Search

In this paper, we use the literature to help us better understand carbon capture costs and how these estimates fare against those of avoided costs, focusing on bioenergy carbon capture and storage (BECCS), carbon capture and storage (CCS), as well as direct air capture technologies. We approach these questions from a meta-analysis perspective. The analysis uses meta-analysis tools while applying them to numerical rather than statistical studies. Our analysis shows that avoided costs are, on average, 17.4% higher than capture costs and that the carbon intensity of the feedstock matters: the estimates for coal-based electricity generation capture costs are statistically smaller than those for natural gas or air. From a policy perspective, the literature suggests that the costs of CCS are like the 45Q subsidy of USD 50 per metric ton of carbon captured. [ABSTRACT FROM AUTHOR]

Fuel pellets are becoming popular for home heating and electrical generation. Although wood residues such as sawdust, chips, and shavings have been a significant fiber feed stock pellet production, paper waste, agricultural residue, and roundwood also can be used. In 2005, 41% of the fiber feed stock used to produce pellets in the Lake States was wood residue, 27% was wastepaper or agricultural residue, and 32% was multiple or unspecified fiber feed stocks. Between 2005 and 2008, the number of wood pellet processing facilities in the Lake States region nearly doubled, and the volume of products manufactured increased by 300%. Facility expansion required an additional 592,000 dry tons of fiber feed stock. Forty-three percent of the additional feed stock was wood residue, 25% was roundwood, 18% was agricultural residue, and 13% was undefined or multiple feed stocks. In 2008, two facilities producing 67,000 tons of pellets were under construction, and four plants slated to produce an additional 600,000 tons of product were in the planning stage. The primary product manufactured in these new or planned facilities will be commercial fuel pellets, and the primary feed stock will be roundwood, harvest residues, and paper and agriculture waste. The potential increase in the demand for lower quality roundwood by these new facilities could replace the declining demand for this material by the paper industry. If the pellet industry continues to expand, it could compete for roundwood used by the fuel chips and pulpwood industries. [ABSTRACT FROM AUTHOR]

Thin wafers and thin wires are beneficial to the photovoltaic industry for reducing costs, increasing efficiency, and reducing the cost of electricity generation. It is a development trend in solar silicon wafer cutting. Thin wire cutting reduces the kerf between silicon wafers to less than 50 μm. Therefore, it is extremely difficult to supply cutting fluid to the cutting area. And this affects cutting performance. This paper proposes the use of the capillary effect produced by ultrasonic waves in fixed diamond wire slicing to improve the cutting fluid supply and reduce wafer adsorption. To explore the rules of ultrasonic capillary action between two plates and guide the industrial applications, the effects of the distance between parallel plates, the distance from the bottom of the parallel plates to the ultrasonic radiation surface, the non-parallelism between the plates, the temperature of the working fluid, the ultrasonic action time, and the type of working fluid on the liquid level rise height were studied. The conclusions can be used to guide the improvement of the supply of working fluid in fixed diamond wire slicing. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

The paper describes the possibility of hybrid electricity generation in already existing wind power plants (WPP) by additional installation of photovoltaic power plants (PVPP) on individual wind turbines (WT). Wind turbines have the lowest generation of electricity in the summer months, while PVPPs have the opposite, the highest generation in those months. Connecting the PVPP to the existing WT (hybrid electricity generation) brings a more even generation and a greater degree of use of the infrastructure of the existing wind farms (HV/MV transformer station, medium voltage cable distribution...). This way the variation of generation in relation to the average annual generation is reduced, which reduces the problems of balancing electricity. [ABSTRACT FROM AUTHOR]

