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Novel hydroelectric cells exploit porosity and structural defects to dissociate deionized water for hydroelectricity generation. In the present study, these essential attributes have been engineered using Zr4+ doping in MgFe 2 O 4 using the facile sol-gel method. Comprehensive structural analysis performed using modern analytical techniques such as X-ray Diffraction (XRD) and Raman Spectroscopy ascertained the pure spinel cubic phase of the synthesized nanoparticles. Morphologically, the material was probed using Field-Emission Scanning Electron Microscopy (FESEM) analysis, identifying highly porous and agglomerated spherical grains. The mesoporous structure of the samples was further determined using Brunauer-Emmett-Teller (BET) surface examination. An inclusive evaluation employing Photoluminescence (PL) examination in the UV spectral region revealed the existence of oxygen-deficient sites and intrinsic defects inside the doped magnesium ferrites. These results were further validated using X-ray Photoelectron Spectroscopy (XPS). Significant lattice strain (≈2.5 × 10−3) and the presence of oxygen vacancies, defects, and mesoporosity influence water dissociation at the ferrite surface. These attributes were utilized to facilitate the generation of hydroelectric current. The maximum short circuit current values recorded for MgZr 0.05 Fe 1.95 O 4 (ZMF-1) and MgZr 0.10 Fe 1.90 O 4 (ZMF-2) were observed to be 6.3 and 11.6 mA, respectively, with corresponding output voltages of 0.77 V and 0.89 V. The incorporation of Zirconium (Zr) dopant not only enhances the hydroelectric current but also contributes to sustained current generation over extended periods. [Display omitted] [ABSTRACT FROM AUTHOR]

The current research focuses on enhancing oxygenation through titanium dioxide (TiO 2) nanoparticle-doped biofuel-diesel blend and oxy-hydrogen (HHO) gas. Initially, baseline diesel data was collected on conventional diesel engine. A 20% palmyra oil biodiesel (POB) with an 80% diesel blend was formed as POB20 and operated in the next stage. In the successive stages, the diesel engine is retrofitted to work in dual-fuel mode using HHO gas as inducted fuel along with POB20, 50 ppm TiO 2 -doped POB20, and 100 ppm TiO 2 -doped POB20 are used as primary pilot test fuels The minimal hydrocarbon (HC), carbon monoxide (CO), and smoke emissions were noticed with 100 ppm TiO 2 -doped POB20 along with HHO gas about 0.12 g/kWh, 2.25 g/kWh, and 27%, respectively. For the same test fuel operation, brake thermal efficiency (BTE) [by about 5.5%], maximum cylinder pressure (P max) [by about 5.95%], and maximum heat release rate (HRR max) [by about 7.6%] were increased compared with diesel. Due to the promising results of TiO 2 -doped POB20 and HHO gas operation, this research holds promising potential for practical implementations in various fields like agriculture and stationary electric power generation. [Display omitted] • Palmyra oil biodiesel, HHO gas, and TiO 2 nanoparticles are used to run the engine. • Higher ICP and HRR are noticed for TiO 2 -doped biofuel-diesel blend and HHO gas. • TiO 2 -doped biofuel-diesel blend and HHO gas resulted in higher BTE. • Low HC and smoke emissions are noticed with all the oxygenated test fuels. [ABSTRACT FROM AUTHOR]

The coexistence of an evaporation potential and streaming potential in a NWEG has been proposed using ZSM-5 as the generation material. A streaming potential formed as water flowed inside the ZSM-5 nanochannels, driven by electrodynamic effects that increased from the bottom to the top of the generator. Conversely, an evaporation potential existed at the surface-gas interface of the ZSM-5-solution, which decreased from the bottom to the top as the evaporation height increased. The resulting open-circuit voltage (V oc) depended on the balance between evaporation and streaming potential, both of which were influenced by the evaporation enthalpy (E e) or vapor pressure. Generally, a higher E e or lower vapor pressure led to a lower evaporation potential and subsequently a lower V oc. A dual mechanism involving synergistic evaporation potential and streaming potential is proposed to explain the mechanism of NWEGs. [Display omitted] Electricity generation by natural water evaporation generators (NWEGs) using porous materials shows great potential for energy harvesting, but mechanistic investigations of NWEGs have mostly been limited to streaming potential studies. In this study, we propose the coexistence of an evaporation potential and streaming potential in a NWEG using ZSM-5 as the generation material. The iron probe method, salt concentration regulation, solution regulation, and side evaporation area regulation were used to analyze the NWEG mechanism. Our findings revealed that a streaming potential formed as water flowed inside the ZSM-5 nanochannels, driven by electrodynamic effects that increased from the bottom to the top of the generator. In addition, an evaporation potential existed at the surface interface between ZSM-5 and water, which decreased from the bottom to the top as the evaporation height of the generator increased. The resulting open-circuit voltage (V oc) depended on the balance between the evaporation and streaming potentials, both of which were influenced by the evaporation enthalpy (E e) or vapor pressure. Generally, a higher E e or lower vapor pressure led to a lower evaporation potential and subsequently a lower V oc. A dual mechanism involving synergistic evaporation potential and streaming potential is proposed to explain the mechanism of NWEGs. [ABSTRACT FROM AUTHOR]

