. The load response speed of the plant is faster in SF mode. TF mode is beneficial to the safe operation of the plant. Considering the operational safety of thick-wall component, the recommended running mode is the collocation of SF and SP mode, TF and CP mode. The results show that the load change rate of 10% Pe /min is permittable under TF and CP modes. The conclusion of this paper lays a foundation for the formulation of a peak regulation strategy for molten salt STPs. • Dynamic model for STP plant as participated in grid peak-shaving is established. • Transient response characteristics of STP plant during real load regulation are analyzed. • Influences of load changing rate on SPT plant under SGS following control mode are compared. • Recommended operation mode and load adjusting rate of the 50MW el STP plant are given. [ABSTRACT FROM AUTHOR]