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Copyright of Scientia Iranica. Transaction D, Computer Science & Engineering & Electrical Engineering is the property of Scientia Iranica 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 energy optimization in smart power grids (SPGs) is crucial for ensuring efficient, sustainable, and cost-effective energy management. However, the uncertainty and stochastic nature of distributed generations (DGs) and loads pose significant challenges to optimization models. In this study, we propose a novel optimization model that addresses these challenges by employing a probabilistic method to model the uncertain behavior of DGs and loads. Our model utilizes the multi-objective wind-driven optimization (MOWDO) technique with fuzzy mechanism to simultaneously address economic, environmental, and comfort concerns in SPGs. Unlike existing models, our approach incorporates a hybrid demand response (HDR), combining price-based and incentive-based DR to mitigate rebound peaks and ensure stable and efficient energy usage. The model also introduces battery energy storage systems (BESS) as environmentally friendly backup sources, reducing reliance on fossil fuels and promoting sustainability. We assess the developed model across various distinct configurations: optimizing operational costs and pollution emissions independently with/without DR, optimizing both operational costs and pollution emissions concurrently with/without DR, and optimizing operational costs, user comfort, and pollution emissions simultaneously with/without DR. The experimental findings reveal that the developed model performs better than the multi-objective bird swarm optimization (MOBSO) algorithm across metrics, including operational cost, user comfort, and pollution emissions. • Presenting a multi-objective model for energy management via day-ahead scheduling in smart grids. • Enhancing day-ahead scheduling with renewables and demand response strategies. • Introducing a probabilistic model for solar and wind energy uncertainty prediction. • Proposing a hybrid demand response to lower peak energy demand and prevent rebound peaks. • Utilizing MOWDO algorithm for optimal Pareto fronts exploration to achieve tri-objective optimization: cost, emissions, and user comfort. [ABSTRACT FROM AUTHOR]

In many different places, wind energy has the potential to solve many challenges. Reducing dependency on distant power networks increases energy security, provides an alternative to energy independence, and advances environmental sustainability as a renewable energy source. Furthermore, the development of wind energy established in these regions improves regional economies and creates possibilities for employment in manufacturing, construction, maintenance, and operation. Landowners might receive more revenue from wind farms through land leasing agreements. Furthermore, wind energy diversifies the energy mix, reducing its vulnerability to fluctuations in energy prices and environmental degradation and decreasing energy expenditures for individuals and companies. Sustainable energy sources are crucial to solving communities' unique energy problems. This study aims to assess Saudi Arabia's wind energy potential. It completes assessments of wind resource availability, site suitability, wind turbine technology, energy output, environmental impact, and economic viability. Statistical techniques such as time series analysis, regression analysis, and probability distributions are utilized to understand and improve wind farm efficiency. [ABSTRACT FROM AUTHOR]

Solar energy is one of the renewable and clean energy sources. Accurate solar radiation (SR) estimates are therefore needed in solar energy applications. Firstly, two deep learning models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were developed in this study. Next, a data pre-processing technique named multivariate variational mode decomposition (MVMD) was used to construct the MVMD-GRU and MVMD-LSTM hybrid models. To better test the performance of proposed simple and hybrid models, four stations located in the Illinois State of the USA (i.e., Dixon Springs, Fairfield, Rend Lake, and Carbondale) were considered as the study sites. Whole the simple and hybrid models were established under two different strategies, i.e., local and external. In the local strategy, SR of each location was estimated using the minimum and maximum air temperatures from the same station. While, minimum and maximum air temperatures as well as SR data from the nearby station were utilized in external strategy to estimate SR time series of any target site. Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics were used when evaluating the models performances. The overall results revealed that the proposed MVMD-GRU and MVMD-LSTM hybrid models illustrated better SR estimates compared to the simple GRU and LSTM in both the local and external strategies. The values of error metrics obtained for the superior hybrid models (i.e., MVMD-LSTM) during the testing period were as: RMSE = 2.532 MJ/m2.day, MAE = 1.921 MJ/m2.day, R2 = 0.916 at Dixon Springs; RMSE = 2.476 MJ/m2.day, MAE = 1.878 MJ/m2.day, R2 = 0.921 at Fairfield; RMSE = 2.359 MJ/m2.day, MAE = 1.780 MJ/m2.day, R2 = 0.924 at Rend Lake; RMSE = 2.576 MJ/m2.day, MAE = 1.941 MJ/m2.day, R2 = 0.914 at Carbondale. Therefore, the coupled models proposed in this study can be possibly recommended as suitable alternatives to the simple deep learning models with a reliable precision in estimating SR time series. [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

In the Vehicle-to-Grid (V2G) scenario, a multitude of coordinated electric vehicles (EVs) equipped with high-capacity batteries actively participate in power grid dispatching as energy carriers, aiming to achieve a tripartite objective encompassing peak load reduction and valley filling, enhanced utilization of renewable energy sources, and added benefits for electric vehicle owners. To address the existing limitations in the charging–discharging decision-making process for electric vehicles based on V2G, such as the lack of consideration for charging pile constraints, EV profitability, EV transportation timeliness, and high costs associated with central servers, we proposed a reinforcement learning-based Multi-vehicle Joint Routing and Charging–Discharging Decision algorithm (MJRCDD). Firstly, the Markov decision process (MDP) was established to describe the problem, and the route selection and charging–discharging behavior of the vehicle were innovatively integrated in the vehicle action space. Secondly, the multi-vehicle joint route planning and charging–discharging decision problem was solved by multi-agent reinforcement learning. Finally, the effectiveness of MJRCDD was verified by simulation and comparison experiments based on PeMS. [ABSTRACT FROM AUTHOR]