In light of the growing integration of renewable energy sources in power systems, the adoption of DC microgrids has become increasingly popular, due to its simple structure, having no frequency, power factor concerns. However, the dependence of DC microgrids on cyber-networks also makes them susceptible to cyber-attacks. Potential cyberattacks can disrupt power system facilities and result in significant economic and loss of life. To address this concern, this paper presents an attack-resilient control strategy for microgrids to ensure voltage regulation and power sharing with stable operation under cyber-attack on the actuators. This paper first formulates the cyber-security problem considering a distributed generation based microgrid using the converter model, after which an attack-resilient control is proposed to eliminate the actuator attack impact on the system. Steady state analysis and root locus validation illustrate the feasibility of the proposed method. The effectiveness of the proposed control scheme is demonstrated through simulation results. [ABSTRACT FROM AUTHOR]

The daily production and life of human beings are inseparable from electricity. As the power supplier, electric power enterprises provide power demand for the majority of users. In the new era, electric power enterprises are also facing market-oriented reform. The focus of reform is electric power marketing. The formulation of electric power marketing strategy needs scientific decision-making analysis as guidance. With the increase of power demand, the scale of power grid is also expanding continuously. With the introduction of new energy equipment, the power system is becoming more and more complex, and it is difficult for relevant staff to effectively monitor and analyze the system. Combined with the above situation, this paper combined Bayesian network to build a power marketing decision analysis system, and combined Bayesian algorithm to test the power marketing real-time cost control system. The experimental results showed that the average judgment accuracy was 91.90 %, and the average warning time was 0.39 s. From the above data, it can be seen that this algorithm can play a good optimization effect on the performance of the system. In this paper, the elasticity test of the power system was also carried out from the aspect of wind speed, and the results showed that the maximum elasticity value can reach 0.94. It can be seen that the elasticity effect of the power system is good as a whole under different wind speeds. [ABSTRACT FROM AUTHOR]

Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy. [ABSTRACT FROM AUTHOR]

Several recently signed environmental agreements and protocols emphasize the global need to reduce GHG emissions, with a focus on limiting coal consumption due to high NOx and CO2 emissions. However, many countries, including those in the Western Balkans, rely heavily on coal for electricity generation. The outdated thermal power plant infrastructure in these regions poses a major challenge when it comes to meeting modern environmental standards while maintaining efficiency. This study is part of the more comprehensive research which aims to develop an expert system that utilizes existing measurements to estimate key parameters crucial for both energy production and pollution reduction. The focus is on Serbian thermal power plants, particularly plant Nikola Tesla unit B1. One of the critical parameters for optimizing thermal power plant control loops is the heating value of coal, which is challenging to measure in real time due to the coal's varying chemical compositions and caloric values. This paper examines 74 different parameters measured in 59 instances to estimate the hating value of coal at unit B1. Through detailed analysis and feature selection methods, including linear regression, this research aims to identify the most informative parameters for estimating the heating value of coal, which will improve the control system that enables more efficient and environmentally friendly power generation in coal fired thermal power plants. [ABSTRACT FROM AUTHOR]

The development of public transportation is considered a vital issue in reducing traffic as well as urban pollution. City buses play an important role in the city transportation system. In Iran, due to the high average age of city buses, it is necessary to replace the old buses with new ones, To replace the old buses, diesel and CNG, hybrid, and electric buses are proposed as the main alternatives. Global warming and the energy crisis are now considered as two potential serious threats for the world. Therefore, energy consumption and CO2 emissions are examined as two outstanding criteria for comparing candidate buses in this paper. To make an accurate comparison, the amount of energy consumption and CO2 emissions have been calculated based on the well-to-wheel approach. The electric bus well-to-wheel analysis has been done for both electricity generation mix and renewable generation. To perform more accurate calculations and simulations, as a case study, a real driving cycle has been constructed for Tehran. For this approach, a modified micro trip method as a novel solution is presented to synthesize the driving cycle. The results show that due to the high share of fossil power plants (about 92%) in Iran, the use of electric buses in the bus fleet may not have much effect on reducing energy and CO2 eq emissions. By using renewable power plants, the amount of well-to-wheel energy consumption and CO2 emissions decrease significantly (about 56% and 93%, respectively) compared to that for the generation mix. [ABSTRACT FROM AUTHOR]

