Search

Climatic phenomena, particularly hydrological droughts, have led to significant changes in reservoir operation strategies. Multipurpose reservoir operations are essential for effectively managing stored water resources for various activities like electricity generation and agricultural irrigation. Despite considerable efforts to support decision making for each economic activity, there remains a weak integration across these sectors in joint analyses. To address this, an integrated approach combining a model of a large power system and a model at the basin scale is proposed to analyze the operation of both power and agricultural systems. This approach allows evaluation of the operating policies of a multipurpose reservoir and its performance at both the local and regional scales under different hydrological scenarios. A modification is implemented whereby the priority of water extraction to agricultural users is increased. Its effects are assessed for different hydrological trajectories in a case study in the Laja Lake basin in southern Chile, the biggest Chilean basin with a capacity of up to 5,500 Hm3. The Laja Lake, a multipurpose reservoir with substantial hydroelectric generation capacity and extensive agricultural areas plays a crucial role in the operation of the national power system. Based on an analysis of 2025, it is demonstrated that hydrological changes directly impact electrical and agricultural performance. Drought conditions increase thermal generation, costs, emission intensity, and water deficits. Furthermore, the policy modification reveals tradeoffs between the power sector's emissions and agricultural water deficits. For drier scenarios, increasing agricultural extraction priority results in low additional operational costs and emissions from the power system, which supports adopting a policy aligned with netzero objectives. [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]

Microbial fuel cell (MFC), a bio-electrochemical device that exploits electroactive microbes, has gained more attention in developing countries, such as Indonesia. Unfortunately, studies related to bio-electrochemistry are often constrained due to the need for precise and high-cost instrumentation, such as data logger/acquisition or data logging-multimeter for continuously monitoring electricity generation of MFCs in the rigid time interval. This present work aimed at two issues: (1) to evaluate the use of a low-cost microcontroller-based data logger, the developed multi-channels Arduino UNO-based data logging system, for monitoring the electricity generation of ten MFC bioreactors simultaneously, and (2) to evaluate the electrochemical performance of MFCs biocatalysts by ten electroactive microbes isolated from aquaculture pond sediment in Indonesia. The monitoring system worked with a multi-channels Arduino UNO, a multiplexer, an external 16-bit ADC (analogue to digital converter) ADS1115, and a RTC (real time clock) module. The MFC performance was evaluated in the terms of open circuit voltage and close circuit voltage (polarization curve, power density, and losses). Statistical analysis confirmed the high accuracy of the developed system with the average of absolute and relative error values of 1.21 mV and 1.26%, respectively, comparable to traditional multimeter utilized for MFC electricity measurement. These results suggested that the developed data logging system could be a considerable option as a low-cost monitoring device for electrochemical studies of MFCs. Electrochemical performances of ten anodic biocatalysts were also evaluated, suggesting that there were three effective bacteria (isolate KCf2, KCf4, and KCf10) for producing relatively stable bioelectricity in the reactor of MFCs. These three electroactive microbes can produce power density of 0.069 W m−2, 0.021 W m−2, and 0.010 W m−2, respectively. [ABSTRACT FROM AUTHOR]

PV(photovoltaic) systems have been deployed more in the recent years to support green energy generation. In the recent era, grid tied PV has been sparked by the emergence of the electricity market and offers an alternative solution to traditional fossil fuel-based electricity generation. A few of the potential impacts of solar PV on grid are false tripping of feeders, unwanted tripping, unnecessary islanding, and blind protection. In this research paper, different artificial intelligence techniques are tested in order to overcome the protection challenges. The PSCAD/EMTDC software package is used to analyze a section of the power system, and an algorithm has been constructed using Python v3.8 and MATLAB 2018. On an IEEE-9 BUS power system connected to a PV source, the Artificial Neural Network, Naive Bayes, Support Vector Machine, Random Forest, and Convolution Neural Network (CNN) algorithms are implemented to classify the faults. The suggested methods are proven effective for both in-zone and out-of-zone problems on power lines interconnected with solar park. The proposed techniques have been validated using total 16,320 internal and external fault cases with a wide range of system parameters alteration. In the proposed system, the effectiveness of several machine learning and deep learning techniques is compared. The obtained results demonstrate that CNN provides greater accuracy in the presence of a PV source, but at the same time, it is appropriate for a large number of data sets. The fault classification accuracy acquired is adequate and demonstrates the adaptability of the proposed approach. [ABSTRACT FROM AUTHOR]

Electrical power generation in Oman depends mainly on natural gas, while in rural areas it is based mainly on diesel fuel. Oman has committed to net-zero emissions by 2050, in line with the Paris Agreement's goal of limiting global warming to 1.5°C. The objective of this paper is to propose an optimal energy mix model for electricity generation from various energy sources, such as gas, diesel, wind, and photovoltaic, that considers more renewable sources in the energy mix. The optimization model minimizes various costs such as construction cost, operation cost, fuel cost, and carbon emissions, while satisfying the load demand. In this paper, Masirah Island is selected to perform our analysis. Several scenarios are contemplated, including grid extension to the island. The results found that gas-powered generators with 20% PV and storage are the best option in terms of the levelized cost of electricity. The grid extension is an economically feasible option if the existing load is more than doubled. [ABSTRACT FROM AUTHOR]

Biotrickling filters (BTFs) designed for the treatment of volatile organic compounds (VOCs) have attracted much attention in the field of environmental engineering. As one of the main pollutants in VOCs, xylene is an aromatic hydrocarbon that poses a threat to human health. In this study, a biotrickling filter-microbial fuel cell (BTF-MFC) system was constructed to treat p -xylene by using a BTF filled with conductive packing materials as the biocathode for microbial fuel cell (MFC). The BTF-MFC showed good p- xylene removal performance even at high inlet concentrations. The elimination capacity reached 27.67 g/m3h at a 400 s empty bed residence time, and the removal efficiency reached 98.31% at a 300 s empty bed residence time. The conductive packing materials added to the BTF-MFC effectively promoted bacterial colonization, which as a result promoted p -xylene degradation and increased power output. The output voltage reached 725 mV, with a power density of 3.50 W/m2. The abundance of the genera Hydrogenophaga , Flavihumibacter , Chryseobacterium , and Mycobacterium , which have an excellent degradation capacity for aromatic compounds, increased during BTF-MFC operation. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary: Due to the energy crisis and environmental concerns, wind power has seen a considerable increase in use over the past 10 years as a source of renewable energy. Since wind is intermittent and variable, it is obvious that as penetration levels rise, the influence of wind power generation on the electric power system must be considered. Wind power forecasting is essential because large‐scale wind power integration will make it more difficult to plan, operate, and control the power system. An accurate forecast is an efficient way to deal with the operational problems brought on by wind variability. To better utilize wind energy resources, it is essential to increase prediction accuracy. Frequently, the noise in the dataset causes the ramp events to be misclassified or overestimated. The main emphasis of this study is the pre‐processing of wind power data that produces precise time‐series data while reducing noise or artifact content and maintaining the swing feature of the original data. For this task, the data augmentation approach is proposed, where the augmented data (synthetic data) will be added up with the training data, in such a way as to make the strategy useful for real‐time applications. As the next step, the significant features, such as higher‐order statistical features and lower‐order statistical features are extracted. The extracted features act as the input to the recurrent neural network (RNN) classifier, the weights of which are tuned using the proposed honey badger crow (HBCro) optimization algorithm. The proposed HBCro optimization algorithm acts as the major contribution of the proposed model, and it is modeled with the integration of the concepts of the crow search optimization (CSO) algorithm and the honey badger optimization algorithm (HBA). The proposed system is simulated in MATLAB and the effectiveness of the proposed method is validated by comparison with other conventional methods in terms of Error measures. Furthermore, the developed HBCro‐based RNN obtained efficient performance in terms of MSE, MAE, RMSE, RMPSE, MAPE, MARE, MSRE, and RMSRE of 0.1578, 0.0442, 0.2102, 123.238, 124.72, 0.9944, 1.1799, and 1.0732, respectively. [ABSTRACT FROM AUTHOR]