The increasing occurrence of severe vulnerabilities, such as natural catastrophes and man-made attacks, has resulted in a corresponding rise in power outages on a global scale. Given the growing recognition of such exceptional occurrences, there is a pressing need to examine the matters pertaining to resilience and the mitigation of risks. This study presents a comprehensive overview of the current state-of-the-art in power system resiliency, as well as an exploration of the measures required to ensure a sustainable environment.These instances of measures include resilience by enabling localized generation and distribution of electricity,diversification of energy resources, withstanding of severe weather conditions, cyberattacks and enabling communities to proactively address the consequences of power outages. There are multiple approaches to bolstering resiliency, which aim to facilitate recovery from unforeseen circumstances and promote stability in the face of uncertain events. These measures also serve to mitigate the impact of unexpected incidents such as power outages. Integrating unpredictable renewable energy sources like solar and wind power into energy networks is difficult, especially in terms of resilience. Renewable energy output fluctuates owing to weather and time of day, requiring sophisticated grid management, energy storage, and demand-response mechanisms to maintain system balance and resilience. This study elucidates the enhanced principles of power system dependability and resilience, in addition to several ways for establishing a sustainable power ecosystem. It examines the complex dynamics of risk assessment, including equipment failures, natural disasters, and human errors, to determine their likelihood and implications. Moreover, the study thoroughly examines the critical moments that occur after accidents, emphasizing the need of prompt reaction and recovery measures in reducing downtime and restoring regular operations to impacted power networks. This involves determining the fundamental reasons behind the incidents, such as whether they arise from equipment malfunctions, human mistakes, external influences like natural calamities, or cyber assaults. In addition, the report examines the efficacy of current response protocols and emergency procedures in reducing the impact of accidents and restoring regular operations to impacted electrical systems. [ABSTRACT FROM AUTHOR]

The aim is to assess the impact of the civil sector's heat demand electrification on the entire energy system, in the Italian case study. The hourly heat demand profiles are estimated at census cells level using the BIN method or monitoring data. Profiles are used as input in the oemof-based NEMeSI model employed to optimize both the capacity expansion of power generation and the operation of the power system in 2030, with hourly temporal resolution and NUTS2 spatial detail, in three heat demand electrification scenarios. The model considers the availability of sources, the import and export profiles, and the limit on renewable sources. The results show that no additional capacity of renewable energy is driven by the increasing electrification in fact the installed capacity remains the same in the three scenarios (97GW of photovoltaic and 33GW of wind turbines). The increase of power demand results in a reduction of overgeneration (48.6TWh to 38.3TWh), an increase in installed batteries (40.6GWh to 115.3GWh) and in CHP (+10%) and CCPP systems (+16%). The results show a slight increase of natural gas in electricity generation (+5.2TWh) respect to a high reduction in its use in civil sector's heat demand (-68.5TWh). [ABSTRACT FROM AUTHOR]

As global energy demands increase, grid-connected photovoltaic systems are gaining popularity and acceptance as a viable and attractive alternative energy source. These systems face significant challenges in terms of power quality and maximising solar panel output under varying environmental conditions. This study aims to experimentally validate the effectiveness and stability of a single-stage grid-connected photovoltaic system and to evaluate the performance of the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) techniques in reaching the maximum power point under partial shading conditions. For experimental validation, dSPACE 1104 and LAUNCHXLF28379D were used. The results demonstrate that the system exhibits both robustness and stability, with the GWO algorithm outperforming PSO in terms of speed and accuracy in achieving the maximum power point, thus enhancing the system's efficiency under different operating conditions. [ABSTRACT FROM AUTHOR]

Faced with rapidly rising energy demand in industrialised societies and widespread global concern, countries are actively promoting the transition from conventional to renewable energy systems. The goal is to invest in renewable energy in the most efficient way to meet rising energy demand and reduce the challenges posed by climate change. However, decision makers must carefully weigh various factors when selecting the most appropriate renewable energy investment projects. This paper presents a novel method for Multi-Attribute Decision Making(MADM) that uses the Bipolar Complex Fuzzy(BCF) to convey the vagueness and uncertainty of decision makers, so that the result obtained better reflects the actual scenario and the subjective biases of decision makers. We defined BCF Einstein Weighted Averaging (BCFEWA) operator and BCF Einstein Ordered Weighted Averaging (BCFEOWA) operator to aggregate evaluation information. Then we discussed some properties of the proposed aggregation operators. Additionally, we present an integrated MADM technique grounded in the BCF framework that combines the CRiteria Importance Through Intercriteria Correlation (CRITIC) and ELECTRE III methods. Specifically, the CRITIC method determines attribute weights, and the ELECTRE III method ranking the alternatives to determine the best renewable energy investment projects. After analysing the results and comparisons, it can be inferred that the suggested methodology offers an effective evaluation process. [ABSTRACT FROM AUTHOR]