[Display omitted] The mounting global energy demand urges surplus electricity generation. Due to dwindling fossil resources and environmental concerns, shifting from carbon-based fuels to renewables is vital. Though renewables are affordable, their intermittent nature poses supply challenges. In these contexts, aqueous flow batteries (AFBs), are a viable energy storage solution. This study tackles AFBs' energy density and efficiency challenges. Conventional strategies focus on altering molecule's solubility but overlook interface's transport kinetics. We show that triggering electrostatic forces at the interface can significantly enhance the mass transport kinetics of redox active molecules by introducing a powerful electrostatic flux over the diffusional flux, thereby exerting a precise directionality on the molecular transport. This approach of controlling the directionality of molecular flux in an all iron redox flow battery amplifies the current and power rating with approximately 140 % enhancement in the energy density. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

Photosynthetic microbial fuel cells (pMFCs) are hybrid systems that enable simultaneous wastewater treatment under anaerobic conditions and the generation of electricity by utilizing the potential difference in the anaerobic anode chamber and the oxygenated cathode chamber. Dairy wastewater with a concentration of 2000 mg COD/L was treated in the anode of a batch pMFC. In the cathode chamber, Chlorella vulgaris or Arthrospira platensis was cultivated in synthetic medium, and next in diluted effluent from the anode chamber. The highest power density of 91 mW/m2 was generated by the pMFC with the cultivation of Arthrospira platensis. Higher values of dissolved oxygen remained during the dark phase in the cathodic medium with Arthrospira platensis cultivation than with Chlorella vulgaris. This depletion of oxygen significantly decreased voltage generation, which during the light phase increased again to the maximum values. The COD removal achieved in the anodic chamber was 87%. The efficiency of nitrogen removal in the cathode chamber during the cultivation of Arthrospira platensis and Chlorella vulgaris was about 78% and 69%, respectively. The efficiency of phosphorus removal in the cathode chamber with the cultivation of Arthrospira plantensis and Chlorella vulgaris was 58% and 43%, respectively. This study has shown that the introduction of Arthrospira platensis into the cathode chamber is more effective than that of Chlorella vulgaris. [ABSTRACT FROM AUTHOR]

Electrogen is a microbial group that plays an important role as anodic biocatalyst of microbial fuel cells (MFCs), one of the most extensively studied microbial-based technologies for bioelectricity generation. The aims of this research were to isolate potential electrogenic bacteria from aquaculture pond sediments and conduct a thorough evaluation on the MFCs' electricity production and efficiency parameters. A total of 18 electrogenic bacteria having various electrochemical abilities was successfully isolated using thioglycollate solid media enriched with Fe3+. Five isolates (namely KCf1, KCf2, KCf4, KCf10, and KCf14) were non-pathogenic electrogens and able to produce relatively stable and high open-circuit voltage values (690–810 mV) on glucose-fed MFCs. Further evaluations on electricity production in close-circuit mode (fixed resistor technique, polarization tests, and analysis of power overshoot phenomenon) and efficiency parameters (Coulombic and energy efficiencies) showed that KCf2, molecular identified as Bacillus altitudinis AC11.2, was the most potential MFC biocatalyst among all isolates. It produced an MPP value of 67.11 mW m−2, current density of 333.03 mA m−2, and Coulombic and energy efficiency of 53.86% and 63.27%, respectively. Efforts to increase the MFC's electrical output have been done by assembling four reactors in series and parallel circuits, obtaining the maximum total voltage of 1.6–2.0 V (for series configuration). This potential output was higher than a portable zinc–carbon battery (1.5 V) and a Ni–Cd battery (1.2 V). However, the voltage reversal suffered in series circuits was another challenge in the development of MFCs for bioelectricity production, since the existence of this phenomenon due to biological factors (microbial metabolism dynamics) are not easy to be controlled. [ABSTRACT FROM AUTHOR]

Recent reports indicate that the development of electricity generation using wind turbines will continue to grow. Despite the long service life of wind turbine blades, their technological life comes to an end at a certain point. Currently, there is no industrial method for recycling them, and the proposed solutions need to consider a complete and comprehensive approach to this material. In many countries, these blades are stored in special landfills and await proposals for rational recycling. It has been proposed that this recyclable yet still troublesome raw material be used in building sheathing boards. Sheathing boards used in the construction industry have a relatively long lifecycle. Three types of polymer chips and two resins, i.e., PF and MUF, were used in the study. The boards' quality was assessed per the standards specified for particle boards. The resulting boards were characterized by strengths above 20 N/mm2 and an elastic modulus close to 4000 N/mm2. Slightly better results were obtained with the MUF resin. [ABSTRACT FROM AUTHOR]

Load forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short‐term electricity load prediction for residential users, single‐task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi‐task residential short‐term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre‐training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi‐task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short‐term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real‐world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

