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Electrical vehicles are finding wide acceptance in the Jordanian transportation market; this has caused an accelerating shift in the emissions of greenhouse gases from the direct burning of fossil fuels consumed by the transportation sector towards the power generation sector. On the other hand, as electric vehicles gain more popularity, an extra load is added to the electrical power generation systems, raising essential concerns such as the capability of the power network to support this massive extra load and the increased emission of greenhouse gases caused by power plants. Studies show that Jordan's weather is known for being bright, sunny, and very suitable for the generation of electric power from solar energy sources. Therefore, with an infrastructure that can support convenient off-grid charging, a huge burden will be taken off the national grid and the environment. Therefore, this work proposes a basic design for an off-grid PV-covered carport and presents a study of the economics and effectiveness of using such a system to charge electric vehicles owned by the students and employees of the Applied Science Private University. The study is based on actual solar irradiance data collected on-site during university working hours (8 a.m.–5 p.m.) to allow students and employees to charge their electric vehicles from an off-grid carport system while on campus. Space limitations for carport design, initial design cost, return on investment, and annual electricity consumption are discussed to demonstrate the benefits of such a system for both the consumer (convenience and low charging cost) and the power company provider (less load to maintain). [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]

Using renewable energies is one of the alternatives to mitigate climate change. Among them, photovoltaic energy has shown a relevant growth of participation in the electric sector. In the backdrop of such growth, in countries such as Brazil, photovoltaic energy has surpassed the generation of electricity by petroleum derivatives since 2019. The significant growth in photovoltaic generation around the world can be attributed to several key factors, including abundant sunlight, supportive government policies, falling solar panel costs, environmental concerns, energy diversification goals, investor interest, job creation, and local manufacturing. However, photovoltaic system performance is heavily tied to weather variability. Different models are used to account for this meteorological dependence; however, there is a gap regarding the differences in the outputs of these models. The study presented here investigates the variability and sensitivity of the models used to estimate photovoltaic production ( P p v ). Six models were compared by percentage difference analysis. Statistical analyses from the perspective of variability revealed that the difference between the P p v estimated by these models reaches a 12.89% absolute power difference. Considering that temperature and solar irradiance are the meteorological variables that most influence P p v , the sensitivity analysis focused on these. Regarding sensitivity, in the context of temperature changes, the average relative difference in P p v between models can reach up to 5.32% for a 10 °C change, while in the context of changes in solar irradiance, the average relative difference can reach up to 19.05% for a change of 41.67 W/m2. The consideration of the variability and sensitivity of the main sets of equations used to estimate the potential of photovoltaic energy production can help refine methodologies and assumptions in future research in this area. There are variations and sensitivities, as observed, of such magnitude that, depending on the set of equations adopted in the study, they can alter the conclusion about photovoltaic energy production in a given region. Accurate estimations are pivotal not only for feasibility analyses but also for gauging economic and socio-environmental impacts. These divergences can, in turn, reformulate feasibility analyses and compromise the reliability of photovoltaic energy systems, thus leading to different economic and socio-environmental consequences. [ABSTRACT FROM AUTHOR]

This document explores the challenges and solutions related to the sustainable development of new power systems under dual carbon goals. The integration of renewable energy sources has impacted the stability of power systems, requiring measures such as grid control, optimization, and accurate forecasting of renewable energy generation. The accurate prediction of photovoltaic and wind power is crucial, and deep learning-based models have shown superior forecasting accuracy. Control and optimization strategies, such as voltage feedforward control and adaptive controllers, enhance system stability. Effective control of the power grid also has economic benefits, and planning and scheduling play a significant role in power system reliability and stability. System state management and intelligent question-answering systems contribute to efficient system operation. The advancement of artificial intelligence technology and big data analytics is crucial for enhancing power system stability and efficiency. Overall, these measures are essential for the sustainable development of power systems. [Extracted from the article]

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh. [ABSTRACT FROM AUTHOR]