The growing importance of sustainable energy sources, driven by environmental concerns and energy security, has led to increased interest in the biomass sector's impact on economic growth. This study employs multiple linear regression analysis to examine how the biomass sector influences Romania's economic landscape. The aim of this research is to unravel the sector's dynamics, understanding its contribution to the national economy, and exploring its potential in advancing sustainable development objectives. A comprehensive literature review delves into past studies probing the relationship between the biomass sector and economic growth, with an emphasis on the Romanian context. Key economic indicators and variables are identified and included in the regression model. The empirical analysis is based on data obtained from various sources, allowing for a robust examination, and the results of the multiple linear regression analysis reveal significant insights into the dynamics of the biomass sector's impact on economic growth in Romania. Furthermore, potential confounding factors are considered, and their influence on the findings is discussed. The paper also includes a stakeholder analysis for using black pellets as the biomass option of choice in the energy sector. Contributing to existing knowledge, the research sheds light on Romania's biomass sector specific context, while discussing practical and policy implications, and offering guidance to stakeholders and policymakers in sustainable energy and economic development. Emphasising the pivotal role of biomass in economic growth, the study underscores the need for informed decision making and policy development to nurture this sector in Romania. [ABSTRACT FROM AUTHOR]

Countries are facing interconnected and cascading crises, including COVID-19, climate change, wars and military conflicts, and disruptions in geopolitics. To address these global challenges, it is necessary to introduce eco-innovations, and implement renewable energy. The purpose of the article is to analyze the countries' eco-innovative development, determine the impact of factors on alternative energy sources consumption, and consider models for eco-innovation network management. The dynamics of the Global Innovation Index, the European Innovation Scoreboard, and the Eco-Innovation Index were analyzed. The research implements the analysis of 27 EU countries for the period 2013-2020 by indicators that influence the eco-innovations development. The model of the impact of countries' investment development and economic growth indicators (such as foreign direct investment; GDP per capita), management level and the willingness of the government to invest in eco-innovation (total tax and contribution rate; government effectiveness; control of corruption; rule of law; research and development expenditure), and the level of income inequality (income share held by highest 10%; Gini index) on the consumption of alternative energy sources, energy-efficient technologies, and waste management and recycling implementation was built. The linear and cybernetic models for eco-innovation network management were considered. The pace of eco-innovation is strongly influenced by the effectiveness of state eco-innovation policies, the availability of a comprehensive information base, and the mechanisms of interaction between the science and production sectors. Effective eco-innovation networking implies that between the participants there are different types of relationships, the main of which are economic; legal; administrative; technological; social; and informational. [ABSTRACT FROM AUTHOR]

Copyright of DYNA - Ingeniería e Industria is the property of Publicaciones Dyna SL 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 article explores the concept of lifecycle thinking and its application in chemical engineering. Lifecycle thinking, supported by lifecycle assessment (LCA), offers a comprehensive framework for evaluating the potential impacts of products or processes throughout their lifecycle. The article emphasizes the importance of LCA in guiding future developments in chemical engineering and provides best practices for its use in different sectors. It also discusses the relationship between LCA and emissions reporting, as well as the emerging field of social lifecycle assessment (S-LCA) for quantifying social impacts. Overall, the article highlights the role of LCA in transitioning to a circular and net-zero-emissions economy. [Extracted from the article]

Energy demand is a critical contemporary concern, with significant implications for the future. While exploring renewable or sustainable energy sources offers potential solutions, optimizing energy consumption in existing power generation systems is also key. Aviation accounts for a substantial portion of energy demand, underscoring the importance of energy efficiency in this sector. Conventional energy analyses may be misleading; hence, employing exergy-based analyses provides a clearer understanding of energy consumption. Also, most of these analyses do not include the effect of the turbine blade's cooling in calculations. In the present study, exergy analyses have been conducted on a helicopter turboshaft engine with turbine-blades cooling, focusing on design parameters such as ambient temperature, compressor pressure ratio, and turbine inlet temperature. Thermodynamic optimizations are conducted using a genetic algorithm. Results show that increasing pressure ratio and turbine inlet temperature boost performance, yet technical restrictions on compressor and turbine size, and metallurgical constraints on turbine blades' material limit these gains. Sea level scenario prioritizes ambient temperature-drop for enhancing net-work and efficiency, while altitude-gain boosts turboshaft performance. Combustion chambers incur the highest exergy destruction of 74-80%, followed by 16-20% and 4-6%exergy destructions in the turbine and compressor, respectively. Lower air temperatures and higher flight altitudes demand larger fuel consumption for equivalent turbine inlet temperature, albeit enhancing cooling capacity and reducing required cooling air fraction for turbine blades. [ABSTRACT FROM AUTHOR]