With the increasing integration of renewable energy sources into distribution and transmission networks, the efficiency of cascade H-bridge multilevel inverters (MLIs) in power control applications has become increasingly significant for sustainable electricity generation. Traditionally, obtaining optimal switching angles of MLIs to minimize total harmonic distortion (THD) requires solving the selective harmonic elimination equations. To this end, this research aims to use two recently developed intelligent optimization algorithms, dandelion optimizer and gold rush optimizer, to solve this problem. To evaluate the effectiveness of the proposed algorithms, an eleven-level cascaded H-bridge MLI (CHB-MLI) was considered in the study. Simulation results for different modulation indices were obtained, and the accuracy and solution quality were compared with genetic algorithm and particle swarm optimization algorithms. MATLAB/Simulink-based models were used to verify numerical computations, ensuring the reliability of the findings. This research contributes to the field by providing insights into obtaining optimal switching angles and minimizing THD in MLIs by applying intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

A circular economy based on symbiotic relationships among sectors, where the waste from one is resource to another, holds promise for cost-effective and sustainable production. This research explores such a model for the agriculture, energy, and construction sectors in California. Here, we develop new an understanding for the synergistic utilization mechanisms for rice hull, a byproduct from rice production, as a feedstock for electricity generation and rice hull ash (RHA) used as a supplementary cementitious material in concrete. A suite of methods including experimental analysis, techno-economic analysis (TEA), and life-cycle assessment (LCA) were applied to estimate the cost and environmental performance of the system. TEA results showed that the electricity price required for break even on expenses without selling RHA is $0.07/kWh, lower than the market price. As such, RHA may be available at little to no cost to concrete producers. Our experimental results showed the viability of RHA to be used as a supplementary cementitious material, meaning it can replace a portion of the cement used in concrete. LCA results showed that replacing 15% of cement with RHA in concrete can reduce carbon dioxide equivalent (CO2e) emissions by 15% while still meeting material performance targets. While the substitution rate of RHA for cement may be modest, RHA generated from California alone could mitigate 0.2% of total CO2e from the entire cement production sector in the United States and 1% in California. [ABSTRACT FROM AUTHOR]

Zinc‐air batteries (ZABs) have attracted considerable attention for their high energy density, safety, low noise, and eco‐friendliness. However, the capacity of mechanically rechargeable ZABs was limited by the cumbersome procedure for replacing the zinc anode, while electrically rechargeable ZABs suffer from issues including low depth of discharge, zinc dendrite and dead zinc formation, and sluggish oxygen evolution reaction, etc. To address these issues, we report a hybrid redox‐mediated zinc‐air fuel cell (HRM‐ZAFC) utilizing 7,8‐dihydroxyphenazine‐2‐sulfonic acid (DHPS) as the anolyte redox mediator, which shifts the zinc oxidation reaction from the electrode surface to a separate fuel tank. This approach decouples fuel feeding and electricity generation, providing greater operation flexibility and scalability for large‐scale power generation applications. The DHPS‐mediated ZAFC exhibited a superior peak power density of 0.51 W/cm2 and a continuous discharge capacity of 48.82 Ah with ZnO as the discharge product in the tank, highlighting its potential for power generation. [ABSTRACT FROM AUTHOR]

Fossil fuel prices are increasing day by day due to growing demand and resource limitations. This situation shifts conventional electricity generation to renewable power generation. Among renewables, solar photovoltaic (PV) energy is very promising as the third fastest growing one. However, PV system modeling requires the estimation of unknown parameters of PV cells and modules. This problem remains a compelling task due to its multi-dimensional, multi-model and non-linear characteristics. In order to contribute to the solution of this optimization problem, this work makes a detailed comparison of artificial hummingbird algorithm, artificial rabbits optimization, enhanced Jaya algorithm, flow direction algorithm and artificial gorilla troops optimizer for determining the unknown parameters of PV models. Experimental results demonstrate that artificial hummingbird algorithm in single diode modeling of PV cell, flow direction algorithm in double diode modeling of PV cell and artificial rabbits optimization in single diode modeling of PV module are found to be capable of estimating accurate and efficient design coefficients for PV systems. In addition, the mentioned metaheuristic algorithms show reasonable convergence characteristics for the corresponding PV models. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions, including CO2 emissions, are an issue in the energy sector that must be addressed urgently. The energy sector, including electricity, has been given a global aim of net zero emissions (NZE). This article examines three scenarios for reaching net-zero emissions in power supply. These scenarios are baseline, NZE1, and NZE2. The baseline scenario represents power plant capacity planning based on existing regulations in the base year. The net zero emissions consisting of the NZE1 and NZE2 scenarios aim to achieve net zero emissions by 2060. The NZE1 and NZE2 scenarios differ in the usage of nuclear power plant technology. The NZE1 scenario employs advanced costs for small modular reactors and large reactors technology, whilst the NZE2 scenario employs the low cost of small modular reactors and large reactors. The three scenarios were implemented and examined using the low emissions analysis platform software. The analytical results demonstrate that the NZE1 and NZE2 scenarios can meet the net zero emission objective by 2058. The baseline scenario results in power plant capacity planning with an average annual CO2 emission growth rate of 3.58%. On the other hand, the baseline scenario has the lowest investment expenses, at only 44 billion USD. [ABSTRACT FROM AUTHOR]

This article presents the issue of the impact of the operation of prosumer installations on the operation of the distribution network. The emergence of a very large number of distributed generation units in electric distribution networks (in particular, photovoltaic installations in low-voltage grids) can be associated with both positive and negative consequences. Positive consequences can include, among others, the environmental aspect of electricity generation. Negative consequences, on the other hand, can include technical problems of power grid operation, manifested, for example, in changes that can be observed in steady states, in the values of such parameters of grid operation as voltage level, values of branch currents, or power and energy losses. The issue discussed will be illustrated by a calculation example considering, among other things, the size and location of the prosumer installation. [ABSTRACT FROM AUTHOR]