Wave energy conversion holds promise for renewable energy, but challenges like high initial costs hinder commercialization. Integrating wave-energy converters (WECs) into shore-protection structures creates dual-function structures for both electricity generation and coastal protection. Oscillating water columns (OWCs) have been well studied in the past with their simple generation mechanism and their out-of-water power take-off (PTO) system, which can minimize bio-fouling effects and maintenance costs compared to other submerged WECs. In addition, a slotted barrier allows for better circulation behind the breakwater while dissipating incoming wave energy through viscous damping. This study examines the performance of a new design which combines an OWC with a slotted breakwater. Small-scale (1:49) laboratory tests were performed with a piston-type wave generator. The performance is evaluated in terms of wave transmission, wave energy extraction, and wave loading under various wave conditions while focusing on the effects of the porosity of the slotted barrier and tide level changes. Results show that under larger waves, a decreasing wave transmission, increasing power extraction from the OWC, and energy dissipation from the slotted barrier are observed. On the other hand, under increasing wavelengths, wave transmission is observed to be constant; this is important for harbor design, which means that the breakwater is effective under a wider range of wavelengths. Porosity allows for more transmission while inducing less horizontal force on the structure. [ABSTRACT FROM AUTHOR]

This study investigates how differences in energy-related properties of cities influence the composition of a cost-efficient urban energy system, assuming electrification of the transport and industry sectors and zero-emission of CO2. These differences are evaluated for two scenarios regarding the capacities of the modeled cities to import electricity. A linear optimization model that encompasses the electricity, heating, industry, and transport sectors, using measured data from six cities in Sweden, is applied. Results show that when strict constraints on electricity imports are enforced, cities with a lower ratio of annual electricity demand for heat encourage the implementation of power-to-heat solutions in the heating sector. This study also reveals that under such stringent electricity import conditions, cities with a high level of flexibility in electricity demand favor a combination of batteries and solar photovoltaics as opposed to biomass-based electricity production. Conversely, when electricity importation is less restricted and biomass prices surpass 20 EUR/MWh, local electricity generation is outcompeted by imports, and large-scale heat pumps working in tandem with thermal energy storage dominate the heating sector in all modeled cities. This assertion holds true when the maximum electricity import capacity is utilized up to 5000 h annually. [ABSTRACT FROM AUTHOR]

This publication explores current and prospective methods for hydrogen production and purification, with a strong emphasis on membrane-based technologies for purification and separation. This focus is justified by the ongoing shift towards renewable energy sources (RESs) in electricity generation, necessitating strategic changes to increase hydrogen utilization, particularly in the automotive, heavy road, and rail sectors, by 2025–2030. The adoption of hydrogen from RESs in the construction, energy, and industrial sectors (e.g., for process heat or fertilizer production) is also under consideration, driving the need for innovative production, separation, and purification methods. Historically, industrial-scale hydrogen has been predominantly derived from fossil fuels, but renewable sources such as electrolysis, biological, and thermal processes now offer alternatives with varying production efficiencies (0.06–80%) and gas compositions. Therefore, selecting appropriate separation and purification methods is critical based on specific usage requirements and the gas composition. Industrial-scale hydrogen purification commonly employs pressure swing adsorption (PSA) technologies, capable of achieving up to 99.99% purity. Cryogenic distillation is suitable for applications needing up to 95% purity. Membrane technologies, including polymer, metallic, and electrolytic membranes, have traditionally been limited to moderate volumes of pure gas production but are crucial for hydrogen purification and separation. This publication critically evaluates the potential of membrane technology for hydrogen separation, particularly in response to the anticipated rise in demand for RES-derived hydrogen, including from renewable feedstocks. [ABSTRACT FROM AUTHOR]

The relationship between a region's dependency on variable renewable energy (VRE) and the viability of long-duration energy storage (LDES) technologies is recognised through various electricity grid modelling efforts in the contemporary literature. Numerous studies state a specific VRE penetration level in total electricity generation as an indicator of the emergence of an LDES market. However, there is considerable variability across studies when comparing VRE penetration levels in conjunction with LDES technology utilisation, and significant diversity exists in electricity grid modelling approaches. This review aims to highlight these inconsistencies by offering an overview of disparate findings and dissecting the influencing variables. Sixteen parameters are identified from reviewed studies, complemented by an additional five recognised through in-depth analysis. This comprehensive examination not only sheds light on critical aspects overlooked in previous reviews, requiring further investigation, but also provides novel insights into the complexity of this correlation, elevating the understanding of LDES market creation by unravelling the factors that influence the technology adoption across various contexts. Furthermore, it provides clarity in LDES research terminology by rectifying ambiguous language in the existing literature. Altogether, seven databases were explored to produce a trustworthy foundation for the study. [ABSTRACT FROM AUTHOR]