Due to climate change, emission balancing is a relevant tool to quantify the environmental impact of a building system. The electrification of energy production at a national level, as well as energy supply at a building level, shifts the focus to the emission factor (EF) of the electricity grid. Currently, static EFs are used for calculating the emission balance. However, the electricity grid already shows fluctuations in power generation and EF due to renewable energies. The paper reviews recent literature outlining the research gap and presents the development of a simulation setup and concept, in which the emission balance of the building operation changes, using dynamic EFs that map fluctuations at an hourly resolution. In the first step, we simulate the thermal building and radiance performance. The data are than used in a second step to conduct a system simulation, which analyzes the effects of the dynamic EFs. The results show that the dynamic balance approach for different building system variants deviates considerably from the static approach. By comparing different concepts for the loading strategy, the predictive strategy outperforms a common control strategy, when considering the energy prices and/or the emissions. This is especially true for systems with inert storage units, where charging times significantly influence the balance, as well as for systems with PV integration. This paper outlines the potential of the EFs-optimized control increases when evaluating a potential scenario for the year 2040, factoring in increased seasonal and daily fluctuations in electricity generation. [ABSTRACT FROM AUTHOR]

Unreliable resources in conventional power systems have created new challenges for the users of these systems. These sources of production, including wind and solar sources, with a significant share in the power generation of many power systems, cause problems in the safe and stable operation of power systems due to uncertainty. Therefore, it is very important to provide a method or model for safe and stable operation of power systems. The main innovation of this paper is to present a new SCUC model to maximize the flexibility of the system along with reducing the operating cost in the form of a security constrained multi-level robust optimization problem in the presence of wind uncertainty sources. Also, a set of demand response programs and rapid response hybrid cycle units are used as sources of flexibility. Accordingly, the tolerable range of uncertainty and system slope reservation is developed with a low-cost combination and with the commitment to flexible covering resources. The desired optimization problem has been solved with the help of the NCCG algorithm and MILP method. The efficiency and reliability of this proposed model have been simulated and evaluated in IEEE 6-bus and 118-bus standard power systems, and the simulation results confirm the technical and economic efficiency of this model. [ABSTRACT FROM AUTHOR]

Recognising the urgency of addressing climate change and the imperative to mitigate its effects, the European Union (EU) has embarked on a transformative journey to reshape its energy landscape, with a pronounced emphasis on harnessing renewable energy sources (RESs) and augmenting their contribution to electricity generation. To propel Europe towards sustainable development through its energy transition, EU member states are encouraged to foster collaborative efforts on a European scale, inviting neighbouring countries to participate in joint ventures aimed at leveraging RESs for electricity generation. Consequently, it becomes imperative to evaluate the potential depth of cooperation among these nations, assessing how such partnerships can align with Europe's overarching objectives while fostering mutually beneficial conditions. This paper seeks to undertake a thorough analysis and evaluation of the potential impacts of such cooperation, both in advancing RES objectives and in promoting broader cooperation goals concerning the countries involved. The appropriate methodological framework has been developed, utilising and implementing the fuzzy PROMETHEE multicriteria decision analysis method, to address the problem's multidimensional character, intending to implement an appropriate action plan and promote production from RESs. The methodology has been applied to assess alternative strategies in two case study countries, Morocco and Egypt, while important outcomes have emerged towards the successful implementation of cooperation mechanisms promoting RES. [ABSTRACT FROM AUTHOR]