Zinc‐air batteries (ZABs) have attracted considerable attention for their high energy density, safety, low noise, and eco‐friendliness. However, the capacity of mechanically rechargeable ZABs was limited by the cumbersome procedure for replacing the zinc anode, while electrically rechargeable ZABs suffer from issues including low depth of discharge, zinc dendrite and dead zinc formation, and sluggish oxygen evolution reaction, etc. To address these issues, we report a hybrid redox‐mediated zinc‐air fuel cell (HRM‐ZAFC) utilizing 7,8‐dihydroxyphenazine‐2‐sulfonic acid (DHPS) as the anolyte redox mediator, which shifts the zinc oxidation reaction from the electrode surface to a separate fuel tank. This approach decouples fuel feeding and electricity generation, providing greater operation flexibility and scalability for large‐scale power generation applications. The DHPS‐mediated ZAFC exhibited a superior peak power density of 0.51 W/cm2 and a continuous discharge capacity of 48.82 Ah with ZnO as the discharge product in the tank, highlighting its potential for power generation. [ABSTRACT FROM AUTHOR]

Electricity market violations affect the overall operations of the electricity market. This paper explores the evolutionary stability strategies of electricity generation enterprises and electricity consumers under two modes: traditional regulation and blockchain regulation to analyze blockchain technology's mechanism and conditions in solving electricity market violations. The experimental results indicate that the likelihood of consumers accepting electricity and the regulatory capacity of regulatory agencies play a crucial role in determining the violation approach adopted by electricity generation enterprises. Under traditional regulatory models, due to information asymmetry, regulatory agencies may not be able to detect violations promptly. Meanwhile, electricity consumers may choose to accept violations by power generation companies due to high appeal costs. Blockchain technology enables regulatory agencies to improve their regulatory capabilities by eliminating information asymmetry, reducing the cost of complaints from electricity consumers, thereby elevating the risk for enterprises engaging in market violations and optimizing the evolutionary game towards an optimum state. [ABSTRACT FROM AUTHOR]

Paying close attention to the environmental risks associated with traditional forms of energy, particularly the serious issue of global warming caused by the release and concentration of greenhouse gases, has become a paramount concern. This growing awareness has prompted governments worldwide to prioritize the benefits of alternative energy sources and develop comprehensive plans accordingly. Moreover, it is crucial to thoroughly consider and explore existing potentials in our quest for sustainable energy solutions. One such potential lies in the energy loss experienced due to gas pressure reduction in urban gas reduction stations. Furthermore, as we increase the amount of natural gas preheating, we observe a corresponding rise in the temperature within the expansion turbine, ultimately resulting in a significant boost in power generation. In this paper, we delve into the exciting possibility of harnessing electricity generation by utilizing a turbo expander within a municipal gas pressure reduction station. To maximize the work generation capacity, we propose preheating the gas input to the turbo expander using geothermal energy. Our study reveals that the turbo expander exhibits optimal performance, boasting an impressive isentropic efficiency of 92.3%, when the inlet temperature is maintained at 84 °C and the amount of preheating reaches 192.3 kJ/kg. [ABSTRACT FROM AUTHOR]

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 Mt CO 2 , eq /year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P 75 and P 90 are 10.08% and 19.22%, respectively, lower than the energy value in P 50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas. [ABSTRACT FROM AUTHOR]

Global concern over socioeconomic activities' environmental impact is undeniable, given their implications for climate and human welfare. In our study, we analyze Pakistan's landscape, with emphasis on the effects of electricity generation and consumption (both symmetric and asymmetric), energy trade, and the role of information and communication technology (ICT). We harnessed data from 1993 to 2020, sourced from World Development Indicators, Climate Watch, and the USA EIA. Our analysis involved application of the Nonlinear Autoregressive Distributed Lag model and the Autoregressive Distributed Lag bounds testing methods. Our results reveal that electricity consumption, whether experiencing positive or negative shocks, exhibits a tendency to elevate emissions over the long term. Similarly, shocks in electricity generation from fossil fuels, whether positive or negative, have adverse environmental consequences both in the short and long run. A negative shock in electricity generation from renewable and alternative sources triggers environmental degradation. Remarkably, nuclear energy plays a pivotal role in bolstering environmental sustainability in Pakistan. Conversely, we identified factors contributing to ecological degradation, including imports of coal, crude oil, metallurgical coke, and ICT usage, in the long run. Equally, trade and natural resources rent emerge as mitigating factors over time. Ultimately, our study underscores the importance of diversifying the energy mix, with a particular emphasis on expanding the renewable sources for reducing emissions and fostering environmental sustainability in Pakistan. Additionally, our research suggests that leveraging natural resources rent and trade capabilities can pave the way for an eco-friendlier future in the country. This study assesses the environmental impact of electricity generation by source, electricity consumption, energy trade, and ICTs in Pakistan. The findings confirm that both positive and negative shocks in fossil fuel electricity generation and consumption detrimentally affect the environment. Coal, crude oil, metallurgical coke imports, and ICTs degrade the environment, while nuclear electricity generation, natural resources rent, and trade improve it. Integrating energy diversification with focus on promoting renewable energy sources has the potential to significantly reduce emissions and advance long-run environmental sustainability. Strategically utilizing natural resources rent and trade capabilities can aid in establishing an environmental sustainability in Pakistan. [ABSTRACT FROM AUTHOR]