Ensuring a balance between supply and demand is critical within electricity grids, requiring a supply composition that guarantees consistent service provision in the short and medium term. Between 2008 and 2017, Ecuador's electricity generation capacity expanded significantly, with an investment of approximately USD 8150 million into harnessing the potential energy of water. This led to the construction of five high-capacity hydroelectric projects by 2017, contributing 33.4% of the sector's energy output by 2023. However, it is important to note that if installed hydroelectric projects operate as run-of-river plants, with limited reservoir capacity, they can only regulate water use for electricity generation on an hourly basis. As of 2023, these run-of-river plants represent 68.8% of Ecuador's total hydroelectric capacity within the National Interconnected System (SNI). Consequently, during periods of low inflows, supplementary energy from other power generation plants is necessary to prevent energy crises. This paper addresses the impact on energy storing for electricity generation resulting from the evolution of hydroelectric power plant entry from 2006 to 2023. This aspect has not been thoroughly examined in hydrothermal systems, which primarily focus on potential energy obtained from dams. Our approach involves a statistical analysis of hydroelectric dam reservoir operational levels. We further explore the influence on demand service within Ecuador's electricity system, particularly during observed energy crises towards the end of 2023. [ABSTRACT FROM AUTHOR]

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real time. Through precise prediction of the load and the photovoltaic (PV) and wind power outputs, the proposed approach guarantees optimum scheduling. The precise predictions of the load, PV, and wind power levels are achieved by employing a predictor of the Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational costs of the hybrid power plants and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the feasibility of the proposed dispatch engine. Real-time measurement data pertaining to load and the wind and PV power outputs are obtained from different locations in the Sultanate of Oman. The real-time data are utilized to predict the future levels of power output from PV and from the wind farm over the course of 24 h. The predicted power levels are then used in combination with a PV–Wind–DG–ESS–Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed FFNN's powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce a plant's running expenses. [ABSTRACT FROM AUTHOR]

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called "grid–coordinate" based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process. [ABSTRACT FROM AUTHOR]

Recently, there has been a push by countries to diversify their energy mix considering various factors. In this regard, there have been several studies conducted to assess the potential for using sources such as wind and solar to generate supplemental energy to the already present energy generation setup. In this regard, this study explores the potential of wind for the Commonwealth of Kentucky. To perform this study, wind data were sourced for eight locations across Kentucky from the publicly accessible wind speed information present at Weather Underground for the years 2020–2021 (two years). An analysis was performed concerning the seasonal, monthly, and hourly variation in the wind speed so as to identify the expected times of sufficient wind energy generation. Moreover, a comparison of the collected data was performed with data from a home-based weather station as well as a deployed wind turbine to validate the variation pattern of the publicly sourced data. Finally, in order to investigate the variation patterns of wind and solar energy sources, a comparative analysis was also performed using data from a solar power generation plant in Kentucky. A seasonal and monthly complementarity was observed between the wind and solar energy. However, when considering daily patterns, the wind was found to follow solar generation with an offset. While further research is required, this analysis indicates that it is possible to deploy wind energy power generation projects in the Commonwealth of Kentucky. The seasonal complementary behavior of wind and solar energy can be used along with battery storage in conjunction with natural gas to provide a diversified electricity generation portfolio. [ABSTRACT FROM AUTHOR]