The use of photovoltaic panels for sustainable electricity generation is increasing worldwide. Hence, large solar power plants must be monitored to find defects quickly and easily, avoiding prolonged interruptions in electricity generation. The present study aims to analyse the incorporation of transfer learning in convolutional neural network models to classify defects in visible spectral images of solar panels. Deep learning with convolutional neural networks is known for their precise classification of images, but they need a significant volume of images and training time. Transfer learning is intended to help the training process become faster and more precise. In addition, a publicly available image dataset was constructed using 36,000 images containing three classes of defects and a class without defects to evaluate tested network models. In this study, 17 networks were tested as potential classification models. The best network exhibited an accuracy higher than 99%. This accuracy was obtained with the MobileNetV3 network, which was optimised with Nvidia Tensor RT to run on an edge device with low power consumption and low weight, enabling the real-time classification of the defects presented in this study and allowing the classification of an image in an average of 50 ms. This approach has yet to be explored in the literature, and this paper aims to contribute to this discussion. The presented work has the limitation of not making image segmentation, where the image obtained by the camera is directly classified. From experiments with a large dataset close to an in-field solar plant inspection, trained models successfully classified the defined classes. These findings help solar plant operation and maintenance teams make quick and accurate decisions about scheduled maintenance. [ABSTRACT FROM AUTHOR]

Accurate parameter identification plays a crucial role in realizing precise modelling, design optimization, condition monitoring, and fault diagnosis of photovoltaic systems. However, due to the nonlinear, multivariate, and multistate characteristics of PV models, it is difficult to identify perfect model parameters using traditional analytical and numerical methods. Besides, some existing methods may stick in local optimum and have slow convergence speed. To address these challenges, this paper proposes an enhanced nature‐inspired OLARO algorithm for PV parameter identification under different conditions. OLARO is improved from ARO incorporating existing opposition‐based learning, Lévy flight and roulette fitness‐distance balance to improve global search capability and avoid local optima. Firstly, a novel data smoothing measure is taken to reduce the noises of I–V curves. Then, OLARO is compared with several common algorithms on different solar cells and PV modules using robustness analysis and statistical tests. The results indicate that OLARO has better ability than others to extract parameters from PV models such as single diode, double diode, and PV module models. Moreover, the convergence performance of OLARO is more excellent than the other algorithms. Additionally, the I–V curves of two PV modules under different irradiance and temperature conditions are applied to verify the robustness of the proposed parameter extraction algorithm. Besides, OLARO is successfully applied to two real operating PV modules, and it is compared with two recent well‐known methods improved by FDB. Finally, sensitivity analysis, stability analysis, and discussion of practical challenges are provided. [ABSTRACT FROM AUTHOR]

This paper presents reduced-order modeling of thermal power dispatch (TPD) from a pressurized water reactor (PWR) for providing heat to nearby heat consuming industrial processes that seek to take advantage of nuclear heat to reduce carbon emissions. The reactor model includes the neutronics of the reactor core, thermal–hydraulics of the primary coolant cycle, and a three-lump model of the steam generator (SG). The secondary coolant cycle is represented with quasi-steady state mass and energy balance equations. The secondary cycle consists of a steam extraction system, high-pressure and low-pressure turbines, moisture separator and reheater, high-pressure and low-pressure feedwater heaters, deaerator, feedwater and condensate pumps, and a condenser. The steam produced by the SG is distributed between the turbines and the extraction steam line (XSL) that delivers steam to nearby industrial processes, such as production of clean hydrogen. The reduced-order simulator is verified by comparing predictions with results from separate validated steady-state and transient full-scope PWR simulators for TPD levels between 0% and 70% of the rated reactor power. All simulators indicate that the flow rate of steam in the main steam line and turbine systems decrease with increasing TPD, which causes a reduction in PWR electric power generation. The results are analyzed to assess the impact of TPD on system efficiency and feedwater flow control. Due to the simplicity of the proposed reduced-order model, it can be scaled to represent a PWR of any size with a few parametric changes. In the future, the proposed reduced-order model will be integrated into a power system model in a digital real-time simulator (DRTS) and physical hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

In interconnected power systems, operated by several system operators, the participating areas are strongly dependent on their neighbors. In order to identify the economically efficient generation dispatch in each area, a distributed multi-area optimal power flow (OPF) must be solved. Additionally, installation of an increasing number of HVDC lines to deal with increased power flows from renewable generation is expected to change the power system operation paradigm. The controllability introduced by the HVDC lines should be considered and incorporated in the OPF algorithm. In this paper we introduce a formulation for the distributed solution of the OPF problem in multi-area systems consisting of both HVAC and HVDC lines. We show the applicability of this formulation on two different operating schemes for HVDC grids and we compare their performance with a central solution for the mixed HVAC/HVDC grid. The proposed formulations are based on a linearized solution of the OPF problem. The only data to be exchanged between the areas pertains to the border nodes. [ABSTRACT FROM AUTHOR]

