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Ample interest for more efficient utilization of bio-based residues has emerged in the Nordic pulp and paper (P&P) industry, which uses virgin wood as feedstock. Although different bioenergy retrofit technologies for production of liquid, solid, and gaseous bioenergy products have been applied in the existing P&P mills, the number of installations remains small. The lack of profound knowledge of existing bioenergy retrofits hinders the replication and market uptake of potential technologies. This review synthesises the existing knowledge of European installations and identifies the key drivers and barriers for implementation to foster the market uptake of potential technologies. The bioenergy retrofits were reviewed in terms of technical maturity, drivers, barriers and market potential. Based on this evaluation, common drivers and barriers towards wider market uptake were outlined from political, economic, social, technical, environmental, and legal perspective. Technologies already commercially applied include anaerobic fermentation of sludge, bark gasification, tall oil diesel and bioethanol production, whereas lignin extraction, biomethanol production, hydrothermal liquefaction and hydrothermal carbonization are being demonstrated or first applications are under construction. The findings of this review show that a stable flow of residues at P&P mills creates a solid base for retrofitting. New innovative bio-based products would allow widening the companies' product portfolios and creating new businesses. Also, European Union's (EU) legislation drives towards advanced biofuels production. Wider uptake of the retrofitting technologies requires overcoming the barriers related to uncertainty of economic feasibility and unestablished markets for new products rather than technical immaturity. • Bioenergy retrofits can widen the product portfolio of the pulp and paper industry. • Bioenergy retrofits in Europe were comprehensively reviewed for the first time. • Drivers and barriers for the market uptake were assessed from different perspectives. • Retrofits support the REDII targets for advanced biofuels in transport sector. • Utilization of residues for bioenergy products improves mills' resource efficiency. [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]

Vanadium Flow Batteries (VFBs) are a stationary energy storage technology, that can play a pivotal role in the integration of renewable sources into the electrical grid, thanks to unique advantages like power and energy independent sizing, no risk of explosion or fire and extremely long operating life. The first part of this paper presents the main features and the basic performance parameters of VFB, which determine their electric, hydraulic, thermal, and aging feature. The latter part outlines the strengths and weaknesses of the technology, the services that it can provide to the grid, and a short economic analysis. After presenting the fundaments of the technology, prospects and trends of VFBs deployment are outlined. Most of the considerations highlighted in this paper are inspired to studies performed on an industrial-size VFB operated at the Electrochemical Energy Storage and Conversion Lab (EESCoLab) at the University of Padua (Italy). [ABSTRACT FROM AUTHOR]

This article presents a state-of-the-art review of the status, development, and prospects of DC-based microgrids. In recent years, researchers' focus has shifted to DC-based microgrids as a better and more feasible solution for meeting local loads at the consumer level while complementing a given power system's reliability, stability, and controllability. DC microgrid has an advantage in terms of compatibility with renewable energy systems (RESs), energy storage, modern electrical appliances, high efficiency, and reliability. However, the integration of different distributed generations has complicated the control of bus voltage and current. Therefore, several efforts have been made in the research community to further explore efficient control techniques for a reliable and stable DC microgrid. In spite of the numerous review papers published on DC microgrid control, so far, not any has given sufficient emphasis on the power flow analysis methods used in various DC microgrid topologies and the hybrid control topologies discussed in this review. In general, this paper presents a meticulous explanation of DC microgrid architecture; power flow analysis; control strategies with comparative analysis; challenges with recommendations; as well as classical and intelligent-based energy management strategy (EMS). Finally, suggestion for further research is presented. This review paper will go a long way in helping readers to understand the present state of development on DC microgrid control. [ABSTRACT FROM AUTHOR]

• Straight-forward, in-expensive birchwood hydrolysis. • Low requirement of reagents. • Increased rate of biogas production. The global demand for non-fossil energy sources is increasing rapidly. As a result, biogas presents a suitable alternative; however, first generation biofuels (e.g., sugar cane) potentially impact food crops globally. Second generation biofuels based on lignocellulose-based biomass are being used more frequently as they do not impact food crops. Furthermore, in Northern Europe, there is a significant interest in utilizing birchwood and paper mill waste for biogas production due to its high availability. The utilization of birchwood for biogas has significantly improved in recent years with the improvement of required pretreatment processes. To date, the most effective and economically feasible pretreatment in an industrial context is the steam explosion of lignocellulose-based biomass. Despite this, there is potential for releasing more digestible components from this biomass by efficiently degrading the lignocellulose components. This research presents another pretreatment that can be applied to steam-exploded wood based on ultrasonication and Fenton reagents. It was observed that by treating the steam exploded birchwood with ultrasonication and mild concentrations of Fenton reagents, an increase in the rate of biogas production was achievable. This would allow the increase in biogas yield of a continuously feed industrial anaerobic digester without increasing the size of the reactor. [ABSTRACT FROM AUTHOR]

This paper deals with a dynamical analysis of a supercapacitor based multilevel inverter. In Fact, applications for multi-level inverters based on supercapacitors include uninterruptible power supplies (UPS), grid-tied renewable energy systems, electric vehicles, and other power conversion systems where high-power quality and rapid energy transfer are important. These systems are the subject of ongoing research and development in the field of power electronics and energy storage, as they have the potential to enhance the performance and efficiency of various electrical and electronic applications. However, If the input voltage sources are fixed, this can limit the flexibility of the inverter, especially in terms of regulating the output voltage and adapting to load variations. This study is focused on the dynamic analyses which is crucial for designing, optimizing, and verifying the performance of supercapacitor-based multi-level inverters in various applications. Hence, the behaviour of the system was studied on a resistive load, material calculations showing the functioning of the circuit as well as simulations were performed for the positive phase of the sinusoid at the terminals of the charge. [ABSTRACT FROM AUTHOR]

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development. [ABSTRACT FROM AUTHOR]

• Classified sales channels for different quality renewable energy. • Dynamic assessment indicators for the renewable energy's power deviation. • Power curve optimization strategy considers the self-owned battery. • Accurately and timely transmit the peak shaving pressure. • Guarantee the independent decision-making power of new energy stations. By the end of 2023, the installed capacity of renewable energy (RE) in China accounted for 36.0% of the total installed capacity, while the installed capacity of conventional thermal power units had decreased from 64.0% in 2016 to 47.6% in 2023. RE accounts for a higher proportion, leading to a shortage of peak shaving resources (PSR). As a result, the current RE quota system becomes unsustainable. In this paper, RE stations can classify their electricity into high-quality and low-quality parts based on the accuracy of their power curves. High-quality RE will be fully purchased and under stricter assessment, while low-quality RE will compete for clearing in the intraday spot market. Firstly, the article estimates the degree of PSR shortages in different periods based on technical parameters of thermal units, RE forecast information, and load forecast information. Consequently, the PSR supply capacity (PSR supply capacity) can be divided into PSR shortage status and sufficient status. Secondly, a clearing method for low-quality RE in the spot market is designed. The grid can set different PSR supply capacitys for different time periods according to the degree of PSR shortage. The assessment indicator values vary under different PSR supply capacitys. Lastly, based on the classification sales channels, this paper proposes a power curve optimization strategy that considers self-owned battery energy storage (BES). Case studies show that the proposed classification sales channels can effectively decrease the degree of PSR shortage while taking into account the benefits of RE station. The designed PSR supply capacity dividing method can accurately reflect the degree of PSR shortage in real-time, and the proposed power curve optimization strategy can further enhance the economic benefits of RE station. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