Tidal stream turbines (TST) are a promising option for electricity generation to meet the ever-increasing demand for energy. The actuator disk (AD) method is often employed to represent a TST, to evaluate the TST operating in a tidal flow. While this method can effectively reduce the computational cost and provide accurate prediction of far-wake flow conditions, it falls short of fully characterising critical hydrodynamics elements. To address this limitation, a hybrid method is implemented by coupling AD with the blade element momentum (BEM) theory, using detailed performance data, such as thrust, to enhance the prediction of the wake effects. This work focuses on the development of a hybrid BEM–AD method using Reynolds-Averaged Navier–Stokes (RANS) turbulence models within computational fluid dynamics (CFD). Two variations and a hybrid modification of an AD model are presented in this paper. The first modified variation is a velocity variation that takes into account velocity profile inflow into the disk's configuration. The second modified variation is a radial variation that integrates the blade element theory into the disk's configuration. The hybrid modified model combines both the velocity profiles influenced and blade element theory in the design and analysis of the actuator disk. Several key investigations on some of the pre-solver parameters are also investigated in this research such as the effect of changing velocity and radial distance on the porosity and loss coefficient of the actuator disk performance. Importantly, this work provides an improved method to evaluate the key wake effects from a TST array which is crucial to determine the power performance of the TST array. [ABSTRACT FROM AUTHOR]

This comparison of five power plants in West Texas is intended to provide various decision-makers and stakeholders with a holistic picture of the life-cycle environmental impacts associated with these power plants. A key contribution of this analysis is that we assumed all power plants generate the same amount of electricity over a 30-year life, taking a 500 MW combined-cycle gas turbine (CCGT) plant as a benchmark. Also, in two cases, we added battery storage to wind and solar PV facilities to render them nearly as dispatchable as the CCGT. We included the entire supply chain supporting electricity generation, which encompassed raw material sourcing, processing, manufacturing, operations, and product end of life, also called "cradle to grave". We report on 18 environmental impacts using ReCiPe midpoint (H) impact assessment. The supply chains are global, and impacts are felt differently by host communities across the world. The results can help stakeholders identify hotspots across numerous supply chains with the highest environmental impacts. We discuss some remedial measures and challenges to inform future analysis by the research community. [ABSTRACT FROM AUTHOR]

Research-based education is a long-standing tradition at the University of Tartu (UT). Basic knowledge of electrochemistry and the principles of developing electrochemical devices have been taught and implemented at UT since 1960. For instance, during then, self-made alkaline electrolysers were used to generate hydrogen. The hydrogen was further purified and used to saturate aqueous and non-aqueous electrolytes. The fundamental electrochemical research has formed a solid background on which the development of supercapacitors and Na+-ion or Li+-ion batteries is based today. Since 1991, the Ph.D., MSc and undergraduate students have investigated the properties of high surface–area carbon materials in non-aqueous electrolytes to develop energy conversion and storage devices with high energy and power density. Moreover, porous thin-film complex metal hydride–based hydrogen storage devices are also under study. The research of solid oxide fuel cells (SOFC) and polymer electrolyte membrane fuel cells (PEMFC) began at the UT in 2001 and 2010, respectively. Based on the collected knowledge, a sustainable green electricity and hydrogen generation-storage complex (GEHGSC) was constructed, consisting of solar cells and fuel cells for electricity generation, batteries for storage and electrolysers for hydrogen generation. The main aim of GEHGSC is to educate students, young scientists and local authorities specialized in sustainable energy technologies and applied electrochemistry. Electrolyzed hydrogen has been used for experimental testing of SOFC and PEMFC, produced at the Institute of Chemistry. The 300 bar hydrogen compressor has been installed, and thereafter, the PEMFC-powered self-driving car Iseauto, completed by contract for Auve Tech OÜ, has been fuelled with hydrogen produced by GEHGSC. [ABSTRACT FROM AUTHOR]

Within the modern greenhouse horticultural sector energy usage is planned using mathematical models that simulate the greenhouse's future performance. These models contain parameters whose values can be inaccurate which create errors in model predictions. This reduces the effectiveness of energy management and planning done using these models. This study proposes and demonstrates an algorithm to quantify the impact of parameter errors on greenhouse gas and electric power prediction uncertainty. The proposed algorithm introduces a Polynomial Chaos Expansion as a method for the sensitivity analysis in the domain of greenhouse horticulture. Contrary to commonly used sensitivity analyses, this approach introduces the analysis of higher order interactions into the domain of greenhouse horticultural research. It was found that for both electric power and gas production the HPS lamp power rating was the most influential individual parameter. Moreover, this study found that for power demand the uncertainty in parameters relating to the lamp system were far more impactful than those related to the crop or greenhouse structure, with a respective coefficient of variation of 24 %, 5 % and 5 %. This study makes a notable and novel conclusion that for parameters related to the greenhouse structure, larger groups of parameters were responsible for prediction uncertainty through higher order interactions of second to sixth order. These results reinforce the importance of future greenhouse research considering the impact of higher order parameter interactions on prediction uncertainty using the algorithm proposed in this study. • An electrical power and gas demand uncertainty analysis was performed. • A polynomial chaos expansion was used as part of an uncertainty analysis. • The proposed algorithm shows groups of parameters impacting prediction uncertainty. • Lamp intensity is the key factor for power and gas demand prediction uncertainty. [ABSTRACT FROM AUTHOR]

Moisture‐enabled electricity generation (MEG) is a prominent renewable energy harvesting technology in hydrovoltaic power generation, boasting the broadest energy harvesting spectrum. However, practical application faces limitations due to irreversible performance degradation caused by structural changes and moisture‐generated carrier (MGC) losses in Moisture‐enabled electricity (ME) materials, rendering them non‐renewable. This study introduces a rechargeable moisture‐enabled electricity cell (rMEC) based on dual ME functional layers and active metal electrodes. The rMEC demonstrates outstanding power generation performance, with a single cell providing an output voltage of 1.08 V and a power density of 5.83 µW cm−2 through redox assistance. Moreover, it can be recharged when MGCs are lost, utilizing the reversibility of the redox reaction (moisture of H2O2 solution) for self‐repair. Notebly, the rMEC maintains stable operation for over 2080 h and undergoes 100 charging/working cycles, marking the longest span life record in MEG research history. When exposed to industrial wastewater/gases with oxidation characteristics, the rMEC not only completes charging but also facilitates the reuse of toxic waste resources. The environmentally friendly rMEC, with its long cycle life, significantly overcomes the limitations of non‐renewable ME materials, serving as a paradigm for promoting iterative upgrades in MEG technology. [ABSTRACT FROM AUTHOR]