As worries about climate change and energy security grow, the need for clean and sustainable energy sources becomes increasingly critical. Among the possible alternatives, solar thermal technology provides a dependable and adaptable method for harnessing the sun's energy as heat. At the core of this technique is the solar thermal absorber, a critical component that converts sunlight into useful thermal energy. The Graphene Based Disk-Square Integration Resonator Solar Absorber (GBDSIRSA) structure is examined in this work between 200 and 2500 nm in wavelength. The GBDSIRSA has remarkable performance throughout a wide variety of spectral ranges, underscoring its adaptability and effectiveness. GBDSIRSA has exceptional efficiency in absorbing light over the full spectrum, from UV to MIR wavelengths, with an average absorptance of 97.73%. The absorptance rates are particularly high, achieving 94.02% in the UV, 96.36% in the VIS, 97.12% in the NIR, and an astounding 98.94% in the mid-infrared (MIR) region. The GBDSIRSA is independent of polarization effect and also an incident angle independent up to 80 degrees. The machine learning (Local Regression) approach used with test size of 0.2 and 1.8780 ×10-5 mean squared error for absorptance prediction has good prediction accuracy R2 of more than 95% which is applied to simulation resource reduction. Because of its wide absorptance spectrum, polarization and incident angle independence, the GBDSIRSA is a very promising technology for a variety of applications, including thermal imaging and renewable energy. It can efficiently harvest solar energy. All things considered, GBDSIRSA's remarkable absorptance properties highlight its promise as a top choice for cutting-edge optical and solar energy applications. [ABSTRACT FROM AUTHOR]

Power generation systems using photovoltaic (PV) technology have become increasingly popular due to their high production efficiency. A partial shading defect is the most common defect in this system under the process of production, diminishing both the amount and quality of energy produced. This paper proposes an Artificial Neural Network and Golden Eagle Optimization based prediction of the fault and its detection in a standalone PV system to recover the optimum performance and diagnosis of the PV system. The proposed technique combines the Artificial Neural Network (ANN) and Golden Eagle Optimization (GEO) algorithm. The major contribution of this work is to raise PV systems' performance. The result is a defect in the classification and identification of an ANN is used. The use of GEO provides an efficient optimization technique for ANN training, which reduces the training time and improves the accuracy of the model. The proposed technique is executed on the MATLAB site and contrasted with different present techniques, like genetic algorithm (GA),Elephant Herding Optimization (EHO) and Particle Swarm Optimization (PSO). The findings displays that the proposed technique is more accurate and effective than the existing methodologies for detecting and diagnosing defects in PV systems. • A hybrid technique for predict fault in standalone PV to improve the performance of PV • The proposed method is the joint execution of ANN and GEO • The major contribution of this work is to improve the performance of PV systems • The use of GEO provides an efficient optimization technique for ANN training • The proposed method is more accurate and effective than existing techniques [ABSTRACT FROM AUTHOR]

The experimental investigation aimed to determine how the use of biodiesel derived from dill and cresson oil affected the performance of semi-industrial burners. Furthermore, an investigation will be conducted to assess the combustion properties of different blends of biodiesel, specifically B10, B20, B40, and B60. The study looks at biodiesel's chemical makeup, physical properties, and how it works in the system that moves it to the burner and the burner simulator's burning process. Biodiesel exhibits comparable qualities to conventional diesel oil, enabling the possibility of blending it to achieve the desired ratio. The results suggest that increasing the percentage of biodiesel leads to a reduction in flame distance and a rise in flame temperature. Furthermore, the complete combustion of the fuel is responsible for the brilliant and transparent flame. Additionally, using dill and Cresson fuels that come from biodiesel raises the average flame temperature by about 17% and 16.1%, respectively, compared to regular diesel fuel. [ABSTRACT FROM AUTHOR]

The paper presents the historical review of a series of indicators of energy poverty in Bulgaria, including the recently adopted official definition, and the existing and planned policy instruments to reduce energy poverty in the field of energy efficiency, heating appliances and systems, and renewable energy sources. Targeted heating support and temporary direct support in the form of targeted heating grants and compensation under Operation Support for Vulnerable Households -- SAFE are not analysed. The official definition of energy poverty is used to assess both the level and the effectiveness of the definition and its ability to reflect national specifics and trends, its potential shortcomings in the programme scope, targeted groups and results. [ABSTRACT FROM AUTHOR]

This study is a comparative analysis of Kenya and South Africa, the largest economies in Eastern and Southern Africa respectively, based on gross domestic product (GDP), energy use and carbon emissions. This study investigates the contribution of economic growth and renewable energy use on greenhouse carbon dioxide emissions in both country-level and group data, to observe their possible impact on environmental pollution. The present study addresses United Nations Sustainable Development Goal 13, to combat climate change and its impacts. To this end, this study is conducted in Kenya and South Africa using secondary data for the period 1990-2022. As an econometric strategy, we adopt the use of both panel and time series over the highlighted countries. The study employed an ARDL and PMG panel estimation approach to analyze the long-run relationship while Granger causality was conducted to confirm the short-run relationship between study variables. The empirical results show that economic growth and energy use significantly increase carbon emissions in South Africa, Kenya and aggregate data. Moreover, changes in industrial structure and urbanization have a mixed influence on carbon emissions in all models. Furthermore, short-run causality results point to a unidirectional relationship running from economic growth to carbon emissions in Kenya. In contrast, for South Africa, causality runs from carbon emissions to growth. In addition, for panel analysis, the study confirmed a bidirectional relationship. In conclusion, this comparative case study shows that countries with substantial growth in GDP and intensive energy use have high carbon emissions. Given these, the positive relationship poses a dilemma for Kenya and South Africa in their drive to achieve growth and at the same time manage environmental pollution. The empirical findings of this study imply that these countries should take all possible policy actions such as the massive investment and deployment of renewable energy, shifting gradually from non-renewable energy sources to renewable sources, a range of renewable energy sources, especially those that don't generate greenhouse gases, are needed, the use of climate finance to transition to clean energy, carbon tax policy and trading schemes to curtail growth in carbon emissions and environmental degradation. [ABSTRACT FROM AUTHOR]