The study presents a novel integrated three-compartment electrochemical continuous electrodeionization reactor that was constructed in a laboratory environment. In order to extract large amounts of carbon dioxide from naturally occurring oceanwater in the form of bicarbonate and carbonate, it develops a innovative electrolytic cation exchange process. At the same time, it produces hydrogen gas for possible hydrocarbon synthesis. The study employs the Box-Behnken experimental design approach implemented through the Design Expert software to investigate and optimize the performance of electrochemical hydrogen extraction from Ocean water from an integrated reactor. The integrated electrochemical continuous-type electrodeionization reactor comprises two cation-permeable membranes that operate as boundaries between three compartments: a central compartment and electrode compartments that house the cathode and anode, each of which may reverse polarity. These membranes are made of gel polystyrene cross-linked with divinylbenzene, which has acidic properties that allow cation transport while inhibiting anions. The integrated reactor's electrode chambers are enclosed in plexiglass end plates and contain distinct Plexiglass electrode compartments. These compartments have mesh and solid steel anode and cathode electrodes, with an electrode active area of 176.71 cm2. The influence of various factors, such as voltage and electrolyte amount, on the production of hydrogen synthesis is examined. The study results demonstrate a maximum hydrogen production rate of 2.2 mg/min and a hydrogen production efficiency of 7.82%. The lab testing was carried out using experimental equipment to determine viability. The tests included 35-min runs that measured hydrogen generation under various parameters, such as applied voltage, electrolyte concentration, and pH levels. The ocean salt solutions of 0.5M, 1M, and 2M were initially prepared and evaluated for hydrogen generation. Each concentration is assessed at a voltage range of 4–15V, with matching solution pH values ranging from 2 to 7. Optimization studies were conducted to enhance the hydrogen production rate. The results indicate that hydrogen production rises proportionally with applied voltage and electrolyte concentration but falls with pH growth. Within the Design Expert software, the statistical methods for response surface analysis and optimization are utilized accordingly. • The paper develops an integrated three-compartment system for hydrogen production. • The paper investigates the system overall efficiencies for assessment. • It discusses how varying voltage and concentration affects hydrogen production. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emission is creating a distressing situation and requires substantial solutions. • Economic complexity and FDI reduced carbon emissions. • Green technology and Renewable energy correlated negatively with carbon emissions. • Positive shocks in ECI, FDI, GRT, and REU reduce COE. • Negative shocks in GRT and REU can lead to increased pollution in the long run. All economies are concerned about rising carbon emissions, which contribute to environmental degradation. The current paper formulates a novel framework to scrutinize the impacts of shocks in economic complexity, FDI, environmental technology, and renewable energy on carbon emission in the leading clean energy investment countries, spanning the period from 1995 to 2020. In spite of the constraint for better environmental defence and the realization of the Sustainable Development Goals (SDGs), this paper introduces an empirical approach utilizing the Panel NARDL methodology to investigate the asymmetrical connections between carbon emissions and relevant exogenous factors. Furthermore, we utilize additional techniques, namely AMG and CCEMG, to enhance the robustness of our findings. Our empirical findings reveal that positive shocks in economic complexity, FDI, environmental technology, and renewable energy reduce carbon emissions while negative shocks may result to elevated pollution levels in the long-run. However, adverse shocks in economic complexity and FDI cause increased pollution in the long run. Likewise, the short-run coefficient signs are also similar to the long-run coefficient signs but different in significance level and magnitude. This has paved the way for a well-designed policy for leading clean-energy investment countries should focus on structural change, FDI, technology and renewable energy consumption. [ABSTRACT FROM AUTHOR]

Energy storage is one of the hot points of research in electrical power engineering as it is essential in power systems. It can improve power system stability, shorten energy generation environmental influence, enhance system efficiency, and also raise renewable energy source penetrations. This paper presents a comprehensive review of the most popular energy storage systems including electrical energy storage systems, electrochemical energy storage systems, mechanical energy storage systems, thermal energy storage systems, and chemical energy storage systems. More than 350 recognized published papers are handled to achieve this goal, and only 272 selected papers are introduced in this work. A comparison between each form of energy storage systems based on capacity, lifetime, capital cost, strength, weakness, and use in renewable energy systems is presented in a tabular form. Selected studies concerned with each type of energy storage system have been discussed considering challenges, energy storage devices, limitations, contribution, and the objective of each study. The integration between hybrid energy storage systems is also presented taking into account the most popular types. Hybrid energy storage system challenges and solutions introduced by published research are summarized and analyzed. A selection criteria for energy storage systems is presented to support the decision-makers in selecting the most appropriate energy storage device for their application. For enormous scale power and highly energetic storage applications, such as bulk energy, auxiliary, and transmission infrastructure services, pumped hydro storage and compressed air energy storage are currently suitable. Battery, flywheel energy storage, super capacitor, and superconducting magnetic energy storage are technically feasible for use in distribution networks. With an energy density of 620 kWh/m3, Li-ion batteries appear to be highly capable technologies for enhanced energy storage implementation in the built environment. Nonetheless, lead-acid batteries continue to offer the finest balance between price and performance because Li-ion batteries are still somewhat costly. The applications of energy storage systems have been reviewed in the last section of this paper including general applications, energy utility applications, renewable energy utilization, buildings and communities, and transportation. Finally, recent developments in energy storage systems and some associated research avenues have been discussed. Academics and engineers interested in energy storage strategies might refer to this review. • The complexity of the review is based on the analysis of 250+ Information resources. • Various types of energy storage systems are included in the review. • Technical solutions are associated with process challenges, such as the integration of energy storage systems. • Various application domains are considered. [ABSTRACT FROM AUTHOR]

