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This paper considers two pertinent research inquiries: 'Can an AI-based predictive framework be utilised for the optimisation of solar energy management?' and 'What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?' The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R 2 . A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency. [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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Microbial fuel cell technology draws attention with its ability to directly recover electrical energy from various organic materials. In this study, the operating conditions affecting the oxidation–reduction and electricity generation efficiency of MFC were optimized using the Taguchi Experimental Design model. Optimization was carried out for maximum power density, coulombic efficiency, azo dye removal, and COD removal. With the determined optimum conditions (cathode pH of 3.0, cathode oxygen status of anaerobic, anode substrate of pre-treated, external resistance of 100 Ω, cathode electrode type of plain carbon, cathode electrode surface of 22 cm2, cathode conductivity of 20 µs/cm), 177.03 mW/m2 power density, 7.50% coulombic efficiency, 91.26% azo dye removal efficiency and 21.61% COD removal efficiency were obtained. By Pareto analysis, it was determined that the power density, coulombic efficiency and COD removal efficiency were most affected by the substrate type at the anode, and the azo dye removal was most affected by the catholyte pH. The maximum power density and internal resistance of the MFC operated under optimum conditions were determined as 145.11 mW/m2 and 243.30 Ω, respectively by the polarization curve. Cyclic voltammetry was also performed for the electrochemical characterization of MFC operated under optimum conditions. An anodic peak at −183.2 mV and a cathodic peak at −181.2 mV was visible in the CV curve. [ABSTRACT FROM AUTHOR]

The climate crisis is driving a rapid increase in size and number of offshore wind farms to reduce carbon emissions from electricity generation. However, there are concerns about the potential impact of offshore wind farms on the marine environment. Seabirds are considered to be at risk of being displaced from preferred foraging habitat, by construction and operation of offshore wind farms, resulting in reduced energy intake or elevated energetic costs and consequent decreases in survival and/or productivity. Typically, displacement or avoidance behaviour is assessed by comparing abundance and spatial distributions of seabirds before and after an offshore wind farm is constructed. However, seabird distributions are highly variable through time and space and so discerning a change in distribution caused by an offshore wind farm from other environmental variables can be challenging. We present a new method that controls for temporal variation by examining the location of individual seabirds relative to turbines. Mean seabird density at different distances from individual turbines (0-400m) was calculated from data collected on a total of 12 digital aerial surveys of the Beatrice Offshore Wind Farm (UK), in May-August in 2019 and 2021. Mean densities of common guillemot (Uria aalge), razorbill (Alca torda), Atlantic puffin (Fratercula arctica) and black-legged kittiwake (Rissa tridactyla), both flying and sat on the water, were calculated. If the presence of turbines had no effect on seabird distribution, there should be no relationship between distance from turbine and seabird density. This was tested by simulating a relocation of turbines, relative to seabird distribution, and recalculating seabird density over 0-400m from simulated turbine locations. This was repeated to generate a bootstrapped distribution of expected densities against which observed density was compared. If displacement was occurring, mean observed density close to turbines would be significantly lower than expected density, derived from the bootstrap distribution. Overall, observed mean density did not differ significantly from expected density, i.e. no displacement effect was detected. There was a slight tendency for guillemot and razorbill, when sat on the water, to be at higher densities than expected, near turbines, suggestive of possible attraction to turbines, and for flying birds to be at lower densities than expected, near turbines, suggestive of possible avoidance. No flying razorbills were recorded within 100m of turbines but sample sizes were small. Kittiwake tended to show no avoidance or attraction behaviour, although flying kittiwake density was slightly lower than expected at 200m from turbines. Puffins sat on the water were recorded in densities similar to the expected density. Overall, no effect of turbine rotor speed was found, i.e. birds were not more likely to be displaced/avoid turbines at higher or lower rotor speeds. The results of the turbine relocation analysis gave a more consistent and more easily interpreted assessment of displacement/avoidance behaviour than the typical approaches of comparing abundance and seabird distribution through time. We strongly encourage application of this new approach to post-construction spatial distribution data from other offshore wind farms, to build the evidence base on the effects of offshore wind farms on seabirds. [ABSTRACT FROM AUTHOR]

This paper examines the health tradeoffs associated with the historical expansion in coal-fired electricity generation in the United States. We combine newly digitized data on all major coal-fired power plants for the period 1938–1962 with two complementary difference-in-differences strategies. Coal-fired plants imposed large negative health externalities that were partially offset by the benefits of local electricity generation. The health impacts varied widely according to initial electricity access and evolved as the stock of generating capacity expanded over the sample period. These findings highlight the importance of accounting for both current and future payoffs when designing environmental regulation. [ABSTRACT FROM AUTHOR]

This study examines the determinants of modern cooking fuel choices in Korea. The ordered probit model is estimated using an extensive online survey of Korean household energy consumers. Our empirical results showed that age, gender, and education are significant determinants of modern cooking fuel choices among the socioeconomic demographic variables: females, older people, and highly educated people are more likely to prefer electricity. Also, electricity is more likely to be preferred over natural gas by people who have a higher preference for district heating. Two psychological factors significantly influence cooking fuel choices. People whose behaviors are environment-friendly and people who are more health-conscious are more likely to prefer electricity over natural gas or propane. There are three important insights into the cooking fuel transition toward electricity. First, as interest in health and climate change has significantly increased in recent years in Korea, it may stimulate the transition from natural gas or propane to electricity. Second, electrification of cooking methods will be beneficial for the environment if electricity generation becomes less carbon-intensive. Since electricity in Korea is mainly generated by fossil fuels, it is crucial to implement more aggressive policies toward renewable sources in the energy mix for electricity generation. Third, the public should better understand why this problem cannot be overlooked because the energy mix is important in mitigating climate change. The better the people understand the exact relationship between energy consumption and pollutant emissions, the more effective and environmentally sound will the energy mix policy become. [ABSTRACT FROM AUTHOR]