Renewable energy aspires to boost decarbonisation and power the circular economy in accordance with the Green Deal objectives. The transition, however, faces many challenges and understanding the drivers behind it is essential. This paper analyses 27 European Union countries for the period 2008 - 2020 to identify economic and social determinants influencing renewable energy consumption. The results indicate that the population density, education, economic growth, digitalisation and research and development have a considerable positive impact. Urbanisation, on the other hand, appears to exert a negative influence. In addition, the empirical findings reveal a unidirectional causal relationship running from education, economic growth and energy poverty towards renewable energy consumption. This research provides useful insights into important drivers of renewable energy adoption which can facilitate or hinder the energy transition and achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

The use of renewable energy presents a viable alternative to fossil fuels. However, their intermittent nature does not allow for an immediate response to energy demand. Thus, it is necessary and beneficial to harness various renewable sources and integrate a storage system as an auxiliary source to mitigate this intermittency. The hybridization of energy sources requires efficient management of power flows to ensure the proper functioning of the overall system, regardless of changing weather conditions. In this paper, we propose an intelligent power management control for hybrid wind-solar-battery systems connected to micro-grids based on fuzzy logic. The proposed control approach addresses several specific challenges compared to conventional methods in the intelligent energy management of renewable hybrid systems. It effectively manages the uncertainties and nonlinearities inherent in weather variations, optimizes performance by dynamically adjusting the operations of energy sources and storage systems, and ensures efficient realtime utilization of available energy resources, thus providing greater flexibility and adaptability. Additionally, it enhances the stability and reliability of micro-grids by integrating more flexible and adaptive decision-making mechanisms. The simulation results using MatLab/Simulink demonstrate the significant advantage of this intelligent management lies in its ability to precisely control the state of charge of the battery across five distinct levels, which is not achievable using traditional management practices that rely solely on the maximum and minimum levels of the state of charge. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are an integral part of smart energy systems. Hybrid renewable energy sources necessitate a trustworthy control strategy for optimal coordination and utilization of these systems. Improving Microgrid DC MG is the primary goal of this study, which focuses on power management in bidirectional DC-DC converters through the local control unit. Problems brought on by changes in power production and interruptions in load are examined. A hierarchical control strategy addresses these issues with primary layer droop control. This study aims to manage energy by examining variations in photovoltaic systems, batteries, and supercapacitors. Energy management efficiency is defined by the design of the control circuits and converters. Diagrammed. Responsiveness and absence of overshoot/undershoot show that the system is effective and resilient in handling DC MG local control problems, regardless of the load or generation conditions. [ABSTRACT FROM AUTHOR]

The integration of renewable energy sources (RESs), is a workable way to meet operational demands and boosting industrial activities' efficiency, sustainability, reduce and environmental effect, in particular, in the field of pumping systems. This study looks into the effective dynamic integration of wind turbines and photovoltaic (PV) systems to drive an induction motor (IM) under varying environmental conditions. Results show that the integrated system's power output grows in direct proportion to the intensity of solar radiation and wind speed, which enables the IM to operate more efficiently and accelerate. The study's cutting-edge control systems, in particular, operate very well by utilising sliding mode control (SMC) to keep the rotor flux within safe operating limits, hence avoiding magnetic saturation and possible motor failures. The study also emphasises how well the reference speed adjustment (RSA) system works to stabilise the DC link voltage with fuzzy logic approaches, guaranteeing that the IM's generated power is used as efficiently as possible while avoiding undesired fluctuations. [ABSTRACT FROM AUTHOR]

The objective of this paper is to design a smart grid of an ordinary plant with two renewable resources (solar PV and wind turbine) plants. The burnout of fossil fuels globally has created a hasty need for alternative energy sources to meet current energy demands. To address this issue, a hybrid power system has been developed, which combines clean energy sources, such as solar PV and wind, with fossil fuel generators, power conditioning systems and energy storage systems. This hybrid system adduces higher efficiency, more flexibility in environmental and planning benefits compared to relying solely on diesel generators. However, employing solar and wind energy has the disadvantage that they are erratic and dependent on climatic and meteorological variations, which may not coincide with the timing of energy need. This not only has an influence on the system's functionality but also causes batteries to be disposed of too soon. The results illustrated the root mean square (r.m.s) power behaviour over a 24 hour each day in May, 2020 according to the weather climate in Mosul city. Finally, it has been confessed that the individual power generated either by solar PV or by wind turbine was enough to supply the load. [ABSTRACT FROM AUTHOR]

Smart renewable energy harvesting has been implemented from hybrid sources such as solar and wind. The wireless sensor node is created for monitoring surface water. In the intelligent building, electrical energy is harvested from the hybrid source of solar and wind energy. The source energy was selected for the harvesting process by using a fuzzy controller. In this proposed method, piezo-electric reverse electro-wetting on di-electric energy harvesting is proposed where constant DC voltage is generated by a rectifier. A DC-DC converter is designed to power up the remote read-out sensor. The produced charge is transformed by a charge amplifier with the proportion of output voltage that is delivered to the wireless receiver. The harvested DC voltage varies with the temperature and external environmental effect. In our work, we obtained 6 × 10−3 W/m² of voltage and this harvested energy is monitored using the Internet of Things (IoT) by the proposed EHOR (Energy Harvested Optimized Routing) algorithm. [ABSTRACT FROM AUTHOR]

