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Hydrogen is emerging as a critical player in transitioning to sustainable and renewable energy systems, serving roles in energy storage, grid balancing, and decarbonization. This paper explores various aspects of hydrogen, including its production through renewable-electricity-driven electrolysis, advanced storage techniques, and incorporation into current energy systems. It highlights primary electrolysis methods like PEM and alkaline, noting their improved efficiency and cost-effectiveness. Various hydrogen storage methods, such as physical, chemical, and advanced porous materials, are examined for their benefits and limitations. The review further explores hydrogen's integration into grid storage systems and microgrids to enhance energy reliability. It discusses hydrogen's application in fuel cells for electricity generation, focusing on technological advancements that improve efficiency and reduce costs. Additionally, the paper underscores hydrogen's crucial role in reducing CO 2 emissions in industrial processes like steel production and its use in residential and commercial energy supply through combined heat and power systems. Economic aspects and supportive policies from regions are analyzed, highlighting the global efforts and policies supporting the potential hydrogen in renewable energy systems. This analysis emphasizes hydrogen's comprehensive role in enhancing renewable energy systems and achieving global sustainability objectives, providing a thorough review of recent progress and challenges. • Identifies PEM and alkaline as efficient electrolysis methods. • Analyzes varied H 2 storage options for mobility and stationarity. • Highlights advancements in H 2 fuel cells for vehicles. • Discusses global policies supporting H 2 integration. • Reviews economic impacts of scaling H 2 infrastructure. [ABSTRACT FROM AUTHOR]

Climate change and energy crises are twin challenges that require collective wisdom and polygeneration energy systems for sustainable development. Hydrogen is an abundant element in the universe that can shape the future of energy transmission and storage from electrons to molecules. This paper presents utilization of a solar PV assisted hydrogen and electricity production and storage system for a university transportation fleet of 43 student transit buses to reduce annual 9842 liter fuel consumption and 26.52 Mt CO 2 emissions. The system is designed, modeled, and simulated using TRNSYS® energy simulation software and optimized in TrnOpt linked to GenOpt for dynamic weather conditions of Gujrat (Pakistan), Fargo (USA), London (UK). As a model, the results of a 27kW p PV system are presented that assists an alkaline electrolyzer generating 12696 m3 hydrogen, 6348 m3 oxygen, 16848 KWh electricity exported to national grid and 11.56 GJ thermal energy annually. The proposed system generates energy vectors including hydrogen, electricity, heat, and oxygen with optimal performance during intermittent weathers. In this paper, a business model canvas is developed for solar PV assisted hydrogen production and storage system. These outputs can be accredited to the hydrogen as an energy alternative for green and sustainable future for transportation sector. [Display omitted] • A paradigm energy shift from electron to molecules for a sustainable future. • A Solar assisted polygeneration system is dynamically simulated and optimized in TRNSYS for various climate conditions. • Proposed 27kWp system effectively produces 12696 m³ hydrogen, 6348 m³ oxygen, and exports 16848 kWh electricity to grid. • The system cuts annual fuel consumption by 9842 liters for university fleet, reducing CO 2 emissions by 26.52 Mt annually. • Developed a business model for the sustainable application of solar PV-assisted hydrogen production in transportation. [ABSTRACT FROM AUTHOR]

The paper is devoted to the development of a device for protecting facilities with turn-to-turn insulation, namely transformers and wind generators in order to increase the reliability of operation of electrical power systems, renewable energy installations and energy storage systems. The paper presents the results of experimental studies of the parameters of a prototype frequency-dependent device, as well as an assessment of the effectiveness of suppression of high-frequency surge voltages by the proposed device. The results of four groups of measurements are shown, which were compared with each other and with the results of previously showed computer simulations. The high efficiency of the proposed device when influencing the slope and amplitude of short high-frequency transients is shown. It is indicated that the parameters proposed earlier in the simulation allow the device to be used effectively. A brief analysis of alternative methods and means of protection is provided, and the prospects for using the device as protective equipment for wind power plants are also considered. [ABSTRACT FROM AUTHOR]