With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

• Pioneer Spain: first country to include in grid code the synthetic inertia capability. • Synthetic inertial response of a PV inverter studied based on the Spanish grid code. • Analyzed the role of power plant controller in the dynamic response of the facility. • PV system able to contribute to frequency control providing an inertial response. As the large-scale integration of renewable energy grows, inertia in power systems is reduced, which causes the rate of change of frequency to increase and worsens the frequency nadir during disturbances. There is, therefore, an urgent need to accelerate the development of international standards and regulations that require solar PV systems and other renewable installations to also contribute to stability in the network by emulating inertia. Spain is a pioneer in this field: it is currently the only country that includes the synthetic inertia technical requirement in its grid code. In this context, the present paper analyzes the synthetic inertial response of a PV system model that forms part of an actual PV power plant in Spain under two scenarios, according to its grid code: i) when measurements are conducted at the power conversion PV system level and hence only its control algorithms are executed; and ii) when measurements are conducted at the power plant level and the power plant controller also influences the response of the entire installation. Among its aims, this paper seeks to thoroughly assess the response of an actual PV power conversion system that has the capability of actively contributing to frequency regulation in a network and to critically analyze the suitability of having a power plant controller when the PV facility contributes to power-frequency control by means of an inertial response. The results reveal, at the power conversion PV system level, that the inertia emulation module significantly reduces the response times of the installation to changes in frequency -by more than 68% in the worst case under frequency increases, and by 73% under frequency decreases, thus contributing to more rapid power-frequency regulation in the network. However, at the power plant level, the power plant controller disturbs the PV power conversion system's behavior and causes the opposite effect: response times increase -up to more than 7% in the most unfavorable case when the inertia module is enabled. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling and forecasting tasks. While deep learning excels in capturing intricate patterns in data, it may falter in achieving optimality due to the nonlinear nature of energy data. Conversely, meta-heuristic algorithms offer optimization capabilities but suffer from computational burdens, especially with high-dimensional data. This paper provides a comprehensive review spanning 2018 to 2023, examining the integration of meta-heuristic algorithms within deep learning frameworks for energy applications. We analyze state-of-the-art techniques, innovations, and recent advancements, identifying open research challenges. Additionally, we propose a novel framework that seamlessly merges meta-heuristic algorithms into deep learning paradigms, aiming to enhance performance and efficiency in addressing energy-related problems. The contributions of the paper include: 1. Overview of recent advancements in MHs, DL, and integration. 2. Coverage of trends from 2018 to 2023. 3. Introduction of Alpha metric for performance evaluation. 4. Innovative framework harmonizing MHs with DL for energy problems. • Overview of recent advancements in Meta-Heuristics, Deep Learning in energy applications. • Covers 2018-2023 trends, providing insights into recent innovations in energy technologies. • Introduces Alpha metric for evaluating energy tech adoption and performance globally. • Proposes a framework integrating Meta-Heuristics and Deep Learning for energy solutions. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. This approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time. • RTHS is extended to offshore wind turbine monopile structures. • OpenFAST software is modified and integrated into a RTHS framework. • Validated RTHS framework that incorporates the complete system into the simulation. • RTHS enables experimental assessment of offshore wind turbine foundation behavior. • RTHS computational challenges of offshore wind turbine structures are presented. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

The crux to meet the daily exponential growth in power demand and reduce the carbon footprint has seen to rejuvenate the consideration of large-scale renewable energy penetration (LsREP) in the power grid. Conversely, the emergence of the LsREP in the power grid is linked to frequency instability due to the mismatch between the conventional-based synchronous generator and inverter-based generator. Furthermore, the drastic drop in the kinetic energy storage due to the minimal rotating mass resulted in low inertia. This study presents a detailed work evaluating the effectiveness of different low-inertia power grid control techniques such as adaptive, predictive, virtual, and conventional controls. Furthermore, the study revealed gaps in this research area and thus, recommends multi-objective optimization control strategies to accommodate extant grid's dynamism for a better representation and control of low-inertia grid in the presence of perturbations. In addition, the bibliometric analysis was optimized through the VOSviewer -bibliographic software. The analyzed data were extracted from the Scopus database for the period of 2013 to mid-2023 covering 1267 related documents from conference papers, conference reviews, articles, and reviews on low-inertia grid control strategies. The results from the analysis revealed six (6) clusters of new research areas based on the co-occurrence keywords from the assessed papers. • The study classified the low-inertia grid control into 4 categories viz. Adaptive, Predictive, Virtual, and Conventional. • The strengths and limitations of each control classification in the presence of load perturbations were well discussed. • The bibliometric analysis via the VOSviewer revealed top countries with scholarly publications on low-inertia grid control. • The research recommends multi-objective dynamic control strategies for a better control of low-inertia power grids. [ABSTRACT FROM AUTHOR]

Currently, the research agenda of energy transition is gaining momentum. This paper conducts a comprehensive review of this body of research by presenting a framework that describes the connotations, mechanisms, and effects of energy transitions. The study concludes that the energy transition will lead to the reconstruction of energy system elements beyond the energy sector, with the dual connotations of explicit transition and implicit transition. The explicit transition is usually captured by statistic data and information, for instance, energy utilization type, structure, form, transportation mode, and spatial pattern. The implicit transition involves shifts in energy security, geopolitical structure, energy power, energy justice, and energy governance, which receives relatively less attention in current literature. The energy transition is a highly socialized process, driven by many intertwined factors such as technological innovation, market mechanisms, policy arrangements, and sociocultural factors, triggering profound socio-economic and ecological effects. Looking ahead, it is urgent to pay attention to the multi-scale effects of energy transition, avoid the risks brought by energy transition, and ultimately achieve just energy transition. This paper provides a novel perspective and contributes to the literature on theoretical understanding and advancement of energy transition. • This paper proposes an analytical framework to enrich energy transition research. • Energy transition has dual connotations of explicit transition and implicit transition. • Energy transition is a highly socialized process driven by a multitude of factors. • Energy transition brings profound socio-economic and ecological effects. [ABSTRACT FROM AUTHOR]

In the current global landscape, food loss and waste (FLW) persist as a significant challenge, accounting for one-third of global food production and directly impacting 700 million individuals. Beyond its social ramifications, FLW incurs substantial economic costs and raises environmental and ethical concerns. To address this issue, our paper presents a comprehensive systematic overview within operations management, offering a framework that delineates diverse supply chain types, defines FLW, examines its multifaceted impacts, and outlines reduction strategies. Emphasizing a holistic perspective, we utilize various case study approaches to shed light on the global implications of FLW, aiming to contribute to ongoing research endeavors. In exploring the depth of FLW, we build upon two foundational premises: the actions taken by rational actors towards FLW reduction hinge on the balance between benefits and costs, guiding policy formulation. Effective policies necessitate a thorough understanding of synergies, potential unintended consequences and trade-offs. The absence of comprehensive information on FLW leads to both excessive and insufficient rates of FLW. Justifying public intervention lies in its potential to enhance food security, efficiency, and environmental sustainability, albeit quantifying these effects poses challenges. Our overview paper identifies key challenges in FLW policies, encompassing measurement, quantification, interdependencies within the and trade-offs, and value chain in food policy objectives. Despite these challenges, the paper guides the enhancement of loss measurement for targeted interventions, emphasizing investments in policies that foster a conducive business environment and government-led initiatives in awareness and incentive modification. The study also sheds light on crucial gaps in knowledge, including data deficiencies and variability in estimates, calling for standardized data collection methodologies and targeted research initiatives. By addressing these gaps and emphasizing collaborative efforts, this research contributes to a nuanced understanding of FLW, advocating for improved strategies to tackle this multifaceted issue. • Comprehensive Ops Framework: Presents systematic framework for FLW reduction in supply chains. • Multifaceted FLW Impacts: Explores economic, environmental, and social impacts. • Urgent Data Gaps: Emphasizes need for current data on FLW, especially in BRICS nations. • Culture-Specific Solutions: Highlights importance of tailored innovations for waste reduction. • Climate Nexus and Renewable Energy Integration: Explores FLW, climate change, and renewable energy synergy for sustainable, low-carbon paths. [ABSTRACT FROM AUTHOR]