In this paper, with the development of an intelligent power system idea, sustainable energy sources were increasingly deployed, including transmission and distribution systems networks. As a result, optimal use of cascaded H-bridge inverter topologies (MLIs) and power distribution operations is critical for long-term power generation. Traditionally, selective harmonics reduction models must be performed to achieve the optimal switching frequency of multilevel inverters. This research aims to determine the switching frequency for wind-incorporated multilevel inverters to reduce overall harmonic components used in grid applications. This research adds towards the best possible solution by employing multiple newly established adaptive optimization techniques: MNSGA-II and salp swarm. The well-known genetic algorithm and particle swarm optimization are used for the wind-tied multilevel inverters optimization issue. Sevenlevel, eleven-level, and fifteen-level MLIs were employed to reduce overall harmonic distortion. The reliability and convergence rate of simulated data with various modulation indices for seven-, eleven-, and fifteen-level MLIs are obtained and compared. Models are developed based on MATLAB Simulink and are used to validate quantitative measurements. [ABSTRACT FROM AUTHOR]

Nowadays, intelligent control methods play an important role in the advancement of technology and the human movement towards further evolution. The development of new frameworks for power generation and distribution systems by designing a microgrid structure with economic capabilities is one of these areas of progress. Therefore, this paper introduces a new practical method for controlling the frequency of provisional microgrid and is able to cover the following issues at the same time including (1) It considers the nonlinear model of provisional microgrid which has a hybrid structure (AC and DC) in addition to renewable energy sources; (2) Introduces a method for microgrid frequency control under different operational conditions that performs based on the brain emotional learning; (3) Ensures the operation and applicability of the control method for the provisional microgrid through implementation of FPGA for the first time; (4) Confirms the robustness of the proposed method under severe load changes and generation from renewable sources. So, the effects of wind turbines and solar energy are considered in the simulation scenario and under the influence of various changes in load and system uncertainties, the robustness and efficiency of the proposed method are well demonstrated. [ABSTRACT FROM AUTHOR]

This research investigates a polyvalent heat pump that simultaneously produces hot and cold water and uses natural refrigerants. The novelty of using a 48 V direct current compressor driven by solar photovoltaic (PV) energy and the staged retrofitting improvements to this heat pump are discussed. Empirical testing has indicated that using a suction line heat exchanger yields a 30% improvement in the heating Coefficient of Performance (COP). Rising ambient temperatures could positively and negatively impact its heating and cooling performance, respectively. In addition, an average COP of 3.8 was obtained after a one-hour simultaneous mode operation, with the average hot tank temperature rising from 32.7 to 58.9°C and the average cold tank temperature dropping from 18 to 14.4°C. Being capable of operating the polyvalent heat pump under renewable energy sources and utilizing all its generated heating and cooling energy contributes further to the electrification and decarbonization of residential buildings. [ABSTRACT FROM AUTHOR]

Exploring cellular responses necessitates studying real‐time metabolic pathway kinetics, considering the adaptable nature of cells. Glycolysis and glutaminolysis are interconnected pathways fundamental to driving cellular metabolism, generating both energy and essential biosynthetic molecules. While prior studies explored glycolysis tracking, this research focuses on monitoring the kinetics of the glutaminolysis pathway by evaluating the effect of glutamine availability on glycolytic kinetics and by investigating the impact of a stimulator (oligomycin) and inhibitor (2DG) on the glycolytic flux in the presence of glutamine. Additionally, we adapted a rate equation model to provide improved understanding of the pathway kinetics. The experimental and simulated results indicate a significant reduction in extracellular lactate production in the presence of glutamine, reflecting a shift from glycolysis towards oxidative phosphorylation, due to the additional contribution of glutamine to energy production through the ETC (electron transport chain), reducing the glycolytic load. Oligomycin, an ETC inhibitor, increases lactate production to the original glycolytic level, despite the presence of glutamine. Nevertheless, its mechanism is influenced by the presence of glutamine, as predicted by the model. Conversely, 2DG notably reduces lactate production, affirming its glycolytic origin. The gradual increase in lactate production under the influence of 2DG implies increased activation of glutaminolysis as an alternative energy source. The model also simulates the varying metabolic responses under varying carbon/modulator concentrations. In conclusion, the kinetic model described here contributes to the understanding of changes in intracellular metabolites and their interrelationships in a way which would be challenging to obtain solely through kinetic assays. [ABSTRACT FROM AUTHOR]

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Offshore energy generation is one of the fastest-growing U.S. marine sectors, ranging from expanding deployment of offshore wind to rapid development and testing of technologies for energy conversion from waves, tides, ocean currents, and gradients in temperature, salinity, and pressure. Generating energy from the offshore environment creates expanded opportunities to improve upon the collection of ocean and coastal-derived data and information that will support economic and maritime growth, the management of resources, and solutions to address societal and climate issues. Despite its many promises and challenges, barriers remain for widespread implementation of offshore energy generation technologies that address the nation’s energy needs and climate change. A town hall was convened to present a vision of the changing ocean-scape through the lens of marine renewable technologies and to explore innovative solutions that these technologies might offer to support the New Blue Economy. [ABSTRACT FROM AUTHOR]