Russian hydrogen industry exists more than 100 years and is one of the biggest in the world. In 2021 Russia produced more than 5 mln tons of hydrogen (the 5th place in the world after China, US, EU-27, India), more than 1 mln tons of hydrogen were exported as hydrogen-contained production (ammonia, methanol, fertilizers). Following the global trends in the growth of production and use of hydrogen as an energy carrier, which plays the role of an important tool for reducing greenhouse gas emissions, decarbonization of energy, as well as the use of hydrogen in the transport sector and industry, the analysis of the current state and prospects for the development of hydrogen energy in Russia is carried out, the technologies of low-carbon hydrogen production at electric power, oil- and gas processing, logistics and technologies of transportation and storage of hydrogen, the world's leading and promising Russian developments are analyzed, the feasibility of their application in Russia is assessed. The main technology for hydrogen production in Russia remains SMR: by using this technology more than 95 % of hydrogen is produced. At the same time, electrolyzers are present in many industries (oil refineries, power generation, hydrometeorology, microelectronics, food industry etc.). Russian nuclear power plant giant Rosatom is seriously studying the possibility of producing hydrogen in nuclear power plants by using high-temperature technologies. There are no main hydrogen pipelines in Russia (only internal technological pipelines) because big industrial plants that use hydrogen construct their hydrogen facilities on-site. Smaller amounts of hydrogen are delivered to the customers in a compressed state and stored in steel cylinders or reservoirs. Currently, Russian companies and organizations process development in hydrogen transportation and storage in compressed (up to 700 bars, including polymer reservoirs), liquid and bound states. In addition to traditional hydrogen consumers (hydrocarbon refineries, chemical plants) and metallurgy (7 furnaces for iron production) Russian companies and organizations develop hydrogen energy storage units, and there are pilot projects in the commercial and heavy vehicles, buses, river boats). [ABSTRACT FROM AUTHOR]

As electricity systems become increasingly dominated by variable inverter-based generation (such as wind and solar photovoltaics (PV)), additional sources of variability appear, while the share of dispatchable thermal power plants decreases. Maintaining a stable grid frequency requires new sources of inertia to slow the frequency changes, as well as new sources of reserve power to counter the imbalances. This work uses a linear optimization modeling approach to analyze the reserve market of the electricity system during the transition to a carbon-free system with high shares of variable electricity generation. The modeling is performed for three regional contexts and with varying degrees of available flexibility technologies. As for the reserve supply, batteries, electrolyzers, electric boilers and heat pumps for district heating, curtailed wind and solar power, and hydropower and thermal power plants are all included. The results indicate that of these suppliers of reserves, the introduction of grid-scale batteries into the system drastically reduces the reserve market size. Only early in the transition is revenue from the reserve market greater than 5% of the total revenue for any technology. While the demand for reserves increases as the share of solar PV and wind power increases, the modeling reveals that access to flexible loads, storage units, and emulated inertia from wind power also increases. Depending on the choice of flexibility measures available, demand-side participation could play a major role in minimizing the cost of grid stability in the future. [ABSTRACT FROM AUTHOR]

Electricity generation in Europe is undergoing a fundamental change. The aim is to increase sustainability by reducing emissions. Each country has a different electricity mix, and there is no established method for measuring environmental impacts of electricity production with a single monetary indicator, in a uniform manner, and with country-specific data. To address this gap, a model that measures the costs of 19 environmental externalities (usually, types of emissions) has been developed. Using country-specific technologies, electricity mixes, and external cost rates, the development of external costs of generating electricity in 27 European countries between 2010 and 2030 is assessed and analyzed. The simulation results show that the external costs vary heavily between 2.1 and 22.4 euro cents per kWh in this period. Despite the initiated transformation of the energy systems in many EU countries, external costs per kWh are decreasing in only eight of them. This fact underlines the need for a drastic change in national energy strategies. Overall, the results show that more far-reaching policy measures are needed in order to significantly reduce the external costs of the energy sector in Europe. The article raises the level of granularity of research on the external costs of electricity in Europe by combining extensive country-specific emission data and country-specific external cost rates. [ABSTRACT FROM AUTHOR]

The studies have focused on changes in CO2 emissions over different periods, including the COVID-19 pandemic. Even if CO2 emissions are temporarily reduced during the pandemic according to annual figures, this may be misleading. Considering annual figures is important to understand the overall trend, but using data with much higher frequency (e.g., daily) is much better suited to investigate dynamic relationships and external effects. Therefore, this study comprehensively analyzes the association between CO2 emissions and disaggregated electricity generation (EG) sources across the globe by employing the novel wavelet local multiple correlation (WLMC) approach on daily data from 1st January 2020 to 31st March 2023. The results demonstrate that (1) based on the main statistics, daily CO2 emissions range between 69 MtCO2 and 116 MtCO2, indicating that there is an oscillation, but no sharp changes over the analyzed period. (2) based on the baseline regression using the dynamic ordinary least squares (DOLS) approach, the constructed estimation models have a high predictive ability of CO2 emissions, reaching ~ 94%; (3) in the further analysis employing the WLMC approach, there are significant externalities between EG resources, which affect CO2 emissions. The results present novel insights about time- and frequency-varying effects as well as a disaggregated analysis of the effect of EG on CO2 emissions, demonstrating the significance of the energy transition towards clean sources around the world. [ABSTRACT FROM AUTHOR]

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model. [ABSTRACT FROM AUTHOR]

This paper presents a numerical algorithm for environmental/economic load dispatch (EELD) with emissions constraints, which takes into account the emissions trading system's effect on electricity generation cost and is implemented using a Python computer program. The developed program is applied to a power system of six (6) fossil-fueled electricity generating units with NOx, SO2 and CO2 constraints and proved to be significantly beneficial not only for the environment but also for the power company and the consumers. The proposed algorithm uses multi-objective optimization and incorporates both fuel and emissions allowances costs. The schedule of generating units is calculated and the testing of all possible weighting factor combinations with resolution of 0.01 is carried out showing that the proposed algorithm is fast, cost effective and environmentally friendly. [ABSTRACT FROM AUTHOR]

Summary: At present, the implementation of fuel cell power generation system in electric vehicles has been widely concerned. But the voltage produced by fuel cells is generally relatively low and cannot be supplied directly to the load or grid. To raise its output voltage, a new single‐switch dc‐dc boost topology is put forward in this article. That converter has some characteristics as below: (1) The common ground structure availably avoids electromagnetic interference and improves the reliability of the system. (2) All power semiconductor devices withstand inferior to half the output voltage. (3) The input current avoids pulsation and small number of switching devices that greatly prolong the service life of the fuel cell. The circuit working principle, steady‐state performance, the design of device parameters, and comparison with similar boost topologies are researched. Finally, a 100‐W experimental platform is set up to validate the above features and guarantee the infallibility of theoretical research. [ABSTRACT FROM AUTHOR]