Load profile classification is very important in load forecast, planning and management. Although customers are generally grouped by utilities into residential, commercial classes and respective subclasses, there is a lack of systematic framework that can be used to characterize different classes with signatures that are both human-readable and machine-readable. The work presented in this paper attempts to formulate the theoretical framework for customer classification using the annual load profiles. This paper demonstrates how to extract characteristic attributes in frequency domain (CAFD) and use these CAFDs to formulate a hierarchy of load profiles that can be used as the systematic framework for customer load classification. As signatures for customer classes and subclasses, the CAFDs are obtained by using a data mining method called CART (classification and regression tree). The paper presents a load profile classification test to establish the efficacy of the proposed approach which is significant improvement over current practices that provide mostly qualitative labeling. [ABSTRACT FROM AUTHOR]

Iraqi people have been without energy for nearly two decades, even though their geographic position provides a high intensity of radiation appropriate for the construction of solar plants capable of producing significant quantities of electricity. Also, the annual sunny hours in Iraq are between 3,600 to 4,300 hours which makes it perfect to use the photovoltaics arrays to generate electricity with very high efficiency compared to many countries, especially in Europe. This paper shows the amount of electric energy generated by the meter square of crystalline silicon in the photovoltaic (PV) array that already installed in 18 states in Iraq for each month of the year. The results of the meter-square of PV array in three tracking positions are presented in this paper. This paper shows that the average electricity generated in North cities (Dohuk, Al-Sulaymaniyah, and Erbil) are less than the southern cities in the winter season (three positions) by about 40-50%. Iraq has a stable PV electrical generation during all the year in all regions except the North cities while the highest cities in electricity generation are (Najaf and Al-Anbar). [ABSTRACT FROM AUTHOR]

Copyright of Daluyan: Journal ng Wikang Filipino is the property of University of the Philippines - Diliman and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Molasseswastewater contains largeamounts of glucose, and it can provide enough energy for microbial decomposition. The microbial fuel cell (MFC) in this study was demonstrated to be able to treat real wastewater with the benefit of harvesting electricity energy. Efficient operation of this MFC requires a molasses wastewater and preferably an inexpensive anode electrolyte. The results from a batch of experiments showed that molasses wastewater could not only serve as the electron acceptor in anode, but also generate electricity stably. A maximum voltage output of 514.5 mV and a maximum power density of 65.82 mW/m2 were recorded at external resistance of 1,000 Ω. The MFC not only effectively dealt with the molasseswastewater, the chemical oxygen demand removal rate is 81.22%, but also had a significant effect in the processing of analog silver wastewater. At the end of the experiment, after disassembling the device, silver precipitation was found stacked on the cathode carbon paper electrode, and some black sediment was found at the side of the proton membrane anode. [ABSTRACT FROM AUTHOR]

Greater energy efficiency and a secure, low carbon energy mix for the long-term are at the heart of the UK government's Energy White Paper which was published in May 2007. [ABSTRACT FROM AUTHOR]