Marine renewable energy (MRE) is increasingly of interest to coastal nations as a source of renewable energy that can support climate change mitigation goals as well as provide secure locally-produced energy for coastal and island communities. MRE extracts power from tidal streams, waves, ocean currents, run of rivers, and gradients in the ocean, with specialized devices developed and tested for each energy resource. Alongside development of MRE technologies and systems, first in Europe and then in North America, Australia, Asia, and other regions, it has been universally recognized that there is also a need to examine potential effects on marine animals, habitats, ecosystem processes, local communities and other sea users, to ensure that the MRE industry can be developed in a responsible and sustainable manner. This paper looks at the status of assessment and monitoring for potential environmental effects associated with MRE projects around the world. Over 80 projects were identified worldwide as having been tested, demonstrated, or commercially deployed with associated environmental monitoring. Five of the projects that represent tidal stream, wave, and run of river projects are examined in more detail to determine the types of data and information collected for those projects, the outputs of the monitoring campaigns, and the actions taken as a result of the data collection and analysis. Recommendations are provided for standardization of the monitoring approaches, instruments, and analysis methods at MRE project sites worldwide. [ABSTRACT FROM AUTHOR]

With increased focus on offshore wind (OSW) as a renewable energy resource in the United States and elsewhere, there are concerns about OSW impacts to wildlife, particularly birds and marine mammals. This study identifies technology gaps and technological research and development (R&D) priorities for monitoring marine mammals and birds for fixed and floating OSW. A synthesis of current monitoring technologies generated two databases (with over 100 technologies) that can be integrated in current technology repositories for renewable energy projects. Generally, the key technology R&D needs are similar for birds and marine mammals. The main exception is that some types of bird technologies are more likely to require direct integration with OSW infrastructure, whereas marine mammal systems tend to operate independently. Priorities to advance wildlife monitoring include improved early communication, harmonization of technologies and data collection for monitoring systems on OSW structures, battery/power access improvements, remote data transfer improvements, and advancements in automated collection and analysis of data. The successful integration of wildlife monitoring systems into OSW infrastructure and operations is dependent on remote access mechanisms for data collection, system maintenance, and data transfer, in order to minimize risks to worker safety in the offshore environment, as well as minimizing costs and disruption to normal operational activities. Application of the results of this study to prioritize and fund technology R&D will help to support statistically robust data collection and practicable integration of monitoring systems into OSW operations and infrastructure. [ABSTRACT FROM AUTHOR]

This study examines the potential of microgrids (MG), which utilize renewable energy sources to provide sustainable power solutions. To conduct the analysis, we examined load and photovoltaic (PV) data, calculated minimum and maximum averages, and visualized the correlation using big data tools. We cleaned the data by removing unnecessary rows, merged the tables, converted them into CSV format, and uploaded them to the Databricks file distribution system (DBFS). Subsequently, we processed the data by creating a pipeline and using ETL (extract, transform, load) processes. We analyzed and visualized the data using tools such as Power BI and Tableau. The analysis identified the maximum and minimum PV production, assessed the impact of weather patterns on production, and measured the energy shortage between load demand and PV generation. Our research demonstrates the steps involved in handling and analyzing data, uploading it to the Hadoop ecosystem, transforming it into different file formats, connecting it to a relational database management system (RDBMS), and visualizing it using BI tools. In this study, we utilized cloud infrastructure to perform analytical tasks, including the use of business intelligence (BI) tools. [ABSTRACT FROM AUTHOR]

Renewable energy is a promising solution to combat the scarcity of electricity, particularly in isolated and rural areas. Microgrids (MG) can be employed for installing systems with different energy sources, such as renewable energy components and conventional energy sources like utility grids or grid-connected inverter systems. The amount of energy produced by renewable sources depends on their location, which has implications for energy production. This research aims to explore MG and their challenges for efficient operation. The study discusses various AI models used by researchers to mitigate problems associated with MG planning. Additionally, the paper presents a case study based on the most beneficial ML tool like clustering to gain insights into an existing MG system. The paper also delves into the issues related to PV, a connected distributed energy resource (DER), such as forecasting, and predictive management to reduce maintenance costs, and how AI tools can address them. Furthermore, forecasting methods such as LSTM and GRU models are discussed because of the stochastic nature of PV production. [ABSTRACT FROM AUTHOR]

This paper introduces a modification of the genetic algorithm aimed at enhancing the selection process for reproducing the next generation. This modification accelerates the optimization process and improves the outcome. The case study analyzes a grid-connected microgrid comprising renewable energy sources, a battery storage system, prosumers with installed photovoltaic generators, and consumers. The effectiveness of the proposed modification is validated through comparison with two selection algorithms commonly used in the standard genetic algorithms. [ABSTRACT FROM AUTHOR]