The present paper reviews the green hydrogen uptake policies in the United States and the European Union. While the EU has a rather broad public support strategy that envisages the entire hydrogen value chain, the U.S. policies are more focused on hydrogen research hubs and subsidies having electric vehicle applications. From the experience of the solar and wind power rollout of the past three decades, we know that the subsidization schemes of the end product play an important role in the transition to renewables. These kinds of subsidization schemes are still lacking for clean hydrogen both in the EU and the U.S. This paper provides a brief overview of selected subsidization schemes from other renewables markets, which are then applied to define an exemplary subsidization path to empower large-scale growth in a market for green hydrogen. Here is proposed a long-term subsidization scheme – deemed as an adequate strategy for the expansion of clean hydrogen in an electricity mix. The suggested exemplary subsidization path recommends shifting from feed-in tariffs to direct marketing with market premiums once a certain threshold is reached, while both can be flanked by the issuance of a Guarantee of Origin for green hydrogen so that there is one market for hydrogen and another market for the certificates. • -A long-term subsidization scheme for the rollout of green hydrogen in the electricity mixes is proposed. • -An exemplary subsidization path to empower large-scale growth in a market for green hydrogen is drafted. • -U.S. and EU are used as case studies to capture lessons for subsidization of green hydrogen economy uptake. • -The U.S. has more centralized green hydrogen rollout policies, whereas the EU has a wider policy toolkit. • -Shifting from FIT to direct marketing with market premiums is key once a certain pre-defined threshold is reached. [ABSTRACT FROM AUTHOR]

• A stochastic coordinated generation and transmission expansion planning model is proposed. • The planning model takes into account the impact of demand response programs and renewable energy development. • The detailed demand response model with operating characteristics such as power, energy, reserve, response period, and response number is established. • The chance-constrained method is introduced into the planning model to flexibly control the participation level of DR programs. The integration of high-penetration renewable energy has led to a significant increase in the uncertainties of power systems, and coordinated generation and transmission expansion planning has attracted more and more attention. Demand response resources are regarded as a kind of virtual power supply, which can improve the flexibility of power systems and delay generation and transmission investment to a certain extent. This paper establishes a stochastic coordinated generation and transmission expansion planning model taking into account the impact of demand response programs. In particular, the demand response model describes the operating characteristics such as power, energy, reserve, response period, and response number in detail. Considering the difference in the degree of invocation of demand response programs by different power systems, the chance-constrained method is adopted, which can make the planning schemes flexibly adapt to different demand response levels and improve the practicability of the model. In the case study, numerical results of construction and operation indicate that demand response programs can play a role in replacing generation and transmission capacity. Specifically, although the response energy does not exceed 0.3% of the total load energy, the investment cost can be saved up to more than 20%. Moreover, the response level, flexibility parameters, response capacity, and the proportion of renewable energy will all affect the capacity substitution effect of demand response, which verifies the rationality and necessity of considering these factors in this paper. [ABSTRACT FROM AUTHOR]

Periodic cleaning of photovoltaic (PV) panels, such as every three months, is a common industry practice. However, this fixed period may not be optimal for maximizing the profit of a PV power generation system, due to numerous time-variant influencing factors, such as weather, temperature, etc. To increase the overall profit of a solar farm, it is highly desirable to have a flexible cleaning schedule that considers time-variant influencing factors. For this requirement, a rolling-horizon cleaning recommendation system is presented in this paper. Within this cleaning recommendation system, a prediction model and profit model are proposed. The prediction model, called the ensemble long-term and nonlinear autoregressive, can provide a time-variant future horizon by analyzing and compressing the time-variant characteristics in historical information. The profit model based on mathematical constraints, can process time-variant future horizon output from prediction model to generate a flexible optimized recommendation for cleaning schedule. The effectiveness of the proposed system is validated in real farms and all data used in this paper is collected from real world. The two case studies in experiments show that the profit improvement can reach up to 6% and 30%, respectively. [Display omitted] • A rolling-horizon cleaning recommendation system for PV panels. • A novel long-term and nonlinear predictive method. • A concise profit model for easy deployment in industry. • Flexible cleaning schedule considering time-variant characteristics. • Improvements of 6% and 30% in two different real farms, respectively. [ABSTRACT FROM AUTHOR]

• A new Free-Drift Solar Power Satellite concept is proposed. • Large amount of fuel is saved because of the free-drift orbit and attitude. • Symmetric layout is adopted to reduce attitude disturbance. • Two transmitting antennas are used to reduce the total length of cables. Solar power satellite is known as a promising source of clean energy from space in the future. One of the key problems of current solar power satellite concepts is the huge cost because of the excessively large mass and fuel consumption. The objective of this paper is to propose a new Free-Drift Solar Power Satellite concept that reduce mass and fuel consumption. Firstly, Sun-frozen orbit in geosynchronous Laplace plane and quasi-Sun-pointing attitude are designed to significantly save orbital and attitude control fuel. To this end, an orbit-attitude coupled modelling method considering space perturbations is proposed for multibody systems. Orbital and attitude dynamics and control are studied to substantiate the orbit and attitude design. Secondly, two transmitting antennas and three solar arrays are adopted and allocated elaborately to reduce structural deformations and attitude disturbance. The mass of the transmitting cables from solar arrays to transmitting antennas is also reduced by the proposed layout. Finite element analysis is performed to validate the design of supporting structures. Finally, other subsystems are briefly introduced and the system parameters are summarized. [ABSTRACT FROM AUTHOR]

In recent years, the conventional power system is becoming a hybrid power system with increments in the interconnection of Renewable Energy (RE) sources, High Voltage Direct Current (HVDC), and Energy Storage (ES). At the same time, the power system network is also becoming more flexible in managing demand and supply variability. These factors posed a challenge in achieving frequency stability when there are abrupt changes in the power system such as load increment/decrement, loss of generators, and faults. Due to this, load frequency control (LFC) has shifted from traditional controllers to hybrid controllers and from traditional optimization methods to hybrid metaheuristic optimization to achieve better stability in hybrid power systems. This paper provides a comprehensive review of load frequency control (LFC) and power system flexibility. Other frequency stability mechanisms that support further LFC i.e. application of frequency nadir and rate of change of frequency (RoCoF), control in wind turbines, and demand response are also presented. This review also highlights the research gaps and potential future research direction of LFC for multi-area hybrid power systems. [ABSTRACT FROM AUTHOR]

Hydrogen production by the electrolysis of water using renewable energy is a key component of the energy transition toward carbon dioxide emission reduction. Therefore, electrolyzers are in increased demand and have to meet higher technical requirements. The most important component of an electrolyzer is the type of cell. The proton-exchange membrane (PEM) cell is the one that offers the most promise. Cell components and their materials are subject to volatile development conditions. In particular, various production technologies come to mind for producing assembled cells the so-called membrane electrode assembly (MEA). Currently, only small quantities are produced. To meet demand, production must be scaled up quickly. Nevertheless, high requirements are placed on the quality of the cells, since high-priced raw materials are used for PEM cells. Therefore, this paper presents a data-driven approach to assessing the appropriate production technology. First, general and specific requirements of a cell production scale-up are derived. Later, characteristics of suitable cell production technologies are extracted. By using a digital factory model, suitable for shop floor data, the basis for the conception and validation of possible production concepts is laid. [ABSTRACT FROM AUTHOR]