This review explores the social impacts of electricity production by applying the framework of Social Life Cycle Assessment (S-LCA). The authors adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to select studies that were published post-2010 and used S-LCA in the context of various electricity sources, including bioelectricity, solar, wind, and hydropower. The search yielded 13 eligible studies that employed both generic and site-specific assessment strategies, primarily relying on the Social Hotspot Database and Product Social Impact Life Cycle Assessment database for generic evaluations. The findings emphasize the Workers stakeholder as the most frequently examined, with significant attention also given to the Local Community, Society, Value Chain Actors, and Consumer stakeholders when social databases are employed. The studies primarily assessed socioeconomic impact subcategories related to labor practices, health and safety, and economic contributions, as well as a tailored set of self-developed social impacts and indicators specific to the energy sources and geographical contexts examined. This review demonstrates the crucial role of S-LCA in revealing the socio-economic impacts of electricity generation and the need to consider the impacts on Local Community and Society stakeholders through site-specific assessments. Such insights are crucial for guiding policy reforms and industry practices towards more socially responsible energy production. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) panels are projected to become the largest contributor of clean electricity generation worldwide. Maintenance and cleaning strategies are crucial for optimizing solar PV operations, ensuring a satisfactory economic return of investment. Nanocoating may have potential for optimizing PV operations; however, there is insufficient scientific evidence that supports this idea. Therefore, this study aims to investigate the effectiveness of nanocoating on the performance of solar photovoltaic (PV) panels installed in Al Seeb, Oman. A further study was also carried out to observe the influence of coating layers on the performance of PV panels. One SiO2 nanocoated solar panel, another regularly cleaned PV panel, and a reference uncleaned panel were used to carry out the study. The site of the study was treeless and sandy, with a hot and dry climate. A data logger was connected to the solar PV panel and glass panel to record the resulting voltage, current, temperature, and solar radiation. It was observed that nanocoated PV panels outperformed both regular PV panels and uncleaned PV panels. Nanocoated PV panels demonstrated an average efficiency of 21.6%, showing a 31.7% improvement over uncleaned panels and a 9.6% improvement over regularly cleaned panels. Although nanocoating displayed high efficiency, regular cleaning also contributes positively. Furthermore, even though nanocoated PV panels outperformed the other two panels, it is important to note that the performance difference between the regular cleaned PV panels and the nanocoated PV panels was small. This indicates that regular cleaning strategies and nanocoating can further contribute to maintaining a more efficient solar PV system. Coating in many layers was also observed to influence the performance of PV panels insignificantly, mainly the fourth layer coating appeared to have formed sufficient mass to retain heat. [ABSTRACT FROM AUTHOR]

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % P O 4 3 − —the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation. [ABSTRACT FROM AUTHOR]

Solar energy reaching a horizontal surface can possess fluctuations that impact electricity generation at a solar plant. Despite this, energy access remains inadequate, particularly in rural areas, with an estimated 82% deficiency. This drives us to assess the regressive and spatial-temporal accessibility of solar energy in the southern and mid regions of Mozambique. This evaluation aims to determine the actual availability of energy for electrification purposes. Data on global horizontal irradiation from approximately 8 stations across all provinces in the specified regions, collected between 2012 and 2014 at intervals of 1 and 10 min, were analyzed using regression and correlation methods along with a specialized algorithm for classifying days based on clear sky index terms. The statistical analysis identified days with significant potential for energy accessibility, exceeding 50% of the average. The findings suggest a correlation coefficient of approximately 0.30 for energy and non-linear regression with clear sky index coefficients around 0.80. The method employed demonstrated accuracy when compared to theoretical simulations of the clear sky index in the region, indicating its potential applicability in other regions of interest. [ABSTRACT FROM AUTHOR]

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

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

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]

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]

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]

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]

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

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

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

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

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

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

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

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

Energy production from biomass represents a strategic solution for the achievement of global sustainability goals. In addition, the use of biofuels offers both significant environmental advantages and several socio-economic benefits. In this study, the environmental life cycle impacts associated with the use of woodchips from forest residues for combined heat and power generation in Italy were analyzed. Moreover, the use of woodchips was compared to the use of conventional fossil fuels in similar applications, and different biomass supply scenarios were evaluated to understand their effect on the overall impact related to 1 kWh of electricity. The impacts on "Climate Change" (2.94 × 10−2 kgCO2eq/kWh) and "Resources" (4.28 × 10−1 MJ primary) were revealed to be minimal compared to fossil fuels (reduction of about 95–97%) and forest woodchips emerged as a sustainable alternative for electricity generation. Moreover, impacts regarding "Human health" (3.04 × 10−7 DALY) and "Ecosystem quality" (3.58 × 10−1 PDF·m2·yr) were revealed to be relevant and identified as a research area to be further explored. The findings of this study also highlighted the key role played by the supply mode/distance of the woodchips on the overall life cycle impacts, with the use of "local" biomass representing the best reduction option. Lastly, another aspect to be further investigated is the optimization of the biomass supply. [ABSTRACT FROM AUTHOR]