Environmental pollution is becoming ubiquitous; it has a negative impact on ecosystem diversity and worsens the quality of human life. This review discusses the possibility of applying the plant microbial fuel cells (PMFCs) technology for concurrent processes of electricity generation and the purification of water and soil ecosystems from organic pollutants, particularly from synthetic surfactants and heavy metals. The review describes PMFCs' functioning mechanisms and highlights the issues of PMFCs' environmental application. Generally, this work summarizes different approaches to PMFC development and to the potential usage of such hybrid bioelectrochemical systems for environmental protection. [ABSTRACT FROM AUTHOR]

This study investigates the potential for renewable energy-based electricity generation using existing wave, wind, and solar energies in Türkiye. A significant part of Türkiye's energy needs is still met using fossil fuels. Considering the country's resources, renewable energy sources appear as an alternative source to meet these needs. The objective of this study is to find an effective, efficient, economical, environmentally friendly, and sustainable way to produce electricity to reach net-zero targets and transition towards low-carbon and carbon-free energy systems. To be able to make a deep investigation about the relevant issue, six provinces from different regions of Türkiye (Antalya, Çanakkale, İstanbul, İzmir, Kırklareli, and Muğla) are assessed in terms of wave, wind, and solar energy potential, including wave data, wind speeds, sunshine duration, and global radiation values. Wind, wave, and solar energy data of the selected regions were taken from the ERA5 database, which is the weather forecast model of the European Center for Medium-Term Weather Forecasts (ECMWF), and the Ministry of Energy and Natural Resources of the Republic of Türkiye and the General Directorate of Meteorology. Calculations were made using monthly data for the last 5 years. Considering the coastal lengths in the determined regions, the annual total electrical power produced from wave, solar, and wind energies was calculated. In these calculations, the coastal length parameter was assumed to be uniform across all cities, and the electrical power potential from these energy sources was analyzed. Within the framework of these analyses, the number of houses in the selected regions whose electricity needs can be met was calculated. As a result, the potential electrical power and the amount of affordable housing units in the selected regions were compared. As an important result of the studies, it was determined that the characteristic features of the selected regions, such as wavelength, wave height, and wind speed, were directly related to the applicable coast length. The power obtained from wave energy was higher than that from other renewable energy sources, considering the determined coast lengths. Wave energy was followed by parabolic solar collector, wind, and photovoltaic solar energy systems. According to the model, the power obtained from renewable energy systems was at the highest level in the Kırklareli/Demirköy province compared to other locations. Kırklareli was followed by İstanbul, Antalya, İzmir, Muğla, and Çanakkale. It was also found that the electricity needs of 763,578 houses were met in the Kırklareli/Demirköy region, and the electricity needs of 470,590 houses were met in the Çanakkale/Ayvacık region. The statistically optimized factors using the Response Surface Methodology (RSM) for wind, photovoltaic, parabolic solar collector, and wave power were reported as 995.278, 4529.743, 2264.546, and 276,495.09, respectively. The optimal factors aim to achieve a total electricity generation rate of 2.491 × 109 (kWh/year), a total number of houses of 682,590.55 (number/year), and a total cost of USD 813,940,876. In line with the results obtained, the Kırklareli/Demirköy region becomes favorable when considering wave and wave-integrated wind and solar energies. The proposed system has the potential to meet the entire electricity demand of the Kırklareli province based on data from the Republic of Türkiye Energy Market Regulatory Authority (EMRA). [ABSTRACT FROM AUTHOR]

To achieve sustainable development, the energy transition from lignite burning to renewable energy resources for electric power generation is essential for Greece. Wind and solar energy have emerged as significant sources in this transition. Surprisingly, numerous studies have examined the potential for onshore wind based on land eligibility, while few studies on open-field photovoltaic (PV) installations have been conducted. Therefore, based on the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD-RES), along with insights from previous relevant studies, this work conducts a land eligibility analysis of onshore wind and open-field PV installations in Greece using the software Geospatial Land Availability for Energy Systems (GLAES 1.2.1) and ArcGIS 10.2. Additionally, through an in-depth exploration of wind and solar PV energy potential in decommissioned lignite mines integrated with wind power density (WPD) and global horizontal irradiation (GHI) maps, this study compares the suitability of wind versus solar as energy sources for the decarbonization of Greece. Overall, despite the greater spatial eligibility for onshore wind turbines compared to open-field PV power plants, the relatively lower wind energy potential and operational limitations of wind turbines lead to the study's conclusion that solar energy (PV) is more suitable for the decarbonization of Greece. [ABSTRACT FROM AUTHOR]

Combined concentrated solar power with photovoltaics can provide electricity and heat at the same system while maximizing the power output with reduced losses. Spectral splitting is required in such systems to separate the infrared part of the solar spectrum towards the thermal system, while the visible and near-infrared radiation can be converted by the photovoltaic solar cell. The performance of concentrated solar power plants comprising reflective beam splitters for combined generation of electricity and heat is presented in this work. A 50 MW power plant is considered in this work as a case of study in Southern Crete, Greece. The solar power plant consists of parabolic trough collectors and utilizes beam splitters with varying reflectivity. The dynamic performance of the power plant is modeled, and the annual energy yield can be calculated. Up to 350 MWt of thermal power can be delivered to the photovoltaic system utilizing a 50% reflecting splitter. The penalty to the high-reflectivity system is limited to 16.9% and the annual energy yield is calculated as 53.32 GWh. During summer months, a higher energy yield by up to 84.8 MWh/month is produced at 80% reflectivity compared to 90% as a result of the number of parabolic troughs. The reported energy yields with reflectivity by dynamic modeling can highlight discrete points for improvement of the performance in concentrated solar power photovoltaics. [ABSTRACT FROM AUTHOR]

The generation of electricity from renewable sources has made considerable strides in today's industrialized nations. The primary objective of this research is to examine the grid integration of solar systems within permissible total harmonic distortion (THD) limits, with the help of a proposed cascaded feedforward neural network (CFNN) controller fed advanced power conversion devices such as symmetrical hybrid (SH), switched inductor (SL), and switched capacitor (SC) based boost converter and reduced switch cascade multilevel inverter (CMLI). The SH–SL–SC converter for power conversion features a high voltage gain and reduced switch CMLI features minimum number of power electronic switches and low harmonic content in both the voltage and current profiles. When producing a control signal for the recommended SH–SL–SC boost converter, the proposed CFNN-MPPT controller performs better under different weather scenarios where it needs significant data to operate effectively. Consequently, the CFNN-MPPT controller fed SH–SC–SC boost converter-based PV system is integrated into the grid through reduced switch CMLI where it is controlled by proposed CFNN controller. The CMLI can be operated on asynchronous input voltage sources and making it ideal for PV systems when integrated to the grid with improved power quality by reducing total harmonic distortion (THD) to less than 5% at point of common coupling. With reference to the standards outlined in IEEE 1547 and 519, the simulation's outcomes were reviewed. Finally, a symmetric hybrid SL–SC boost converter and modified cascaded nine level inverter have been conceived and produced as a laboratory prototype model and its output waveforms are presented in this article. The proposed intelligent controller fed converter exhibits enhancements in the converter voltage gain and power quality by lowering the THD level at PCC in the grid integration with the PV power systems. [ABSTRACT FROM AUTHOR]