Recent developments show that not only material supply chains but also energy supply systems can be hit by strong disruptive forces. This brings new challenges and highlights the importance of a resilient energy supply to the manufacturing industry. These challenges can be solved by implementation of technical solutions and a change of energy supply system designs. However, manifold options available to a company require a methodological approach to rapidly identify the potentially most suitable variants under individual circumstances for a specific factory. This paper introduces a simplified methodology for the first planning steps in the transition towards a resilient energy supply system of production sites. For this purpose, relevant resilience factors are identified and selected for energy supply systems of a factory. With a structured procedure, different technical system combinations are created and evaluated in respect to implementation effort and their potential impact on the resilience factors. Simultaneously, different disruptive scenarios are ranked based on likelihood and expected severity. The impact of these combinations on the energy load profile of the factory is evaluated and the results are presented based on the suitability of each energy supply system alternative in respect to the different disruptive scenarios. This provides information on the potentially most favorable options to the relevant stakeholders for further implementation. To demonstrate the applicability of the developed methodology, a case study on a battery cell factory is presented. The production of batteries is a fast-growing industry with a high relevance for the transition towards a sustainable economy while the battery production itself requires high amounts of electricity and heat at different temperature levels. Alternative supply scenarios besides the unidirectional consumption of fossil fuels are needed in in terms of sustainability and to increase resilience. [ABSTRACT FROM AUTHOR]

Within the last years but especially since the beginning of 2022 there is a large increase in the demand of renewable energy. Currently, so-called green hydrogen is one of the most promising energy carriers for renewable energy. To meet the demand for green hydrogen, installation of production technologies like water electrolyzer is necessary. In order to play a role within the hydrogen economy, companies have to be able to design their hydrogen production capacities for a spot-on time-to-market. This leads to difficult ramp-up decisions and trade-offs between costs, technological maturity, and market success. As the phases of research and development influences a significant part of the costs (especially within production) after reaching time-to-market, they determine the later cost structure of the company, the possibilities to gain a competitive advantage or to survive on the market. Analyzing literature with regard to wind and photovoltaic industry shows several factors influencing success. This paper identifies and discusses these critical success factors, transfers their relevance onto the current hydrogen economy, and points out critical aspects for market entry decisions. With respect to a globalized world, in which dependencies from single countries can lead to unwanted constraints, the results become even more important. [ABSTRACT FROM AUTHOR]

As part of a global effort of decarbonization, understanding energy system behavior is essential. This paper develops and presents a causal loop diagram of the energy system on St Helena Island. The well-defined physical boundary of this electrical island makes it an interesting case for examining how the system might behave during decarbonization processes. This study focuses on the impact of decarbonization on carbon dioxide emissions and energy accessibility. A model is developed based on publicly available data which is used to test the strength of correlations between key variables. Where there is insufficient statistical data, the authors use behavioral relationships established in existing literature to draw connections. Six main causal loops govern the decarbonization transition through processes of: greener tourism, affordable consumer energy, affordable energy procurement, social development, economic development, and sustainable renewable energy economy. Many of these are positive re-enforcing loops, showing the positive interaction between renewable energy and social and economic development on the island. [ABSTRACT FROM AUTHOR]

Energy flexibility plays a crucial role in the current energy transition, as it can contribute to a stabilization of the grid. The integration of electricity from renewable energy sources leads to large fluctuations in power supply, compromising the reliability of supply and the grid stability. Employing surplus of variable renewable energy (VRE) to cover the industrial demand can on one hand reduce the need for grid upgrade on a long term. On the other hand, integrating VRE can contribute to fulfill decarbonisation targets in the industrial sector. As a consequence, the share of renewable energy in the total energy consumption can be increased. This paper aims at assessing the role of VRE integration in the process industry as a mean to leverage energy flexibility. The assessment consists of a scenario-based evaluation, complemented by a simulation model, able to quantify the reduction of specific CO 2 emissions. The developed approach is demonstrated within a case study in the paper industry. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES) systems offer significant potential as large-scale physical energy storage technologies. Given the increasing global emphasis on carbon reduction strategies and the rapid growth of renewable energy sources, CAES has garnered considerable attention. However, the optimal design of CAES systems presents challenges due to the intricate coupling of multiple parameters, complex physical processes, and highly variable operating conditions. This paper comprehensively reviews three key aspects related to CAES optimal design. Firstly, it examines system analysis and optimization methodologies. Secondly, it investigates the off-design characteristics of CAES systems. Lastly, it explores system design methodologies specifically addressing off-design operations. Notably, the corresponding-point methodology (CPM) is proposed as a means to provide clearer guidance for system improvements. Future research should aim to combine CPM with traditional methods to establish more efficient and intuitive system optimization approaches. Furthermore, the exploration of active off-design regulation strategies, including the integration of various adjustment methods and multistage combined regulation for CAES systems experiencing wide load fluctuations, requires further investigation. Novel CAES configurations that enable efficient off-design conditions should also be explored, despite the inherent complexities associated with regulation and control. Finally, a coupled design methodology based on off-design operation data and probabilistic-load factor analysis is presented. This paper provides valuable insights and guidance for the optimal design and study of CAES systems. • CPM combined with traditional methods is needed to improve CAES round-trip efficiency • Not high temperature/pressure but "corresponding" is key factor to high round-trip efficiency • Multiple regulations and new system structure are needed for wide-load operation • Design methodology addressing complex off-design operations needs further improvement [ABSTRACT FROM AUTHOR]

Carbon emissions from the combustion of fossil fuels continue to be a significant barrier in the fight against global climate change. Carbon taxes are an energy policy tool for substantially carbon dioxide (CO 2) emissions reduction. This paper proposes a new strategy to promote the use of renewable energy by enterprises in industrial parks against the backdrop of a carbon tax. Using an evolutionary game model based on life cycle assessment (LCA), this study investigates the impact of dynamic carbon tax mechanisms, fines, and subsidies on the energy choices of industrial park enterprises. This paper's primary contribution is the incorporation of LCA into the evolutionary game model, which facilitates accurate calculation of the carbon tax parameter value. According to the study's findings, the carbon tax rate increases the propensity of enterprises to acquire a high percentage of renewable energy. While there is a negative relationship between fines and the propensity of enterprises to adopt a low proportion of renewable energy, an increase in subsidies decreases the proportion of enterprises that purchase a high proportion of renewable energy. This paper concludes by providing a theoretical foundation for policymakers to construct precise carbon tax schemes and promote renewable energy consumption among industrial park enterprises while simultaneously reducing carbon emissions. [Display omitted] • Carbon tax & energy decisions: life cycle assessment & evolutionary game theory. • Life cycle assessment for enterprise carbon taxes under multiple energy options. • Carbon tax mechanisms, fines, and subsidies influence enterprises energy decisions. • Excessive subsidies discourage renewable energy use, but fines can. [ABSTRACT FROM AUTHOR]