The reliability and security of an electric power supply have become pivotal to the proper functioning of modern society. Traditionally, the electric power supply system has been designed with the objective of being able to adequately meet present and future demand, with efforts to maintain supply reliability being focused primarily on the supply side. Over the decades, however, the value of demand-side management—efforts focused on enhancing the efficient and effective use of electricity in support of the power system and customer needs—has been widely acknowledged as being able to play a greater role in ensuring that the key objectives of power system operation are satisfied. This article presents a study of demand-side management and opportunities for incorporating it into network planning as an effective means of addressing supply capacity constraints in the South African electric grid. The main drivers, benefits and potential barriers to the effective implementation of demand-side management are examined, along with the main enabling technologies. The key finding of the study is that the effective integration of demand-side management into network planning requires a shift from the traditional network planning approach to one that is more suited to fully exploiting the flexibility resources available on the demand side of the network. [ABSTRACT FROM AUTHOR]

As a part of the transition in higher-level energy systems, distributed cross-sectoral energy systems (DCESs) play a crucial role in providing flexibility in covering residual load (RL). However, there is currently no method available to quantify the potential flexibility of DCESs in covering RL. This study aimed to address this gap by comparing the RL demand of a higher-level energy system with the electricity flow between a DCES and the electricity grid. This can allow for the quantification of the flexibility of DCES operation. Our approach was to categorize existing methods for flexibility quantification and then propose a new method to assess the flexibility of DCESs in covering RL. For this, we introduced a new quantification indicator called the Flexibility Deployment Index (FDI), which integrates two factors: the RL of the higher-level energy system and the electricity purchase and feed-in of a DCES. By normalizing both factors, we could compare the flexibility to cover RL with respect to different DCES concepts and scenarios. To validate the developed quantification method, we applied it to a case study of a hospital's DCES in Germany. Using an MILP optimization model, we analyzed the variation in FDI for different technology concepts and scenarios, including fixed electricity tariffs, dynamic electricity tariffs, and CO2-emission-optimized operation. The results of our calculations and the application of the FDI indicate that high-capacity combined heat and power units combined with thermal storage units provide higher flexibility. Additionally, the results highlight higher flexibility provision during the winter period compared to the summer period. However, further application and research are needed to confirm the robustness and validity of the FDI assessment. Nonetheless, the case study demonstrates the potential of the new quantification method. [ABSTRACT FROM AUTHOR]

In the age of energy transition that we are going through today, many research studies discuss how to develop various approaches to making forecasts aimed at obtaining quantitative assessments of the technical and economic indicators of the energy industry. This paper considers the adaptation of a comprehensive approach to forecasting the structure of energy generation based on the factor and trend approach and using autoregressive and multifactor models that apply a linear regression tool with ridge regularization. To implement this approach, we propose a tool for automated selection of the factors that have the most significant impact on the change in the structure of energy generation. This approach allows us to forecast the dynamics of electricity generation by different types of generating facilities as affected by the key factors in energy transition in the short, medium, and long term. As a result, we obtained quantitative relationships for the energy generation structure. Over the next 10 years, the share of generation using renewable energy sources will increase to 10%, and the share of thermal power plants, on the contrary, will decrease to 50%, despite the growth in demand for electricity. Also, greenhouse gas emissions will be reduced by 30%. We have also provided scientific justification for the sufficient reliability of the forecasts we present. [ABSTRACT FROM AUTHOR]

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]