The electrical power distribution parts include the distribution transformer responsible for reducing high voltage (HV) 11 Kv to low voltage (LV) 416/240 volts, and the power distribution lines (PDLs), which are the source of electrical power supply for residential neighborhoods. Any malfunction of these parts, result in a power outage. Currently, any faults are reported, detected, and alert in a primitive way, and this mechanism certainly takes a lot of time and effort. In this paper, an automatic approach for detection, report and alert has been suggested. This approach process is carried out by continuously checking the status of the three output feeders (power distribution lines), which is the low voltage side of the distribution transformer, when a malfunction occurs in any one or more of these feeders, it will generate an alert signal in the form of a text message using short message service (SMS). The maintenance center is notified by receiving this SMS via SIM900 modem through the global system for mobile (GSM) communications network. The SMS text message includes feeder number, fault type and location address (street number and distribution transformer number). In this design, time and effort are shortened and the maintenance center enables fully automatic monitoring of electric power distribution parts within the sector of responsibility. [ABSTRACT FROM AUTHOR]

As an instrument supporting the realization of EU climate neutrality targets by 2050 and encouraging decarbonization outside its borders, the current proposed Carbon Border Adjustment Mechanism (CBAM) is facing opposition from some countries. Focusing on Brazil, this paper evaluates the impacts of the CBAM on the Brazilian economy through a comprehensive analysis of various scenarios based on the potential EU implementation of the CBAM and Brazil's climate scenarios. Results obtained in this research alleviate concerns of detrimental and competitiveness losses from Brazilian industries. Rather, the implementation of the EU CBAM improves the trade balance of Brazil's Energy-Intensive Industries (EII). The relatively low CO2 contents of Brazilian EII are elemental to this result, while contributions of carbon-free technologies in electricity generation are also critical factors in maximizing this trade surplus. Other consequential factors affecting these results are the contributions of CO2 removal from Brazil's forestry of land used, and homogeneity of CBAM-imposed products. [ABSTRACT FROM AUTHOR]

In this research, a proton-conducting membrane fuel cell is used in conjunction with a gasifier for producing electricity with air considered as the gasification agent. The simultaneous electricity, heat, and hot water generation of these two when coupled with an Organic Rankine Cycle (ORC) installed within a Thermoelectric Generator (TEG) was studied. Additionally, to utilize the electricity generated from various parts of the process; i.e. the fuel cell, organic Rankine cycle turbine, and the thermoelectric generator, the reverse osmosis (OR) water desalination unit was coupled to the process. The aforementioned system was studied and analyzed based on thermodynamic and thermos-economic aspects. A parametric study was also carried out to analyze the effect of main factors on power output, energy/exergy yield, and the total cost of each system. Finally, a dual objective optimization was achieved by taking the main factors into consideration and using a genetic algorithm. Results indicated that the work generated by the system was not significant; however, the amount of hot water produced was relatively promising. Based on exergy and exergoeconomic evaluation, the After-Burner is an important non-renewable energy source while the fuel cell has a higher inefficiency cost which could be due to low work generation. Results indicate that the moisture in the biomass and the figure of merit, are important parameters that have to be kept as low as possible. The energy yield of the fuel cell was calculated to be 45.05%. Additionally, considering the optimized operating point, pure power output and system cost rate will be 1.81 KW and 5.15 $/hr respectively. • Proposing a novel cycle based on fuel cell for electricity generation. • Adding nanomaterial on the fuel cell for increasing the performance. • Double-objective optimization of the related energy system. [ABSTRACT FROM AUTHOR]

The use of fuel cell (FC) is considered one of the methods of producing electricity with relatively high efficiency. So, it has attracted many researchers' attention to develop and improve its performance. The present study deals with the technical-economic optimal design of a hybrid power generation system based on a Proton Exchange Membrane (PEM) FC combined with an Organic Rankine Cycle (ORC). ORC is used to recover the generated heat in PEM FC. The decision variables of this study include FC operating pressure and temperature, current density, FC area, the quantity of FC, and operating parameters of the ORC system, including HRVG PPTD, condenser PPTD, and refrigerant mass fraction in zeotropic mixture. In this study, three zeotropic mixtures, including R11-R245fa, R11-R123, and R123-R245fa, are studied as the working fluid of the ORC system. The objective functions considered to be optimized are the system's exergy efficiency and total cost rate (TCR). Finally, it is observed that the highest exergy efficiency is obtained using the zeotropic mixture R11-R245fa with a value of 54.15%, and the lowest TCR is obtained with the mixture R11-R123 with a value of 0.65 $/s. Zeotropic mixture R11-R123 has the exergoenvironmental index, environmental damage effectiveness index, and exergy stability factor of 0.5506, 1.277, and 0.4750, respectively. • A hybrid electricity generation system including PEM FC and ORC is analyzed. • Exergy Efficiency and economic analysis are considered for system evaluation. • Three novel zeotropic mixtures are examined as the working fluid of the ORC system. • A Parametric Study and optimization are conducted in this research paper. [ABSTRACT FROM AUTHOR]

Among the technologies for the recovery of energy from waste, in particular residual municipal solid waste (rMSW), combustion is the most widely used thermo-chemical treatment process associated with thermal and electric power production by a steam cycle, named, shortly, Waste to Energy (WtE). Today, more than 500 WtE plants in the EU, about 400 in China and 76 in the USA are in operation, based on efficient technologies and advanced air pollution control systems. Energy recovery can be accomplished also by means of gasification; however, the presence of impurities together with the atmospheric pressure, at which syngas is normally produced, impose the feeding of syngas to a conventional steam cycle, leading to generally lower performances than WtE. The energy recovered by WtE offsets traditional energy sources such as fossil fuels and related emissions, providing savings in term of climate change. However, the savings obtainable by replacing electricity and/or heat will diminish as the energy systems will hopefully become increasingly renewable. Over this medium–long-term horizon, one possibility is to capture the CO2 from WtE flue gases and to store/use it. From the life cycle assessment perspective, it has been calculated that the introduction of CO2 capture and storage in WtE, despite energy penalties, is able to reduce the impact on climate change. The alternative approach, proposed to contain the emissions of greenhouse gases in the thermal treatment of waste, is using the carbon contained in it to produce commonly used chemical compounds (waste to chemicals). The benefits, in terms of reductions of greenhouse gases, are expected from the possibility of obtaining chemicals that can replace their analogue normally produced from fossil sources. To date, only one WtC demonstration plant is operating by being fed by rMSW-derived waste, and some similar initiatives are planned, but still adequate assurances in terms of robust knowledge of the involved complex processes, above all, if applied to highly inhomogeneous feed streams such as those obtained from rMSW, are not available. Once the several initiatives come to completion, it will enable waste management professionals to assess performance and to begin to consider such a facility in their planning. [ABSTRACT FROM AUTHOR]