Bifacial photovoltaic cells can produce electricity from incoming solar radiation on both sides. These cells have a strong potential to reduce electricity generation costs and may play an important role in the energy system of the future. However, today, these cells are mostly deployed with one side receiving only ground reflection, which leads to a profound sub-optimal utilization of one of the sides of the bifacial cells. Concentration allows a better usage of the potential of bifacial cells, which can lead to a lower cost per kWh. However, concentration also adds complexity due to the higher temperatures reached which add the requirement of cooling in order to achieve higher outputs. This way, this paper focuses on the effectiveness of forced air circulation methods by comparing the thermal performance of three specific concentrating bi-facial collector designs. This paper developed a computational model, using ANSYS Fluent intending to assess the thermal performance of a covered concentrating collector with bifacial Photovoltaic (PV) cells. These results have then been validated by outdoor measurements. Results show that even a simple natural ventilation mechanism such as removing the side gable can effectively reduce the receiver temperature, thus resulting in favourable cell operation conditions when compared to the case of an airtight collector. Therefore, compared with a standard model, a decrease of 13.5% on the cell operating temperature was reported when the side gables are removed. However, when forced ventilation is apllied a 22.8% reduction on temperature is found compared to the standard air-tight model. The validated CFD model has proven to be a useful and robust tool for the thermal analysis of solar concentrating systems. [ABSTRACT FROM AUTHOR]

Over the past few years, the offshore wind sector has been subject to renewed yet growing interest from the industry and from the research sphere, with a particular focus on a recently developed concept, the floating offshore wind (FOW). Because of its novelty, floating research material is found in limited quantity. This paper focuses on the layout optimization of a floating offshore wind farm (FOWF) considering multiple parameters and engineering constraints, combining floating-specific parameters together with economic indicators. Today's common wind farm layout optimization codes do not take into account either floating-specific technical parameters (anchors, mooring lines, inter-array cables (IACs), etc.) or non-technical parameters (operational expenditure, OPEX; capital expenditure, CAPEX; and other techno-economic project parameters). In this paper, a multi-parametric objective function is used in the optimization of the layout of a FOWF, combining the annual energy production (AEP) together with the costs that depend on the layout. The mooring system and the collection system including the inter-array cables and the offshore substation are identified as layout-dependent and therefore modeled in the optimization loop. Using ScotWind site 10 as a study case, it was found with the predefined technical and economic assumptions that the profit was increased by EUR 34.5 million compared to a grid-based layout. The main drivers were identified to be the AEP, followed by the anchors and the availability associated with the failures of inter-array cables. [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]

In this paper, the coupled dynamics of the floating platform and the wind turbine rotor are analyzed. In particular, the damping is explicitly derived from the coupled equations of the rotor and floating platform. The analysis of the damping leads to the study of instability phenomena, thus obtaining the explicit conditions that lead to the non-minimum phase zero (NMPZ). Two NMPZs are analyzed, one related to the rotor dynamics and the other one to the platform pitch dynamics. The latter introduces a novelty, and an explicit condition is provided in this work for its verification. In the second part of the paper, from the analysis of the damping of the floating platform, a new strategy for the control of floating offshore wind turbines (FOWTs) is proposed. This strategy allows one to impose on the controller an explicit level of damping in the platform pitch motion that adapts with wind speed and operating conditions without changing the period of platform pitching. Finally the new strategy is compared to one without compensation and one with a non-adapting compensation by performing aero-hydro-servo-elastic numerical simulations of a reference FOWT. Generated power, motions, blade pitch and tower base fatigue are compared, showing that the new control strategy can reduce fatigue in the structure without affecting the power production. [ABSTRACT FROM AUTHOR]

The safety and robustness of Deep Geological Repositories (DGRs) are of paramount importance for the long-term management of spent nuclear fuel from electricity generation. The introduction of a multi-barrier system, which includes the host rock formation and an engineered barrier system (including the bentonite buffer), has been a widely used approach to ensure the safety of DGRs. The assessment of the long-term safety of DGRs involves the mathematical modeling of the coupled thermal–hydraulic–mechanical–chemical (THMC) processes that occur in the near-field of the DGRs and their impact on the behaviour and engineering properties of the bentonite buffer. This paper presents a review of the THMC-coupled processes that arise in the bentonite buffer as well as a mathematical model governing such coupled processes. The model is verified against existing analytical solutions and validated against measured data of a thermal diffusion experiment in a sand bentonite column. Also, scoping analyses were performed to assess the influence of coupled THM processes on solute transport in clayrocks. The results of the numerical model closely matched those of the analytical solutions and experimental data demonstrating the capability of the provided mathematical model as well as the numerical approach in enhancing our comprehension of DGR behaviour. This enhanced comprehension will be valuable for safety prediction and assessment in the context of DGRs. The work presented in this paper is part of the Canadian Nuclear Safety Commission's (CNSC) regulatory research to gain independent knowledge on the safety of the geological disposal of radioactive waste. [ABSTRACT FROM AUTHOR]