Hexagonal distributed embedded energy converters, also known as hexDEECs, are centimeter-scale energy transducers that leverage variable capacitance to generate electricity when their hyperelastic structure is dynamically deformed. To better understand, characterize, and optimize hexDEEC designs, a series of numerical methods and techniques were developed to model the hyperelastic mechanics of hexDEECs, electrostatic properties, and electricity generation characteristics. The numerical methods developed for the hyperelastic structural analysis were corroborated by empirical results from another study, and the models and equations for capacitance, electrostatic forces, and electrical potential energy were derived from fundamental electrostatic equations. These methods and techniques were implemented within the STAR-CCM+ multiphysics software Version 2020.3 (15.06.008) environment. Results from this analysis revealed methodologies and techniques necessary to model the energy converters, which will enable future exploration and optimization of more specific designs and corresponding applications. [ABSTRACT FROM AUTHOR]

Microbial fuel cells could be used as an alternative for wastewater treatment and electricity generation. Escherichia coli is a representative bacterium that has been widely studied as a model in laboratory assays despite its limited ability to transfer electrons. Although previous studies have employed glucose and methylene blue in electricity production using E. coli, there remains a lack of understanding on how current generation would impact the production of metabolites and what the most appropriate conditions for current production might be. To shed light on those issues, this manuscript used a 32 factorial design to evaluate the effect of the concentration of organic matter (glucose) and the concentration of the mediator methylene blue (MB) using E. coli DH5α as an anodic microorganism. It was found that as the concentration of glucose was increased, the production of electricity increased and at the same time, its degradation percentage decreased. Similarly, a 17-fold increase in current production was observed with an elevation in methylene blue concentration from 0 to 0.3 mM, though inhibition became apparent at higher concentrations. The maximum power generated by the cell was 204.5 µW m−2, achieving a current density of 1.434 mA m−2 at concentrations of 5 g L−1 of glucose and 0.3 mM of MB. Reductions in the production of ethanol, lactate, and acetate were observed due to the deviation of electrons to the anode. [ABSTRACT FROM AUTHOR]

Measuring and understanding the evolution of the electricity generation efficiency sector is a relevant task for policymakers. This study focuses on assessing the determinants of electricity generation efficiency across 24 countries in Latin America and the Caribbean between 2000 and 2020. A hurdle model was employed to account for the censored nature of the dependent variable. The findings show a positive impact of opening up the electricity sector in fully efficient countries. Market liberalization and the involvement of privately owned power plants contribute to efficiency gains in inefficient countries. Additionally, the tariff protectionism level exhibits positive effects for completely efficient and inefficient countries. These results provide valuable insights, as less-efficient countries can glean strategies from their more efficient counterparts to enhance their electricity generation efficiency. [ABSTRACT FROM AUTHOR]

The transformation of the energy sector, based on the development of low-carbon technologies, is essential to achieve climate neutrality. The Life Cycle Assessment (LCA) is a powerful methodology for assessing the environmental impact of energy technologies, which proves to be a useful tool for policy makers. The paper is a review of the main LCA studies of power generation systems performed over the past ten years aiming at comparing the energy technologies to identify those with the lowest impact on the environment, evaluated in terms of gCO2eq/kWh emissions. Screening criteria were established to include only studies of the highest qualitative significance. The authors decided to assign greater weight to emission values reported in more recent studies. For nuclear and renewable energy technologies, most of the emissions are related to the pre-operational phases. Notably, both nuclear and wind technologies, along with other renewable sources throughout their entire life cycle, exhibit significantly lower and less variable emissions compared with conventional gas- and coal-fired technologies. [ABSTRACT FROM AUTHOR]

In this paper, we present an analysis of the properties of the prototype three-phase Multichannel Permanent Magnet Synchronous Generator (MCPMSG) prototype designed and constructed by the authors. Each channel of the generator has electrically separated windings, which allows us to create an island system of electricity generation. The analyzed MCPMSG is intended for critical applications, and it is designed for four-channel operation. The purpose of this work is to analyze various configurations of the generator channels to improve the redundancy of the electricity generation system. The MCPMSG operation with one or two independent sources of energy consumption in the case of a dual-channel or double dual-channel operation was investigated. For the analyzed cases, the original mathematical models of the three-phase MCPMSG were developed. On the basis of numerical and laboratory tests, the influence of individual configurations on the MCPMSG output parameters was determined. An original method for diagnosing the operation of the MCPMSG channels was developed. Numerical and laboratory tests of the proposed diagnostic method based on a single voltage signal were carried out. As part of the laboratory tests, selected operating states under conditions of full winding symmetry and internal asymmetry were analyzed. The advantage of the proposed diagnostic method is the control of the operating state of the channels both under load and in the de-energized state. The proposed diagnostic method for control of the individual channel requires measurement of only one voltage signal. [ABSTRACT FROM AUTHOR]