Offshore wind energy is becoming an increasingly important element in the decarbonisation of energy systems. This study aims to estimate the technical potential for offshore wind energy in the Polish exclusive economic zone (EZZ) of the Baltic Sea. Such estimates are necessary to plan the expansion of energy infrastructure. This paper proposes a multi-criteria analysis for the estimation of offshore wind potential and site selection using QGIS software (QGIS Desktop 3.28.6). The criteria include wind velocity, water depth, distance from nature conservation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, and distance from the coastline. The best sites are identified through the Analytic Hierarchy Process (AHP). The results show that the total suitable marine area of 16,218.67 square kilometres can be divided into three parts, in which the highly suitable area for offshore wind farms accounts for 17.55%, the moderately suitable area accounts for 48.56%, and the marginally suitable area accounts for 10.26%. Two different wind turbine placement configurations are being considered, in which wind turbines with a nominal capacity of 15 MW are laid out: 2.5 km and 2 km apart from each other. The corresponding overall electrical capacities only in highly suitable areas equal ca. 9 GW and 15 GW, respectively. If this area is extended to also include the moderately suitable parts, these values increase to ca. 31 GW and 50 GW. The results indicate that the technical potential of offshore wind in the Polish EEZ is significant and should be taken into account when designing the future electricity generation mix. [ABSTRACT FROM AUTHOR]

Water electrolysis for hydrogen production with renewable electricity is regularly studied as an option for decarbonised future energy scenarios. The inclusion of byproduct electrolytic oxygen capture and sale is of interest for parallel decarbonisation efforts elsewhere in the industry and could contribute to reducing green hydrogen costs. A deterministic hydrogen electrolysis system model is constructed to compare oxygen inclusion/exclusion scenarios. This uses wind and solar-PV electricity generation timeseries, a power-dependent electrolysis model to determine the energy efficiency of gas yield, and power allocation for gas post-processing energy within each hourly timestep. This maintains a fully renewable (and therefore low/zero carbon) electricity source for electrolysis and gas post-processing. The model is validated (excluding oxygen) against an existing low-cost GW-scale solar-hydrogen production scenario and an existing hydrogen production costs study with offshore wind generation at the multi-MW scale. For both comparisons, oxygen inclusion is then evaluated to demonstrate both the benefits and drawbacks of capture and utilisation, for different scenario conditions, and high parameter sensitivity can be seen regarding the price of renewable electricity. This work subsequently proposes that the option for the potential utilisation of byproduct oxygen should be included in future research to exemplify otherwise missed benefits. [ABSTRACT FROM AUTHOR]

The aggregation of the remaining battery capacity of electric vehicles (EVs) can be used as distributed energy storage to participate in the microgrid optimisation through vehicle‐to‐grid (V2G) technology. However, the reliability of this service to be delivered is affected by the uncertainty of EVs' behaviour. The un‐reliable service will bring risks in the operation of microgrid when EVs' V2G electricity is regarded as an important flexibility resource. This paper, first proposes an analytical method to quantify the reliability of EV aggregation's (EVA's) V2G electricity based on the analysis of historical charging data and compound Poisson process. Based on this model, electric vehicles aggregators can evaluate the V2G bid quantity for any time slot on day t+1 according to the reliability requirements. Secondly, an optimal operation model of microgrid with consideration of V2G's reliability is considered. Finally, a test system with photovoltaics (PVs) and EVA on top of the original IEEE 33‐node system is used to verify the effectiveness of the proposed model. The results show that with the proposed reliability evaluation method of V2G electricity, the optimal operation of the microgrid can be reached with appropriate evaluation of the capability of the EVA. [ABSTRACT FROM AUTHOR]

The development of environmentally sustainable and highly efficient technologies for ammonia production is crucial for the future advancement of carbon‐neutral energy systems. The nitrite reduction reaction (NO2RR) for generating NH3 is a promising alternative to the low‐efficiency nitrogen reduction reaction (NRR), owing to the low N=O bond energy and high solubility of nitrite. In this study, we designed a highly efficient dual‐atom catalyst with Fe‐Cu atomic pair sites (termed FeCu DAC), and the as‐developed FeCu DAC was able to afford a remarkable NH3 yield of 24,526 μg h−1 mgcat.−1 at −0.6 V, with a Faradaic Efficiency (FE) for NH3 production of 99.88 %. The FeCu DAC also exhibited exceptional catalytic activity and selectivity in a Zn‐NO2 battery, achieving a record‐breaking power density of 23.6 mW cm−2 and maximum NH3 FE of 92.23 % at 20 mA cm−2. Theoretical simulation demonstrated that the incorporation of the Cu atom changed the energy of the Fe 3d orbital and lowered the energy barrier, thereby accelerating the NO2RR. This study not only demonstrates the potential of galvanic nitrite‐based cells for expanding the field of Zn‐based batteries, but also provides fundamental interpretation for the synergistic effect in highly dispersed dual‐atom catalysts. [ABSTRACT FROM AUTHOR]

The development of environmentally sustainable and highly efficient technologies for ammonia production is crucial for the future advancement of carbon‐neutral energy systems. The nitrite reduction reaction (NO2RR) for generating NH3 is a promising alternative to the low‐efficiency nitrogen reduction reaction (NRR), owing to the low N=O bond energy and high solubility of nitrite. In this study, we designed a highly efficient dual‐atom catalyst with Fe‐Cu atomic pair sites (termed FeCu DAC), and the as‐developed FeCu DAC was able to afford a remarkable NH3 yield of 24,526 μg h−1 mgcat.−1 at −0.6 V, with a Faradaic Efficiency (FE) for NH3 production of 99.88 %. The FeCu DAC also exhibited exceptional catalytic activity and selectivity in a Zn‐NO2 battery, achieving a record‐breaking power density of 23.6 mW cm−2 and maximum NH3 FE of 92.23 % at 20 mA cm−2. Theoretical simulation demonstrated that the incorporation of the Cu atom changed the energy of the Fe 3d orbital and lowered the energy barrier, thereby accelerating the NO2RR. This study not only demonstrates the potential of galvanic nitrite‐based cells for expanding the field of Zn‐based batteries, but also provides fundamental interpretation for the synergistic effect in highly dispersed dual‐atom catalysts. [ABSTRACT FROM AUTHOR]

This study highlights the promising potential of utilizing an MFC reactor equipped with Nafion 117 and PVA/GA membranes. This innovative and cost-effective approach has proven to be highly effective in addressing the challenge of treating methyl tertiary butyl ether (MTBE) in wastewater, while also offering the added benefit of electricity generation. Biodegradation efficiency and electricity generation were assessed in this study using co-substrates such as glucose (GLS) and sodium acetate (SAC), in addition to surfactants like sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB). The MTBE concentration ranged from 10 to 200 mg/L, and it was quantified in the samples through gas chromatography with a flame ionization detector (GC–FID). To explore the structural characteristics of Nafion 117 and PVA/GA membranes, we conducted field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD) analyses. The maximum biodegradation efficiency of MTBE (%) in MFC reactors with Nafion 117 and PVA/GA membranes was found to be 93.3% and 89.8%, accompanied by maximum production voltages (mV) of 895 mV and 853 mV, respectively. This was achieved with the addition of SAC co-substrate and SDS surfactant. Notably, the addition of co-substrate had a more significant impact compared to the surfactant. Simultaneously adding both the co-substrate and surfactant had a substantial effect on both the biodegradation efficiency (%) of MTBE and the generated voltage. Based on these findings, it can be concluded that the MFC reactor equipped with Nafion 117 and PVA/GA membranes is capable of effectively breaking down MTBE. However, due to its lower cost, the PVA/GA membrane may be considered a more economical choice. Therefore, the utilization of an MFC reactor featuring Nafion 117 and PVA/GA membranes holds great promise as an efficient approach for treating MTBE in wastewater, while concurrently generating electricity. [Display omitted] • The process of MTBE biodegradation was examined in a 2-part MFC reactor. • Proton exchange membranes showed similar efficiency, but different voltage generation. • The addition of co-substrate and surfactant improved efficiency and voltage generation. • Simultaneously adding SAC co-substrate and SDS surfactant led to the best results. • The MFC reactor performed well in both MTBE biodegradation and voltage generation. [ABSTRACT FROM AUTHOR]