This research presents a renewable energy system that takes advantage of the energy potential available in the territory. This study emerges as a relevant option to provide solutions to geological risk areas where there are buildings that, due to emergency situations at certain times of the year during deep winter, are a target of danger and where its inhabitants would find it difficult to abandon their properties. The record of mass movements covering the city of Cuenca-Ecuador and part of the province has shown that the main triggering factor of this type of movement comprises the geological characteristics of tertiary formations characterized by lithological components that become unstable in the presence of water and due to their slopes being pronounced. Hybrid systems are effective solutions in distributed electricity generation, especially when it comes to helping people and their buildings in times of great need and the required electricity generation is basic. A hybrid photovoltaic, wind and hydrokinetic system has been designed that supplies electrical energy to a specific area on the opposite geographical side that is completely safe. The renewable energy system is connected to the public electricity grid available on site; however, in the event of an emergency the grid is disconnected for safety and only the hybrid system will work with the support of a battery backup system. In this study, the Homer Pro simulation tool was used and its results indicate that renewable systems that include PV, HKT and WT elements are economically viable, with a COE of USD 0.89/kWh. [ABSTRACT FROM AUTHOR]

Wave energy, as a significant renewable and clean energy source with vast global reserves, exhibits no greenhouse gas or other pollution during real-sea operational conditions. However, throughout the entire lifecycle, wave energy convertors can produce additional CO2 emissions due to the use of raw materials and emissions during transportation. Based on laboratory test data from a wave energy convertor model, this study ensures consistency between the model and the actual sea-deployed wave energy convertors in terms of performance, materials, and geometric shapes using similarity criteria. Carbon emission factors from China, the European Union, Brazil, and Japan are selected to predict the carbon emissions of wave energy convertors in real-sea conditions. The research indicates: (1) The predicted carbon emission coefficient for unit electricity generation ( E F co 2 ) of wave energy is 0.008–0.057 kg CO2/kWh; when the traditional steel production mode is adopted, the E F co 2 in this paper is 0.014–0.059 kg CO2/kWh, similar to existing research conclusions for the emission factor of CO2 for wave energy convertor (0.012–0.050 kg CO2/kWh). The predicted data on carbon emissions in the lifecycle of wave energy convertors aligns closely with actual operational data. (2) The main source of carbon emissions in the life cycle of a wave energy converter, excluding the recycling of manufacturing metal materials, is the manufacturing stage, which accounts for 90% of the total carbon emissions. When the recycling of manufacturing metal materials is considered, the carbon emissions in the manufacturing stage are reduced, and the carbon emissions in the transport stage are increased, from about 7% to about 20%. (3) Under the most ideal conditions, the carbon payback period for a wave energy convertor ranges from 0.28 to 2.06 years, and the carbon reduction during the design lifespan (20 years) varies from 238.33 t CO2 (minimum) to 261.80 t CO2 (maximum). [ABSTRACT FROM AUTHOR]

In an environment marked by dual carbon goals and substantial fluctuations in coal market prices, coal power generation enterprises face an urgent imperative to make scientifically informed decisions regarding production management amidst significant market uncertainties. To tackle this challenge, this paper proposes a methodology for optimizing electricity generation side market decisions and assessing risks using quantile data-driven information-gap decision theory (QDD-IGDT). Initially, a dual-layer decision optimization model for electricity production is formulated, taking into account coal procurement and blending processes. This model optimizes the selection of spot coal and long-term contract coal prices and simplifies the dual-layer structure into an equivalent single-layer model using the McCormick envelope and Karush–Kuhn–Tucker (KKT) conditions. Subsequently, a quantile dataset is generated utilizing a short-term coal price interval prediction model based on the quantile regression neural network (QRNN). Interval constraints on expected costs are introduced to develop an uncertainty decision risk measurement model grounded in QDD-IGDT, quantifying decision risks arising from coal market uncertainties to bolster decision robustness. Lastly, case simulations are executed by using real production data from a power generation enterprise, and the dual-layer decision optimization model is solved by employing the McCormick–KKT–Gurobi approach. Additionally, decision risks associated with coal market uncertainties are assessed through a one-dimensional search under interval constraints on expected cost volatility. The findings demonstrate the effectiveness of the proposed research methodology in cost optimization within the context of coal market uncertainties, underscoring its validity and economic efficiency. [ABSTRACT FROM AUTHOR]