Generating sustainable electricity from ambient humidity and natural evaporation has attracted tremendous interest recently as it requires no extra mechanical energy input and is deployable across all weather and geography conditions. Here, we present a device prototype for enhanced power generation from ambient humidity. This prototype uses both heterogenous materials assembled from a LiCl-loaded cellulon paper to facilitate moisture adsorption and a carbon-black-loaded cellulon paper to promote water evaporation. Exposing such a centimeter-sized device to ambient humidity can produce voltages of around 0.78 V and a current of around 7.5 μA, both of which can be sustained for more than 10 days. The enhanced electric output and durability are due to the continuous water flow that is directed by evaporation through numerous, negatively charged channels within the cellulon papers. The voltage and current exhibit an excellent scaling behavior upon device integration to sufficiently power commercial devices including even cell phones. The results open a promising prospect of sustainable electricity generation based on a synergy between spontaneous moisture adsorption and water evaporation. Generating electricity from air opens a promising way for green energy harvesting. Here, authors present a prototype driven by the integration of moisture adsorption with evaporation to generate continuous electricity for a long duration. [ABSTRACT FROM AUTHOR]

The ESCOM (Electricity Supply Corporation of Malawi) currently faces difficulties in monitoring and controlling critical parameters, such as electricity generation, transmission, and distribution in real-time. Inefficient energy management, lack of fault detection mechanisms, manual interventions, and limited data-driven decision-making capabilities are hindering ESCOM's ability to deliver reliable and efficient electricity services to the people of Malawi. The main objective of this work is to address the pressing challenges faced by ESCOM (Electricity Supply Corporation of Malawi) in effectively managing and optimizing electricity distribution within their infrastructure. This paper introduces an IoT-based industrial management system designed to tackle these challenges and enhance ESCOM's operational efficiency, energy management, and overall performance. [ABSTRACT FROM AUTHOR]

Transformers are essential components of electric power generation, transmission, and distribution, and the majority of power transformers rely on liquid dielectrics as an insulating medium and for heat transfer. At present, the most common liquid dielectrics used are the mineral oils, which are produced from the middle range of petroleum-derived distillates. In recent years, concerns have been expressed regarding the presence of polynuclear aromatic hydrocarbons in mineral oils, because in the event of a transformer fire, or explosion, polynuclear aromatic hydrocarbons could readily be released into the environment. This situation has put mineral-oil-filled transformers in sensitive areas, such as inside buildings, in residential areas, and near schools or hospitals, under scrutiny. Human health and environmental concerns have led to efforts to develop alternative transformer insulating oils for use in these sensitive areas. In particular, vegetable oils have drawn the most attention, and research on the properties and performance of vegetable oils has been carried out in a few laboratories [1]. [ABSTRACT FROM PUBLISHER]

Although a recent report by the U.S. Energy Information Administration indicates that CO2 emissions from power generation have now fallen below emissions from the transportation sector, electricity use remains a significant part of human's environmental footprint since fossil fuels are the major energy resource for electricity generation worldwide. This explains the continual effort by policy makers to develop a remedy to mitigate carbon emissions. It is then critically important to properly account for the emissions associated with electricity generation, delivery, and consumption. In particular, it is critical to be able to allocate emissions to the relevant electricity consumers creating the demand that results in a polluting emission. Therefore, in this paper a virtual carbon emission tracing method in power systems is thoroughly presented to facilitate allotting carbon obligation. The method is developed based on the proportional sharing principle that has been used in power system operation. The proposed methodology is implemented on the IEEE 5‐bus and 9‐bus systems, two widely used and recognized power grid benchmarks. [ABSTRACT FROM AUTHOR]