This paper compares different maximum power point tracking (MPPT) control strategies in microgrid dynamics, focussing on perturb and observe (P&O), adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimisation (PSO), and PSO sliding mode controller techniques. The study investigates their performance under varying microgrid conditions, considering factors like weather and load variations. The simulation results provide a detailed comparative analysis of the power at the point of common coupling (PCC) for MPPT techniques at different time intervals. Both the P&O and PSO sliding mode recorded a power output of 287 kW, while PSO-ANFIS achieved a slightly higher power output of 294 kW. At 2.5 seconds, the P&O method recorded a power output of 712 kW, while the PSO sliding mode and the PSO-ANFIS techniques achieved 717 kW and 738 kW, respectively. Overall, the PSO-ANFIS technique consistently outperformed the other methods in terms of power output, demonstrating its effectiveness in maximising energy extraction and adaptability to dynamic conditions. These findings provide valuable insights for designing and implementing MPPT controllers in microgrid systems, emphasising the efficiency of the hybrid PSO-ANFIS technique in enhancing the overall performance and stability of renewable energy systems. [ABSTRACT FROM AUTHOR]

This paper introduces a reinforcement learningbased controller, utilising the deep deterministic policy gradient (DDPG) method, to mitigate low-frequency disturbances in electrical grids with renewable energy sources. It features a novel reward function inversely related to the control error and employs a state vector comprising absolute and integral errors to enhance error reduction. The controller, tested on a dualregion system with solar power, utilises phasor measurement unit (PMU) data for global inputs. Its performance is validated through time-domain simulations, pole-zero mapping, modal analysis, frequency response, and participation factor mapping, using a custom MATLAB and Simulink toolkit. The design accounts for communication delays and adapts to variable conditions, which proves to be effective in reducing oscillations and improving system stability. [ABSTRACT FROM AUTHOR]

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method. [ABSTRACT FROM AUTHOR]

While there are some hydrogen refueling stations (HRS) functioning in different parts of the world, their use is not widespread enough, primarily due to their expensive cost. Hydrogen compression is a main contributor to the capital and operation costs of the HRS. By improving H 2 compression technology, it is possible to optimize the infrastructure for refueling with hydrogen in terms of cost and performance. The use of metal hydride hydrogen compressors (MHHCs), which have the potential to be inexpensive and have the ability to use waste heat and renewable energy sources for the H 2 compression, is a promising solution to overcome this issue. The duration of the H 2 compression cycle is nevertheless a serious challenge due to the metal hydride (MH) bed's low heat conductivity. As a heating technique to improve the performance of MHHCs, electromagnetic induction (EMI) is examined numerically for the first time in this work. The dynamic behavior of a two-stage MHHC with each MH reactor having an external heat exchanger and being ringed by a copper coil traversed by an alternating current is described by a two-dimensional mathematical model, which was established and successfully verified by the reference data. Numerical simulations were performed with the help of this model, and the findings showed that the EMI heating method is faster than the heat transfer fluid (HTF) heating technique. For instance, at a delivery temperature of 373 K and a supply pressure of 20 bar, it is possible to produce 106 NL H 2 per kilogram of HPMH at a pressure of 97 bar with a 74 % shorter compression time than with the HTF heating technique. [ABSTRACT FROM AUTHOR]

With the escalation of global warming primarily attributed to fossil fuel and other non-renewable energy consumption, the production of green hydrogen emerges as a mitigation strategy to reduce fossil fuel usage and effectively harness renewable energy sources for energy storage. The proton exchange membrane water electrolyzer (PEMWE) stands out as a promising technology, boasting high efficiency and a rapid response to variations in current density. Despite its stellar performance, the reliance on precious materials presents a cost challenge. To address this concern, we developed a numerical model considering mass transport limitations and temperature variation. The topology optimization (TO) method is employed to generate the optimal structure of the electrode by organizing the two primary constituent materials. Additionally, the impact of optimization points representing low (1.73 V) and high (2.03 V) operating voltage characteristics is analyzed. The optimal structure demonstrates a maximum performance improvement of up to 2.7 times at an operating voltage of 2.03 V compared to the homogeneous electrode structure. The gas coverage model influences the rearrangement of constituent materials, particularly the void fraction, creating channels to facilitate the reaction. Optimization at high voltage points yields a more significant improvement compared to the low voltage scenario. [ABSTRACT FROM AUTHOR]

Smart grids are two-way communications grids that converge Information Technology (IT) and Operational Technology (OT) to transfer energy-related information between different industry components within the grid. Smart grids have changed the energy sector by increasing sustainability, efficiency and integrating renewable energy sources. However, smart grids are vulnerable to IT-related attacks because they rely on Information and Communication Technology (ICT). By surveying relevant papers and evaluating accessible statistics, this study explores cybersecurity in smart grids by examining current communication protocols and standards. We carefully compile various datasets with general information about four of the most smart grid-related datasets. Our study and conclusions address the key components of a smart grid and offer information that can help create cybersecurity plans specifically for smart grids. This research contributes to the discourse on smart grid security, which is important for preserving the stability of contemporary energy systems. [ABSTRACT FROM AUTHOR]

The present study uses K-means++ clustering to assess the financial performance patterns of renewable or clean energy companies using the Return on Equity (ROE) decomposition. It also explores the relationship between ESG and financial performance and discovers a positive relationship between ROE and ESG. The ESG-ROE relationship becomes negative or insignificant when businesses are grouped based on ROE change. The findings suggest that firm-specific heterogeneity affects the ESG-ROE relationship and long-term financial performance. Hence, it emphasizes the significance of the internal and external environment in determining firms' ESG and financial success. [ABSTRACT FROM AUTHOR]