The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience. [ABSTRACT FROM AUTHOR]

Several power take-off (PTO) architectures for wave-powered reverse osmosis (RO) desalination of seawater are introduced and compared based on the annual average freshwater production and the size of the components, which strongly relate to the costs of the system. The set of architectures compared includes a novel series-type PTO architecture not previously considered. These seawater hydraulic PTO architectures are composed of a WEC-driven pump, an RO module, an intake charge pump driven by an electric motor, and a hydraulic motor driving an electric generator for electric power production. This study is performed using an efficient two-way coupled steady-state model for the average performance of the system in a given sea state, including freshwater permeate production, electric power production, and electric power consumption. A multi-objective design problem is formulated for the purposes of this comparative study, with the objectives of maximizing annual freshwater production, minimizing the displacement of the WEC-driven pump, and minimizing the installed RO membrane area. This establishes a framework for comparison in the absence of a mature techno-economic model. The requirement that the system produces enough electric power to meet its consumption is applied as a constraint on the operation of the system. The oscillating wave surge converter Oyster 1 is assumed as the WEC. Weights on performance of the system in a given sea state are based on historical data from Humboldt Bay, CA. This study finds that (1) architectures in a series configuration allow for a reduction in the WEC-driven pump size of 59–92% compared to prior work, (2) varying the displacement of the WEC-driven pump between sea conditions does not provide any significant advantage in performance, and (3) varying the active RO membrane area between sea condition offers improvements between 7% and 41% in each design objective. [ABSTRACT FROM AUTHOR]

This study investigated the applicability of a tracking photovoltaic (PV) system installed in the roof area of a commercial building. Because PVWatts is the only PV module with a tracking feature in EnergyPlus, its electricity generation was validated through comparisons with detailed PV modules in EnergyPlus. The tracking PV system generated 26.8–35.5% more electricity annually than a fixed system in the climate of Incheon (S. Korea). The load coverage analysis of the tracking PV system was conducted with the reference commercial building model in EnergyPlus. Approximately 14% of the total building electric demand, including heating, ventilation, and air-conditioning; lighting; and equipment, was met by one PV array. Finally, the life cycle cost analysis of the tracking PV system was conducted by considering the net present value, which includes the initial installation and operation costs. The initial investment was returned after approximately 8 years, assuming between two and six tracking PV arrays were installed. Moreover, up to 26.8% cost savings were achieved in 15 years compared to the case without any PV arrays. [ABSTRACT FROM AUTHOR]

There is much organic waste that comes from by-products of agriculture and product processing, solid waste from livestock, and municipal waste. Conventional methods that are widely used for the treatment and management of organic fractions of waste are landfilling, composting, anaerobic digestion, incineration, gasification, and pyrolysis. Among the above methods, pyrolysis is a relatively simple, robust, and scalable technology for transforming diverse organic waste feedstock into renewable energy products. Recently, the electrochemical conversion of biochar into electricity in direct carbon fuel cells (DCFC) has also been investigated and shown to be feasible and highly efficient. This paper focuses on the utilization of organic waste as a fuel and the investigation of their characteristics during electrochemical reactions in molten hydroxide direct carbon fuel cells (MH-DCFCs). Organic waste of different origins (the food-processing industry, urban and suburban areas, municipal solid organic waste, sewage sludge) with diversified characteristics was used as the main feedstock. The lowest power density was determined for sewage sludge (5.1 mW cm−2), and the best results were obtained for peanut shells (53.14 mW cm−2). This study concludes that higher elemental carbon, lower ash content and the presence of reactive surface oxygen functional groups in biochar obtained from organic waste might contribute to better cell performance. Moreover, the research establishes the potential of carbonized organic waste as a prospective alternative fuel source for power generation in an MH-DCFC. [ABSTRACT FROM AUTHOR]