In the present situation, renewable energy has become a vital source to mitigate energy crises and environmental threats. In order to ensure that women's contributions, knowledge and opinions represent a significant portion of the expanding energy sector, it is crucial to approach the development of renewable energy from a gender perspective. Bibliometric analysis was performed to identify prior research on two primary terms, "Gender" and "Renewable Energy", from 1995 to 2022. Identifying the most influential countries, authors, publications, journals, and co-occurrence of keywords helps to understand the evaluation and trends of the research through network visualisations by bibliometric approach. This paper presents a comprehensive bibliometric review of the influence of gender on renewable energy. The result of this paper gives details picture of the gender impact on renewable energy during the period from 1995 to 2022. The paper centred on bibliometric and cluster analysis based on the two most important databases, Web of Science and Scopus. The study involved utilising various bibliometric tools, including R Studio, VOSviewer, and Biblioshiny. The study revealed an increase in publications and citations during the third period since 2017, after the United Nations set the target of the Sustainable Development Goal. Most influential countries and productive authors belong to China, the United States of America, and the United Kingdom. Energy Research and Social Science are predominated in publications on the research topic. In addition, the co-occurrence of keywords found major keywords related to this topic, such as renewable energy, gender, women, knowledge, sustainability, climate change and sustainable development. Finally, the study observed a huge research gap because developing nations are more affected by the lack of gender-influencing policies on the growth of renewable energy. Thus, the study provides a comprehensive overview of research in this field that can help researchers and stakeholders identify potential ways for future research in gender considerations on renewable energy access by formulating effective policies to change society and reach the Sustainable Development Goals. • Gender has become a focal point of interest within the renewable energy sector. • Using network visualisation, bibliometric analysis examines research trends by keyword occurrence and country collaborations. • Publication, citation, journal, and authorship contribution trends are to be analysed. • The research growth across different periods was discussed for potential future studies. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy sources (RES) into modern power systems may reflect the problem of frequency stabilization. Therefore, this paper proposes a new combined Fuzzy Fractional-Order Proportional-Integral (FOPI) and Tilt-Integral-Derivative (TID) controller to improve the frequency response of a hybrid power system. The proposed controller combines the advantages of intelligent Fuzzy FOPI and conventional TID controllers, resulting in more effective and robust load frequency control. In this study, the parameters of the proposed Fuzzy FOPI + TID combination are optimized using a novel metaheuristic algorithm, namely Wild Horse Optimizer (WHO). The case study system is a two-area conventional power system integrated with different RES including photovoltaic (PV) and wind generation as well as distributed electric vehicles (EVs) between the two areas. The effectiveness of the proposed controller is tested under various scenarios such as step load perturbation, random load variation, wind speed fluctuation, solar irradiance change and sensitivity analysis. In addition, the disturbance of wave energy oscillation is applied in Area 2 to evaluate the robustness of the proposed controller. For each scenario, the proposed Fuzzy FOPI + TID controller is compared with the conventional PID, single TID, and individual Fuzzy FOPI controllers using the proposed WHO algorithm and other optimization algorithms presented in the previous literature. The results show that the proposed FOPI + TID controller is superior to other controllers in terms of integral square error, peak overshoot, maximum undershoot and settling time for all scenarios. Furthermore, the presence of EVs helps in improving the frequency and tie-line power deviations. Finally, the time domain simulations are implemented using Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Renewable energy has demonstrated a significantly greater increase in its share of total energy consumption than carbon emissions in recent decades. This rise in renewable energy consumption did not effectively mitigate climate change, as evidenced by the accelerated rate of climate change. This paper aims to determine the cause of this failure by examining the total effect centrality and mediating effects of renewable energy in economic systems in the short run and long run. The total effect centrality and mediating effects of renewable energy illustrate the evolution of its overall impact on the major variables of an economic system and its role in mediating the indirect effects of these variables on carbon emissions, respectively. The study employs a recently developed network analysis that is a pioneer in the estimation of the time-varying total effect centrality and time-varying mediating effects of renewable energy, while also accounting for its cross-associations among all variables in a network. The study derives three conclusions by examining the three countries with the highest carbon emissions in their respective regions: the United States in North America, China in Asia, and Germany in Europe. First, renewable energy has become an essential element of economic systems, having a substantial total effect centrality on macroeconomic systems and, by way of its mediating effects, substantially mitigating the indirect effects of major macroeconomic variables on carbon emissions in the short run. Second, the total effect centrality and mediating effects of renewable energy diminish in the long run, indicating that its capacity to effectively mitigate climate change remains constrained in structural terms. Third, the long-term decline in total effect centrality rises by 0.3, 0.7, and 0.4 for the United States, Germany, and China, respectively. In the same nations, the long-term decline of mediating effects rises by 0.2, 0.4, and 0.2, respectively. The study provides pertinent policy recommendations on how to augment the total effect centrality and mediating effects of renewable energy in economic systems. [Display omitted] • Explains why renewable energy failed in curbing the accelaration of climate change. • Introduces the 'total effect centrality' of renewable energy in economic systems. • Illustrates the time-varying mediation role of renewable energy in economic systems. [ABSTRACT FROM AUTHOR]

To accomplish the desired reduction in carbon emissions, society must improve the integration of renewable generation and accelerate the adoption of technologies like peer-to-peer energy trading. This necessity is particularly evident in the agriculture sector, specifically for energy-intensive forms of farming, such as dairy farming. Nevertheless, the integration of renewable energy sources and peer-to-peer energy trading presents notable challenges. Effective control strategies are essential for dairy farms to engage in peer-to-peer energy trading markets. This paper introduces the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator-extended (MAPDES), which leverages multi-agent systems to facilitate the participation of dairy farms in peer-to-peer markets. The energy trading strategy yields a 43% reduction in electricity costs and a 42% decrease in peak demand, while also increasing energy sales by 59% compared to the baseline scenario, which does not include peer-to-peer energy trading or renewable energy sources. This model has been tested across various farm community sizes, renewable energy combinations, and geographical locations to determine the ideal farm community configuration and assess the model's applicability across different settings. • Developing a Multi-Agent System for Peer-to-Peer Energy Trading in Dairy Farming. • Analyzing case studies of dynamic renewable energy and farm sizes/locations for optimal setup. • Evaluating P2P dairy farm energy trading using case studies from Finland and Ireland. • Evaluating the algorithm's practical use in dairy farming by assessing its efficiency and real-time capabilities. [ABSTRACT FROM AUTHOR]

Constructing an accurate and reliable solar photovoltaic (PV) power forecasting system is crucial for smart grid management and dispatch. However, due to the intermittent, non-stationary and random nature of solar energy, current methods cannot effectively capture the dynamic change patterns of PV data, resulting in a forecasting accuracy that cannot satisfy the requirement for stable operation of smart grids. To address this issue, in this paper, a new solar PV power forecasting system is proposed based on a new adaptive mode decomposition method that combines machine learning models. First an adaptive mode decomposition method is proposed for adaptively eliminating high-frequency information of PV data, mitigating the impact of high noise on the forecasting performance. Then the powerful mapping ability of machine learning model is utilized to construct a solar PV power generation combination forecasting system. The system adopts a multi-objective optimization strategy to determine the optimal weights of the combination model, which reduces the volatility of the forecasting results and enhances the stability of the forecasting system. Experimental results demonstrate that the proposed system achieves the best overall performance with online manner on 10 state-of-the-art baselines. Furthermore, the evaluation analysis by Diebold–Mariano test, improvement ratio and sensitivity analysis further show that the stability and accuracy of the proposed system outperforms the comparative baselines. [ABSTRACT FROM AUTHOR]