Several northern European countries have announced ambitious plans to become carbon neutral already before the year 2050. Recent research has, however, highlighted how potential bottlenecks in raw material and resource availability could significantly delay or hinder wind and solar photovoltaic (PV) expansion and continued biomass usage in parts of Europe. To address this issue, this paper assesses how exposed the national energy and climate plans (NECPs) of Finland, Estonia, Germany, Sweden, and Denmark are to resource limitations and techno-economic risks by reviewing and analysing 2030 NECP targets compared to statistical energy use data in these countries. The results indicate that the NECPs of Denmark and Germany are particularly exposed to risks related to global raw material availability, as Denmark plans to rapidly grow the share of wind and solar PV in electricity generation to 81% and 13% by 2030, respectively, followed by Germany, which outlines a 39% and 16% share of wind and solar PV in its national climate strategy. The NECPs of Finland and Germany are also shown to be vulnerable to limitations in biomass availability, as there is a significant disparity between the projected biomass usage and legally binding European Union (EU) targets for land use, land use change, and forestry (LULUCF) sector emissions in 2030 in these countries. [ABSTRACT FROM AUTHOR]

Combustion power plants emit carbon dioxide (CO2), which is a major contributor to climate change. Direct emissions measurement is cost-prohibitive globally, while reporting varies in detail, latency, and granularity. To fill this gap and greatly increase the number of power plants worldwide with independent emissions monitoring, we developed and applied machine learning (ML) models using power plant water vapor plumes as proxy signals to estimate electric power generation and CO2 emissions using Landsat 8, Sentinel-2, and PlanetScope imagery. Our ML models estimated power plant activity on each image snapshot, then an aggregation model predicted plant utilization over a 30-day period. Lastly, emission factors specific to region, fuel, and plant technology were used to convert the estimated electricity generation into CO2 emissions. Models were trained with reported hourly electricity generation data in the US, Europe, and Australia and were validated with additional generation and emissions data from the US, Europe, Australia, Türkiye, and India. All results with sufficiently large sample sizes indicate that our models outperformed the baseline approaches. In validating our model results against available generation and emissions reported data, we calculated the root mean square error as 1.75 TWh (236 plants across 17 countries over 4 years) and 2.18 Mt CO2 (207 plants across 17 countries over 4 years), respectively. Ultimately, we applied our ML method to plants that constitute 32% of global power plant CO2 emissions, as estimated by Climate TRACE, averaged over the period 2015–2022. This dataset is the most comprehensive independent and free-of-cost global power plant point-source emissions monitoring system currently known to the authors and is made freely available to the public to support global emissions reduction. [ABSTRACT FROM AUTHOR]

As electric hot water cylinders (HWCs) have a large capacity for thermal storage, they are well-suited for Demand Side Management (DSM). This paper compares different methods of HWC temperature control and presents a methodology to assess the amount of thermal storage available in HWCs for demand side management based on use behaviour in different household types. Simple stochastic methods for domestic hot water (DHW) demand prediction were employed to design a smart controller that produced lower rates of unmet DHW demand and higher available storage than setpoint and ripple controllers. The average storage available for DSM from the use of this smart controller is predicted to be between 3.63 and 7.20 kWh per household. These results indicate the use of HWCs for thermal storage is a low-cost viable option for peak-shaving of power system load and could decrease power system greenhouse gas (GHG) emissions in countries such as Aotearoa New Zealand, where GHG-emitting electricity generation is primarily used to meet peak loads. [ABSTRACT FROM AUTHOR]

Floating photovoltaic (FPV) systems have generated much interest in the literature, due to the potential synergies with existing hydropower reservoirs. This is of particular relevance to Aotearoa New Zealand, where the national grid's heavy reliance on hydro resources has left the country prone to energy shortages during dry years. This paper investigates the potential implications of a 1-gigawatt peak fleet of distributed FPV systems on seven hydro schemes around the country. The results show that an FPV fleet in Aotearoa New Zealand could provide additional electricity generation when the controlled hydropower storage is low. A larger, wider rollout would allow for the reservoirs to replenish after the country's unique winter peak in electricity demand. [ABSTRACT FROM AUTHOR]

This research applies exergy and exergoeconomic analyses to a turbocharged ethanol-fuelled engine enriched with hydrogen to operate in lean mixture conditions. Utilizing simulation data, the study examines the impacts of up to 6% of hydrogen fraction in the fuel for engine operation in the lean range from stoichiometric to fuel/air mixture equivalence ratio 0.7 are investigated. The exergy analysis revealed that hydrogen enrichment in lean operation can improve global exergy efficiency by nearly 30%. This improvement in efficiency was accompanied by significant reductions in exergy destruction and heat-based exergy transfer. The exergoeconomic evaluation indicated that hydrogen energy density and lean operation can reduce the fuel cost rate by up to 23%. The best operational scenario produced 29% decrease in specific exergoeconomic cost of electricity generation and 31% reduction in total losses. • Hydrogen addition in lean operation with ethanol reduces exergy destruction. • Global exergy efficiency is improved by nearly 70% with up to 6% hydrogen addition. • Hydrogen addition does not have a major impact on heat-based exergy transfer. • 6% hydrogen addition causes 31% reduction in exergoeconomic cost rate of total losses. • Leaner mixtures and higher hydrogen addition enhances the sustainability index. [ABSTRACT FROM AUTHOR]