The development and utilization of electrochemical energy conversion and storage devices can maximize intermittent renewable energy and balance environmental issues. Aqueous metal‐redox bicatalyst batteries, incorporating value‐added electro‐reduction reactions during discharge process at the cathode, are considered a particularly attractive alternative for simultaneous electricity generation and valuable chemical production. In this review, for the first time, a holistic and subtle description of value‐added metal‐redox bicatalyst batteries is made, focusing on recent efforts to optimize the energy conversion/chemical production‐involved cathodic discharging reactions, including CO2 reduction reaction (CO2RR), nitrogen reduction reaction (NRR), nitric oxide reduction reaction (NORR), nitrate reduction reaction (NO3−RR), nitrite reduction reaction (NO2−RR), hydrogen evolution reaction (HER), and acetylene reduction reaction (ARR). A macroscopic understanding of design principles in terms of anodes, cathodes, and reaction parameters is provided. Some mechanism explorations, feasibility analyses, technoeconomic assessments, device combinations, and associated comparisons are involved to deepen the understanding of different systems and evaluate their application prospects. Perspectives on and opportunities for future research directions are also outlined. [ABSTRACT FROM AUTHOR]

It is noted that the traditional direct filed-oriented control (DFOC) is widely used in the field of electric power generation from wind due to its fast response dynamic, ease of implementation and simplicity, but this strategy is characterized by the presence of large ripples at the level of both active and reactive powers. This work presents a new algorithm for DFOC strategy of an asynchronous generator (AG) in a wind power (WP) system, which is based on the use of a new nonlinear controller called fractional-order synergetic control–fractional-order proportional-integral (FOSC–FOPI) controller, where the proposed technique parameters are calculated using the particle swarm optimization (PSO) strategy. It has been observed that the DFOC–FOSC–FOPI–PSO strategy is robust and works well in case of changing generator parameters. Three tests were performed to study the behavior of the designed technique under different working conditions, where the behavior of the DFOC–FOSC–FOPI–PSO strategy was compared with the behavior of the traditional DFOC technique in terms of power ripple ratio, overshoot, steady-state error, response time, tracking reference, and current quality. The simulation of the designed technique based on the FOSC–FOPI–PSO strategy of the AG–WP system is carried out using Matlab software, where the simulation results showed that the suggested technique is better than the classical technique (with PI controller) in terms of improving response time of active power (33.33%) and reactive power (10%) in second test, reduction of the steady-state error of reactive power (96.95%) and active power (97.14) in first test, minimization of harmonic distortion of current (96.57%) in third test and significant minimization of ripples of active power (99.67%, 44.69%, and 98.95%) and reactive power (99.25%, 53.65%, and 70.50%) in the three tests. The effectiveness of the DFOC–FOSC–FOPI–PSO strategy is very high, so it can be a reliable solution for controlling various generators. [ABSTRACT FROM AUTHOR]

The use of decentralized renewable energy systems will continue to play a significant role in electricity generation especially in developing countries where grid expansion to most remote areas is uneconomical. The income levels of these off-grid communities are often low, such that there is a need for the delivery of cost-effective energy solutions through optimum control and sizing of energy system components. This paper aims at minimizing the net present cost (NPC) and the levelised cost of energy (LCOE). The study presents a hybrid power system involving a hydroelectric, solar photovoltaic (PV), and battery system for a rural community in Cameroon. The optimization of the system was done using HOMER Pro and validated using a meta-heuristic algorithm known as genetic algorithm (GA). The GA approach was programmed using the MATLAB software. After the HOMER simulation, the optimal power capacity of 3 kW solar PV, 334.89 Ah battery, and 32.2 kW microhydropower was used to meet the load. The village load profile had a daily energy usage of 431.32 kWh/day and a peak power demand of 38.49 kW. The optimized results showed an NPC and LCOE of $90,469.16 and 0.0453 $/kWh, respectively. The system configuration was tested against an increase in hydropower capacity, and it was observed that increasing the hydropower capacity has the ability to significantly reduce the LCOE as well as the battery and solar PV size. A comparative analysis of the two approaches showed that the optimization using GA was more cost-effective than HOMER Pro with the least LCOE of 0.0344 $/kWh and NPC of $86,990.94 as well as a loss of power supply probability (LPSP) of 0.99%. In addition, the GA method gave more hydropower generation than HOMER Pro. This supports the fact that stochastic methods are more realistic and economically viable. They also accurately predict system operation than deterministic methods. [ABSTRACT FROM AUTHOR]

The natural phenomenon of droplets bouncing on various surfaces holds remarkable potential for applications like water transportation, self‐cleaning, antifreezing, etc. However, achieving precisely controlled patterned droplet bouncing on functional surfaces with accurately controlled factors like bouncing velocity and trajectory in three dimensions remains a formidable challenge. In this context, a concept of bionic hydrophobic functional surfaces composed of mushroom‐like microstructures is introduced. These microstructures are crafted using the projection microstereolithography (PµSL) based 3D printing technique, subsequently coated with a hydrophobic spray. By finely adjusting the geometric attributes and inclination angles of these micromushrooms, the ability is gained to meticulously manipulate the bouncing velocity and trajectory of water droplets. The most optimal performance is demonstrated by a droplet exhibiting a maximal jumping distance and height respectively of 2.5 and 7.1 mm with 50° inclined micromushrooms. Notably, these specially designed micromushrooms orchestrate diverse behaviors in droplet bouncing, encompassing patterned bouncing, antigravity jumps, and directional water transportation. Additionally, the functional surface's adaptable self‐cleaning capability facilitates the harnessing of energy from rainfall on large surfaces, offering potential applications in realms, such as self‐cleaning mechanisms, droplet capture, water conveyance, and clean energy generation. [ABSTRACT FROM AUTHOR]

As the residential sector has a significant environmental impact, there is growing pressure to move away from heavy reliance on fossil fuels and towards renewable technologies. One approach to avoid burning fossil fuels in this sector is a hybrid solar-hydrogen energy system based on photovoltaic panels for electricity supply, a battery for short-term storage, and a hydrogen system consisting of a fuel cell, electrolyzer, and hydrogen tank for long-term storage. This study uses the life cycle assessment method to analyze the environmental impact of such a system and compares the results to a reference system with no hydrogen components in both off-grid and grid-connected scenarios. The results show that the solar-hydrogen system causes lower greenhouse gas emissions in the off-grid scenario. Overall, it is significantly beneficial in 6 out of 16 impact categories. In contrast, the off-grid reference system, equipped with a diesel generator as a backup power source, has lower burdens in 7 impact categories and the single score result. The production of the hydrogen tank is identified as the primary driver of emissions of the solar-hydrogen system. In the on-grid scenario, the grid-connected reference system is beneficial in all impact categories. • LCA of a solar-hydrogen energy system for buildings. • Combination of a Matlab Simulink simulation and LCA. • Dynamic electricity generation data used in the LCA model. • Solar-hydrogen system causes lower greenhouse gas emissions in off-grid scenario. • Hydrogen tank is identified as main driver of emissions. [ABSTRACT FROM AUTHOR]