The major problems in the power system network of the South Asian Association for Regional Cooperation (SAARC) region include the utilization of substantial fossil fuels, which leads to significant emissions of CO 2. Moreover, due to the depletion of fossil fuels, it is very difficult to manage the demand requirement of the SAARC regions, which leads to significant power shortage issues. These problems would be resolved through the integration of the substantial amount of renewable energy resources (RERs) that are available in excess in the SAARC regions into the power system networks. This will provide a futuristic roadmap to a sustainable and green energy environment in the SAARC region. In this regard, the SAARC region has devised a new plan to transform its smart grid infrastructure into the super smart grid (SSG), which would connect the SAARC countries using the high voltage transmission lines utilizing each country's substantial RERs. To accelerate this ecofriendly effort, the key objective of this review study is to evaluate the potential of the SAARC SSG for demand management and emissions reduction. This study provides a comprehensive overview of SSG technology, covering its technical background, management, power flow improvement techniques, power stability techniques, simulation models, and future steps for development in the SAARC region. First, the regional conflicts, energy deficiency, and potential of the SAARC region have been discussed. Second, a potential architecture of the SAARC SSG has been simulated. Third, a novel Markov chain modeling has been presented for demand management in the SAARC regions. The results and discussions section presents findings from simulations and models. The paper also discusses CO 2 reduction techniques, including methods for capturing, storing, and utilizing carbon in biofuel. The study also discusses futuristic optimization techniques in SSG, including blockchain, federated learning, reinforcement learning, the metaverse, digital twin technology, artificial intelligence, and cloud computing. The SSG challenges and limitations section summarizes the challenges and limitations faced by SSG. Finally, a roadmap to a sustainable and green energy environment for the SAARC power grids has been identified to support the implementation of the futuristic SAARC SSG. The results exhibited that there is a good potential for successful demand management and emissions reduction through the SAARC SSG. [ABSTRACT FROM AUTHOR]

[Display omitted] • A methodology to assess carbon neutrality in district and communities is defined. • The proposed methodology is implemented in a ready-to-use and user-friendly tool. • The proposed approach aims at supporting the energy transition of cities. Positive Energy Districts are expected to play a major role in the energy transition of cities. Hence, this paper aims at introducing a novel methodology useful for district energy and environmental analysis and intended to support the accomplishment of the targets of Positive Energy Districts at the district or community level. The proposed approach relies on the basic concepts underpinning the "Baseline Emission Inventory" but encompasses the ambitious and challenging objectives characterizing Positive Energy Districts. For making the proposed evaluation framework accessible to as many user categories as possible (researchers, local institutions, urban planners, etc.), the calculation steps have been transposed in a user-friendly, ready-to-use tool called "En-to-EnD. Energy and Environmental analysis of Districts". The applicability and replicability of the outputs of this work have been proven through the energy and environmental assessment of a reference case study. In particular, the district being analysed is in the South of Italy and is equipped with photovoltaic plants and a wind turbine. The results obtained show that the proposed approach may serve not only to prove the achievement of the Positive Energy District status, but also to guide the design of more sustainable district-based energy systems, thus supporting the decarbonization of cities. [ABSTRACT FROM AUTHOR]

• A multi-microgrid energy real-time optimal control scheduling strategy is proposed. • Energy storage devices can actively participate in optimal energy scheduling. • Improved resilience and flexibility of energy dispatch for multiple microgrid. • Significantly reduce the number of microgrid connections to the distribution grid. • Maximising the full uptake of renewable energy in multi-microgrid systems. With the increasing prominence of the energy crisis and global climate issues, it has become an important cornerstone of the energy revolution to improve the utilization rate of renewable energy and gradually reduce the use of fossil energy. Nonetheless, the erratic fluctuations and instability of renewable energy sources such as wind and solar energy have presented certain obstacles to the effective utilization of renewable energy. In order to maximize the utilization of renewable energy, enhance its utilization efficiency, and reduce the carbon emission of power supply, this paper first proposes a real-time collaborative optimal energy scheduling and dispatching control model for multi-microgrids. Subsequently, it proposes a real-time optimal control and dispatching strategy for multi-microgrid energy based on storage collaborative. This model considers the energy storage device as an energy management controller, enabling it to participate in the energy collaborative dispatch of multi-microgrid. This enables the energy storage device in the microgrid not only to actively participate in the optimal scheduling of energy, but also to make the energy in the storage device be reasonably distributed in the multi-microgrid system, to maximize the full absorption of renewable energy in the multi-microgrid, thereby reducing the dependence of the microgrid on the distribution network, and significantly improving the elasticity and reliability of the multi-microgrid system. Finally, the feasibility and effectiveness of the proposed optimal control scheduling strategy are verified by simulation. [ABSTRACT FROM AUTHOR]

In recent years, dual-atom catalysts (DACs) have emerged as a significant area of interest, extending beyond the scope of single-atom catalysts. The DACs can offer enhanced metal loading and a wider variety of active centers by integrating two metal atoms. The advancement of atomically dispersed catalysts has led to broader adaptability and superior catalytic efficiency for sustainable energy conversion reactions, showcasing great potential in electrocatalysis. However, it is still facing challenges and uncertainties in thoroughly understanding their mechanisms and identifying precisely and universally applicable synthesis methods. In this review, we firstly provide a comprehensive introduction of the synthesis method of DACs, including pyrolysis, atomic layer deposition, and impregnation. Then, we introduce their potential applications in several key energy conversion reactions such as hydrogen evolution reaction, which can achieve an impressive overpotential of only 10 mV to generate a current density of 10 mA cm−2 with IrRu DACs, and oxygen evolution reaction. Through a comprehensive discussion of the synthesis, application, and catalytic mechanism of DACs, this paper aims to understand their structure-performance relationships and the deep catalytic mechanisms, hoping to provide new insights for catalytic science in the field of renewable energy. We anticipate that incorporating DACs into our analysis will pave new pathways for tackling energy and environmental challenges, thereby steering catalytic science towards a future of enhanced efficiency, stability, and sustainability. [Display omitted] • Advanced synthesis techniques for dual-atom catalysts are summarized systematically. • Dual-atom catalysts for wide-ranging electrocatalytic applications are highlighted. • Innovative strategies for enhancing the performance of dual-atom catalysts are discussed. • Future research directions for dual-atom catalysts are analyzed and outlined. [ABSTRACT FROM AUTHOR]

Accurate photovoltaic (PV) power generation forecasting is crucial for optimizing the integration of solar energy into power grids and advancing towards a cleaner, more sustainable energy future. However, the inherent variability and complexity of PV power generation data pose significant challenges for accurate forecasting. To address these challenges, this paper introduces Collaborative Directional Representation (CoDR), a novel deep learning model that extracts and represents the directional fluctuations of solar irradiance data to improve forecasting accuracy and reliability. CoDR utilizes a series of steps, including data preprocessing, fluctuation extraction, directional representation, linearization, de-extraction, mapping, and data postprocessing, to capture both the temporal and spatial dependencies within the data. CoDR leverages a unique directional representation to capture both temporal and spatial dependencies in the data, enabling superior forecasting accuracy and robustness compared to twenty-two state-of-the-art benchmark methods. We validate CoDR on a real-world dataset of PV power generation in Belgium, demonstrating its effectiveness through extensive experiments, ablation studies, and sensitivity analyses. Importantly, CoDR enhances the transparency of the forecasting process by revealing the causal relationships and directional influences among input variables and PV power output. This explainability feature provides valuable insights into the underlying drivers of PV power generation, promoting trust and informed decision-making in the transition to cleaner energy systems. • Introduces Collaborative Directional Representation (CoDR) for accurate PV forecasting. • Outperforms 22 benchmark methods, achieving significant error reductions. • Provides explainable insights into PV power generation using 3D visualizations. • Demonstrates effectiveness on a real-world Belgian dataset through extensive analyses. [ABSTRACT FROM AUTHOR]