Efficient and stable operation of direct carbon solid oxide fuel cells (DC-SOFCs) hinges upon the kinetics of the reverse Boudouard reaction. Crop straw, possessing exceptional physicochemical properties, exhibits great potential to promote the reverse Boudouard reaction and cell performance. Here, we successfully developed two representative biochars derived from sesame and eggplant straw, conducting thorough physicochemical characterization and evaluating their application in DC-SOFCs. Results revealed a substantial impact of biochar intrinsic properties on DC-SOFC performance. At 850 °C, the single cell employing sesame straw biochar achieved the highest output of 228 mW cm−2, while the DC-SOFC fueled with eggplant straw biochar yielded 214 mW cm−2, both surpassing the 173 mW cm−2 output of the activated carbon-powered cell. Furthermore, the sesame straw biochar-fueled cell exhibited the longest operational lifetime of 30.7 h, with a fuel utilization as high as 34.4% at 850 °C and 50 mA. Similarly, the operational lifetime and fuel utilization of the eggplant straw biochar-fueled DC-SOFC were recorded as 21.1 h and 23.6%, respectively. These outcomes consistently outperformed activated carbon-powered DC-SOFC, demonstrating the crucial role of crop straw-derived biochar in enhancing DC-SOFC stability and efficiency. The improved cell performance was ascribed to the biochar's porous structure, larger specific surface area, more disordered carbon, and naturally occurring metal catalysts, which effectively accelerated the reverse Boudouard reaction and ensured an abundant provision of carbon monoxide for DC-SOFC operation. • Sesame and eggplant straw biochars are developed as excellent fuels for DC-SOFCs. • Both biochars display excellent physicochemical properties for carbon gasification. • DC-SOFC performance depends strongly on the characteristics of the biochars. • Sesame straw biochar-fueled cell gives the best output of 228 mW cm−2 at 850 °C. • Biochar-fueled DC-SOFCs exhibit superior operational lifetime and fuel utilization. [ABSTRACT FROM AUTHOR]

This study identifies and summarizes potential risks to operations, regulatory compliance, supply chains, and infrastructure of the electric power industry from changing surface water conditions resulting from global climate change. The results help inform companies/utilities seeking to incorporate climate change risk in their planning and decision‐making processes by ranking risk severity and likelihood of occurrence on both a regional basis and by risk receptor. The assessment includes identification of potential risks to: (1) thermal generating, (2) hydroelectric, (3) land‐based renewable generating, and (4) transmission and distribution assets. These risks may result from such projected changes as reduced water availability (e.g., for hydroelectric or once‐through cooling), increased water temperatures (e.g., decrease in cooling efficiency, inability to meet discharge permit conditions), increased flood severity (e.g., increased streambank erosion and/or damage to river‐adjacent infrastructure), and decreased water quality (e.g., from increased transport of sediment and dissolved solids). The potential risks identified from this qualitative risk‐assessment are documented in a graphical format depicting both severity and likelihood. This approach allows for comparison of risks across a portfolio and for future prioritization of adaptation strategies. A total of 32 risks were identified in the study, including nine risks to infrastructure, six risks to operations, four risks to supply chain, and 13 environmental/regulatory risks. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing urgency among energy-intensive companies to find innovative ways of mitigating the negative financial impacts of rising fuel and electricity prices. Consequently, companies are exploring new technological solutions to lower electricity costs, such as investing in their own power generation sources or storage systems. In this context, this article presents a data-driven optimization-based framework to manage and optimize the operation of a hybrid energy system within industries characterized by substantial power requirements. The framework encompasses several key aspects: electricity generation, self-consumption, storage, and electric grid interaction. The case of an energy-intensive company specializing in wood processing and office furniture production is evaluated. This study explored two system configurations of hybrid energy systems within an energy-intensive company. The result of the analyzed case shows that the system's flexibility is enhanced by its ability to store energy, resulting in electricity cost savings of nearly 72% and total operating cost savings of 20%. [ABSTRACT FROM AUTHOR]

Solar‐based Distributed Generation (DG) powered Electric Vehicles (EVs) charging stations are widely adopted nowadays in the power system networks. In this process, the distribution grid faces various challenges caused by intermittent solar irradiance, peak EVs load, while controlling the state of charge (SoC) of batteries during dis(charging) phenomena. In this paper, an intelligent energy management scheme (IEMS)‐based coordinated control for photovoltaic (PV)‐based EVs charging stations is proposed. The proposed IEMS optimizes the PV generation and grid power utilization for EV charging stations (EVCS) by analysing real‐time meteorological and load demand data. The coordinated control of EMS provides power flow between PV generation, distribution grid, and EVs battery storage in a manner which results in the reduction of peak power demand by a factor of two. Further, the adaptive neuro‐based fuzzy control approach includes forecasting solar‐based electricity generation and EVs loads demand predictions to optimize IEMS according to the Indian power scenario. The proposed IEMS optimally utilizes the buffer batteries system for reducing the peak electricity demand with low system losses and reducing the impact of EVs charging load on distribution grid. The results are analysed using the digital simulation model and validated with real‐time hardware‐in‐loop experimental setup. [ABSTRACT FROM AUTHOR]