We present a high-resolution global emission catalogue of CO 2 and co-emitted species (NO x , SO 2 , CO, CH 4) from thermal power plants for the year 2018. The construction of the database follows a bottom-up approach, which combines plant-specific information with national energy consumption statistics and fuel-dependent emission factors for CO 2 and emission ratios for co-emitted species (e.g. the amount of NO x emitted relative to CO 2 : NOx/CO2). The resulting catalogue contains annual emission information for more than 16 000 individual facilities at their exact geographical locations. Each facility is linked to a country- and fuel-dependent temporal profile (i.e. monthly, day of the week and hourly) and a plant-level vertical profile, which were derived from national electricity generation statistics and plume rise calculations that combine stack parameters with meteorological information. The combination of the aforementioned information allows us to derive high-resolution spatial and temporal emissions for modelling purposes. Estimated annual emissions were compared against independent plant- and country-level inventories, including Carbon Monitoring for Action (CARMA), the Global Infrastructure emission Database (GID) and the Emissions Database for Global Atmospheric Research (EDGAR), as well as officially reported emission data. Overall good agreement is observed between datasets when comparing the CO 2 emissions. The main discrepancies are related to the non-inclusion of auto-producer or heat-only facilities in certain countries due to a lack of data. Larger inconsistencies are obtained when comparing emissions from co-emitted species due to uncertainties in the fuel-, country- and region-dependent emission ratios and gap-filling procedures. The temporal distribution of emissions obtained in this work was compared against traditional sector-dependent profiles that are widely used in modelling efforts. This highlighted important differences and the need to consider country dependencies when temporally distributing emissions. The resulting catalogue (10.24380/0a9o-v7xe, Guevara et al., 2023) is developed in the framework of the Prototype System for a Copernicus CO 2 service (CoCO2) European Union (EU)-funded project to support the development of the Copernicus CO 2 Monitoring and Verification Support capacity (CO2MVS). [ABSTRACT FROM AUTHOR]

The development of sustainable electricity technology is of utmost importance in addressing the increasing energy demand while mitigating greenhouse gas emissions. Fossil fuel-based electricity generation is the primary contributor to air pollution and climate change, necessitating a shift towards renewable energy sources. The efficient production, distribution, and utilization of energy resources, along with ensuring affordable energy access and environmental sustainability, are key policy objectives for any country’s energy sector. However, assessing sustainable electricity technologies is a complex task due to the diverse range of evaluation criteria and impacts associated with the practical implementation of these solutions. To overcome this challenge, this study proposes a multi-criteria decision-making (MCDM) approach to select the optimal solution for the development of sustainable electricity technology. The study employs several reliable methods, including MAIRCA, SPOTIS, COMET, and the CRITIC weighting method, to perform ranking evaluations. Based on this, an evaluation Table of criteria using linguistic variables is constructed. Furthermore, a ranking of methods for developing sustainable electricity technology is established by combining MCDM optimization methods. The results indicate that future energy policies should prioritize sustainable energy technologies, particularly water and solar thermal solutions. These findings have significant implications for development policymakers as the transition towards a sustainable energy system becomes increasingly crucial. In the future, the findings of this research can be further developed on a regional level, enabling the identification of the most appropriate energy technologies for specific regions based on their unique characteristics and requirements. [ABSTRACT FROM AUTHOR]

Electrical energy is now widely recognized as an essential part of life for humans, as it powers many daily amenities and devices that people cannot function without. Examples of these include traffic signals, medical equipment in hospitals, electrical appliances used in homes and offices, and public transportation. The process that generates electricity can pollute the air. Even though natural gas used in power plants is derived from fossil fuels, it can nevertheless produce air pollutants involving particulate matter (PM), nitrogen oxides (NOx), and carbon monoxide (CO), which affect human health and cause environmental problems. Numerous researchers have devoted significant efforts to developing methods that not only facilitate the monitoring of current air quality but also possess the capability to predict the impacts of this increasing rise. The primary cause of air pollution issues associated with electricity generation is the combustion of fossil fuels. The objective of this study was to create three multiple linear regression models using artificial intelligence (AI) technology and data collected from sensors positioned around the energy generator. The objective was to precisely predict the amount of air pollution that electricity generation would produce. The highly accurate forecasted data proved valuable in determining operational parameters that resulted in minimal air pollution emissions. The predicted values were accurate with the mean squared error (MSE) of 0.008, the mean absolute error (MAE) of 0.071, and the mean absolute percentage error (MAPE) of 0.006 for the turbine energy yield (TEY). For the CO, the MSE was 2.029, the MAE was 0.791, and the MAPE was 0.934. For the NOx, the MSE was 69.479, the MAE was 6.148, and the MAPE was 0.096. The results demonstrate that the models developed have a high level of accuracy in identifying operational conditions that result in minimal air pollution emissions, with the exception of NOx. The accuracy of the NOx model is relatively lower, but it may still be used to estimate the pattern of NOx emissions. [ABSTRACT FROM AUTHOR]

Due to increasing geopolitical tensions and disruption of gas supplies, European countries have been looking for alternatives for electricity generation (EG). As part of this process, one of the most important goals for long-term sustainability is to ensure carbon neutrality. Therefore, this study analyzes time, frequency, and quantile-based impacts of EG from different electricity generation sources (i.e., renewable, nuclear, and fossil fuels) on carbon neutrality, focusing on four leading European countries. The study applies the wavelet transform coherence (WC), quantile-on-quantile regression (QQ), and Granger causality in quantiles (GQ) to high-frequency daily data between January 2, 2019 and March 10, 2023. Results show that (i) there is a strong time and frequency dependence between EG and CO2 emissions across countries, while results vary by EG sources and countries; (ii) renewable EG dampens CO2 emissions. At higher quantiles, a higher share of renewable EG lowers CO2 emissions in Germany and Spain, while they increase in France; (iii) nuclear EG is beneficial only for the United Kingdom. (iv) Fossil EG increases CO2 emissions in all countries. Excessive fossil EG leads to more CO2 emissions at higher quantiles; (v) the impacts of EG on CO2 emissions have a time-, frequency-, quantile-, country-, and EG source-dependent structure. The outcomes of the study demonstrates that the ideal EG source for countries is mainly renewable EG, while in the case of the United Kingdom, nuclear EG could be an alternative for improving the environment while reducing fossil fuels. [ABSTRACT FROM AUTHOR]

The article provides a theoretical background for how renewable energy credits (REC) act as property rights to assist policymakers to craft more effective policy. It discusses the use of REC in state renewable portfolio standards, which are programs that require electricity sellers to derive a set percentage of generation portfolio from renewable energy resources, and the purchase of REC by private, nonutility actors. It explains the application of the principle of accession related to REC.