Under the background of carbon peak and carbon neutrality, this work proposes a novel municipal solid waste (MSW) oxygen-fuel combustion system driven by electrolytic oxygen production from renewable energy. Then, the corresponding mathematical model is established, which includes the supply sub-model related to the renewable energy generation process and water electrolysis process, the storage sub-model with battery and oxygen tank, and the demand sub-model of the oxygen-fuel combustion process, featuring a mixed integer linear programming one. To describe in-out characteristics of the MSW oxygen-fuel combustion system, two methods with pre-defined parameters and simulation models, are adopted respectively in this paper. Taking the minimum total cost of the system as an optimization target, the configuration scheme and operating strategy of the oxygen-fuel combustion system can be obtained. An example of an MSW plant coupled with a photovoltaic (PV) system is adopted as a case study to verify the effectiveness of the proposed methods. Results show that the design schemes can be determined by two proposed methods, but the one with simulation models superiors to describe the in-out performance of the MSW oxygen-fuel combustion system. What's more, the effects of the intrinsic intermittence and fluctuation of renewable energy on the MSW oxygen-fuel combustion process are investigated comprehensively, by analyzing the operation scheme of the proposed system. Finally, the environmental impacts of the MSW oxygen-fuel combustion system driven by electrolytic oxygen production from renewable energy are evaluated. A scheme diagram of the oxygen-fuel combustion system of municipal solid waste driven by electrolytic oxygen production from renewable energy. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper introduces a first and second law based transient mathematical model for 4-stroke compression ignition internal combustion engines (CI-ICE) driven by diesel/biodiesel/biogas mixtures to allow for the maximization of system performance. In this way, the CI-ICE morphology that provides easier access to the currents that flow through it is sought, according to constructal law. The total entropy generated in the CI-ICE cycle was calculated by adding the entropy generation in all strokes. An optimization problem was formulated based on the variation of operating and geometric parameters, and the objective functions were the effective and 2nd law efficiencies, η ef and η II , as well as the destroyed exergy fraction, y D. The optimal ranges found for the bore-stroke, compression and conrod-crank ratios were 0.51 ≤ B opt ≤ 1.25 ; 2.07 ≤ ROD / CS opt ≤ 4.14 , and 10.23 ≤ CR opt ≤ 33.44 in which η ef η II max ≃ 0.33 0.37 with y D , min ≃ 0.66 for D100 (pure diesel), and η ef η II max ≃ 0.31 0.35 with y D , min ≃ 0.64 for B50D25NG25 (biodiesel/diesel/natural gas blend). The maxima and minimum found were sharp, since η ef and η II varied up to 71% and 69% in comparison to their maximum values, respectively, and y D up to 45% in comparison to its minimum value within the adopted ranges of variation of the optimized parameters. Such large variations stress the importance of considering the optimized CI-ICE morphology in actual design, i.e., CI-ICE constructal design. [ABSTRACT FROM AUTHOR]

The integration of 5G base station (5G BS) clusters and edge data services introduces novel digital loads (NDLs) into the distribution system (DS), significantly impacting the interactive coordination of 5G-DS. This paper proposes an expansion planning model of 5G and DS considering source-network-load-storage coordination. Here, renewable energy resources (RESs), 5G BS clusters, edge data centers (EDCs), and DS source-network-load-storage are coordinately planned. The dynamic service access of 5G BS cluster are formulated to capture the coupled nature and inherent flexibility of 5G services. Considering the flexible processing of EDCs, a detailed categorization is conducted based on data service type, leveraging the synergies between cloud and edge computing to achieve flexible data load distribution. The original expansion planning model is reformulated as a mixed integer linear programming (MILP) problem to facilitate its computational solution. Simulation results from case studies on Portugal 54 node network over individual expansion planning and 5G static-access are performed to validate the effective and superior performances of the proposed method. Simulations results show that the system cost can be reduced by at most 15% with a higher operational flexibility. [Display omitted] • An expansion planning model of 5G and DS considering source-network-load-storage coordination is proposed. • NDLs from 5G BS clusters and EDCs are introduced to accommodate the RESs. • Dynamic access services of 5G BS clusters are formulated to leverage their flexibility. • Synergies of cloud and edge computing in EDCs are captured to facilitate source-network-load- storage coordination. [ABSTRACT FROM AUTHOR]

Solar thermal energy is one of the important sources of renewable energy. The solar thermal energy gives clean energy which can be utilized for industrial applications. Industrial heaters which require heat energy for many industrial uses can be attained using these solar thermal absorbers. The solar thermal absorber is designed using a layer design that consists of material compositions of Titanium (Ti) and Stannic selenide (SnSe 2) for ground and substrate sections respectively. A graphene layer which has the good properties to be used in solar absorbers is added between the Aluminum and SnSe 2 material. A thin graphene section is inserted above the SnSe 2 substrate and below the Ti resonator. To catch the good absorption in the current design, the best four wavelengths (micrometer) have been attached such as 0.22, 0.8, 2.46, and 2.85. This design's contribution can be studied in 4-atm regimes (from UV, V to NIR, and MIR). According to the wavelength and bandwidth ranges, the current absorption explores more than 97% (97.4%) in between 2.4 and 3 μm (600 nm) bandwidth and exceeds 95% (95.2%) from 0.2 to 1 μm (800 nm) respectively. To the overall range of design, it extracts 90.3% for the 2800 nm band between 0.2 and 3 wavelength regime. The flow of the presentation of the proposed paper is (i) Introduction (ii) Design and Parameters, and (iii) Results and Discussions. The explored absorber can be effective in water heating and swimming pool heating for homes. Moreover, it can also be applied in large industries as a solar thermal absorber for heating applications. [ABSTRACT FROM AUTHOR]

• Description of a multi-objective problem that connects two energy and water systems. • Proposing an enviro-economic model to minimize water extraction and operating cost. • Integrating the electrical, thermal, water, and hydrogen energy systems. • Proposing a day-ahead decision-making for optimal management of networked EHs. • The profit of cooperation is divided among EHs based on their contributions. This paper proposes a cooperative multiple-objective approach for the sustainable economic scheduling of networked energy systems to simultaneously optimize the cost of energy and groundwater extraction from underground sources. The suggested strategy enables networked energy hubs to exchange electricity for greater financial gain. Various energy hubs collaborate to maximize gain that is fairly allocated among the hubs to guarantee the coalition's economic stability, according to the collaborative game theory. The coalition achieves its best gain through collaboration and payoff, which is equally and regularly divided among the hubs, according to the Shapley value. The suggested model takes into account a networked system with hubs connected to various types of energy, including electrical and natural gas, renewable energy sources, electrical, and thermal energy storages, combined heat and power units, and gas boilers, cooling, hydrogen, and water systems to meet demands. The numerical results show that the suggested cooperative approach reduces the cost function of the system by 12.71%. The self-discharging rate of battery storage devices can increase the cost function by $ 7.65. Also, the sensitivity analysis on the battery storage sizes demonstrated that they can reduce the total cost of the system by $ 15.65. [ABSTRACT FROM AUTHOR]