As bulky pollutants in industrial and agricultural wastewater, nitrate and formaldehyde pose serious threats to the human health and ecosystem. Current purification technologies including chemical and bio‐/photo‐/electro‐chemical methods, are generally high‐cost, time‐consuming, or energy‐intensive. Here, we report a novel formaldehyde‐nitrate battery by pairing anodic formaldehyde oxidation with cathodic nitrate reduction, which simultaneously enables wastewater purification, electricity generation, and the production of high‐value‐added ammonia and formate. As a result, the formaldehyde‐nitrate battery remarkably exhibits an open‐circuit voltage of 0.75 V, a peak power density of 3.38 mW cm−2 and the yield rates of 32.7 mg h−1 cm−2 for ammonia and 889.4 mg h−1 cm−2 for formate. In a large‐scale formaldehyde‐nitrate battery (25 cm2), 99.9 % of nitrate and 99.8 % of formaldehyde are removed from simulated industrial wastewater and the electricity of 2.03 W⋅h per day is generated. Moreover, the design of such a multi‐functional battery is universally applicable to the coupling of NO3− or NO2− reduction with various aldehyde oxidization, paving a new avenue for wastewater purification and chemical manufacturing. [ABSTRACT FROM AUTHOR]

As bulky pollutants in industrial and agricultural wastewater, nitrate and formaldehyde pose serious threats to the human health and ecosystem. Current purification technologies including chemical and bio‐/photo‐/electro‐chemical methods, are generally high‐cost, time‐consuming, or energy‐intensive. Here, we report a novel formaldehyde‐nitrate battery by pairing anodic formaldehyde oxidation with cathodic nitrate reduction, which simultaneously enables wastewater purification, electricity generation, and the production of high‐value‐added ammonia and formate. As a result, the formaldehyde‐nitrate battery remarkably exhibits an open‐circuit voltage of 0.75 V, a peak power density of 3.38 mW cm−2 and the yield rates of 32.7 mg h−1 cm−2 for ammonia and 889.4 mg h−1 cm−2 for formate. In a large‐scale formaldehyde‐nitrate battery (25 cm2), 99.9 % of nitrate and 99.8 % of formaldehyde are removed from simulated industrial wastewater and the electricity of 2.03 W⋅h per day is generated. Moreover, the design of such a multi‐functional battery is universally applicable to the coupling of NO3− or NO2− reduction with various aldehyde oxidization, paving a new avenue for wastewater purification and chemical manufacturing. [ABSTRACT FROM AUTHOR]

Ubiquitous energy harvesting technologies require new types of renewable, sustainable, and clean energy to solve the problems of fossil fuels. Although various nanogenerators have been developed over the last decade, low current density, requirements for complicated management circuits, generation of direct current (DC) outputs, and significant performance degradation under humid atmospheres have been problems limiting practical applications. This paper reports that the tribovoltaic and moisture‐enabled electricity generation effects can be achieved in a layered double hydroxide (LDH) by applying a metal brushing mode. The ZnAl‐LDH shows enhanced tribovoltaic nanogenerator (TVNG) performance even in a high‐humidity environment coupled with the moisture‐enabled electricity generated by the concentration gradient of a water molecule through the interlamellar structures of the LDHs. The spontaneous dissociation of water in the interior of LDHs removes the hole generated by the tribovoltaic effect, allowing high efficiency of an output voltage of 693.38 mV and current density of 65.48 mA m−2 even under low‐applied force (≈3.5 mN) at relative humidity 80%. The proposed TVNG provides a proof of concept for an all‐in‐one device consisting of a generator and capacitor because the LDH reveals a spontaneous charging performance similar to that of a capacitor. [ABSTRACT FROM AUTHOR]

Water is known for dissipating electrostatic charges, but it is also a universal agent of matter electrification, creating charged domains in any material contacting or containing it. This new role of water was discovered during the current century. It is proven in a fast-growing number of publications reporting direct experimental measurements of excess charge and electric potential. It is indirectly verified by its success in explaining surprising phenomena in chemical synthesis, electric power generation, metastability, and phase transition kinetics. Additionally, electrification by water is opening the way for developing green technologies that are fully compatible with the environment and have great potential to contribute to sustainability. Electrification by water shows that polyphasic matter is a charge mosaic, converging with the Maxwell–Wagner–Sillars effect, which was discovered one century ago but is still often ignored. Electrified sites in a real system are niches showing various local electrochemical potentials for the charged species. Thus, the electrified mosaics display variable chemical reactivity and mass transfer patterns. Water contributes to interfacial electrification from its singular structural, electric, mixing, adsorption, and absorption properties. A long list of previously unexpected consequences of interfacial electrification includes: "on-water" reactions of chemicals dispersed in water that defy current chemical wisdom; reactions in electrified water microdroplets that do not occur in bulk water, transforming the droplets in microreactors; and lowered surface tension of water, modifying wetting, spreading, adhesion, cohesion, and other properties of matter. Asymmetric capacitors charged by moisture and water are now promising alternative equipment for simultaneously producing electric power and green hydrogen, requiring only ambient thermal energy. Changing surface tension by interfacial electrification also modifies phase-change kinetics, eliminating metastability that is the root of catastrophic electric discharges and destructive explosions. It also changes crystal habits, producing needles and dendrites that shorten battery life. These recent findings derive from a single factor, water's ability to electrify matter, touching on the most relevant aspects of chemistry. They create tremendous scientific opportunities to understand the matter better, and a new chemistry based on electrified interfaces is now emerging. [ABSTRACT FROM AUTHOR]