Green hydrogen is crucial for society's access to peaceful, secure, and stable energy sources to flourish sustainably and get universal government support. To protect the environment and provide energy security, it is essential to use hydrogen as a sustainable fuel and energy source to cover the energy baseload. This paper investigates the developments of renewable energy (RE) and the potential for green hydrogen in the Middle East and North Africa (MENA) region. There is a considerable chance to use RE because of the MENA region's excellent solar radiation. On the other hand, many countries in the MENA region are classified as one of the poorest in water resources and are known to have water-energy challenges. This paper focuses on the water needed to produce hydrogen in countries with water scarcity. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was performed for green hydrogen generation in the MENA, where recommendations for countries with a water-energy nexus can be made. Additionally, this paper offers suggestions for creating a sustainable hydrogen economy, such as cutting back on the domestic use of fossil fuels, subsidizing the renewable production industry, and supporting hydrogen manufacturing. • Green hydrogen production is highly affected by water scarcity. • SWOT analysis was performed on green hydrogen generation in the MENA. • Recommendations were provided on a country-by-country basis. [ABSTRACT FROM AUTHOR]

Fossil fuels are deemed to diminish because of their limited availability, no renewability, and emissions of greenhouse gases and other poisonous materials to the atmosphere and the ecosystem. Renewable energy based on sustainable biomass has occupied an increasing share of the energy system over the past decade. Among several massively cultivated crops worldwide, corn, sugarcane bagasse, and soybean straws are some of the leading agricultural by-products that can generate substantial energy (e.g., biofuels, hydrogen) as vital sustainable energy to substitute non-renewable fossil fuels. This paper aims to review recent progress in renewable energy generation from biomass based on thermochemical techniques-i.e., gasification, pyrolysis, and liquefaction-detailed technical features and their application for extraction energy from biomass. The quality, composition, and productivity of renewable energy extracted from biomass based on existing thermochemical techniques are compared, and their technical dependencies upon the thermochemical processes are discussed. Furthermore, analysis of technical deficiencies of existing methods, and exploration of innovative emerging technologies for high-efficiency and high-quality renewable energy generation from biomass straws. In general, biomass gasification stands out as the most efficient technique for hydrogen production (HP) from biomass due to mature technology, high hydrogen yield, and potential for integration with other presented techniques. [Display omitted] • A detailed review of HP from biomass using thermochemical techniques. • Discussion about gasification, pyrolysis, and liquefication working principles. • Comparison between all types of presented thermal techniques. • Advantages and disadvantages of gasification, pyrolysis, and liquefication. • Overall performance of all thermal techniques presented in this review. [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]

Extensive research on zero-emissions autonomous surface vehicles has been recently carried out, aiming at increasing autonomy. Due to their small size, unmanned vehicles present unique difficulties in terms of weight and dimensions when powering the vehicle with renewable energy sources. In this paper, photovoltaic source, fuel cell and Li-ion battery multi-source configurations are proposed, demonstrating by physical layout and simulation results the feasibility of the prototype, compliant with strict constraints of the vehicle under test. The energy system model of the vehicle under test is implemented in MATLAB/Simulink. A comparison of multi-source energy system configurations is proposed and validated by simulation results in terms of endurance, weight and size. If compared with the original battery-powered vehicle, as shown by comparing simulation results, the endurance is doubled, extended up to 12 h on the most favorable day of the year. The solution even complies with payload and physical dimensions constraints. • Autonomous surface vehicles: overview of the most competitive prototypes. • The SWAMP case study: experimental characterization of the battery-powered version. • Modelling and simulation of SWAMP PV-fuel cell–battery multi-source power supply. • Feasibility analysis and comparison of Multi-source power configurations. • Assessment of endurance, weight and size of multi-source configurations. [ABSTRACT FROM AUTHOR]

This paper investigates the feasibility and benefits of integrating hydrogen storage systems into off-grid power systems. As a case study, a stand-alone microgrid located on a small island in southeastern Sardinia (Italy) and already equipped with a photovoltaic (PV) system coupled with batteries is chosen. To evaluate the integration benefits of the two storage systems (hydrogen and batteries) and the optimal sizing of the hydrogen storage section, a parametric analysis with a simulation model implemented in the MATLAB environment has been carried out. Results show that the optimal integration between the two storage systems is found by imposing a share of the batteries (18 kWh, 50% of the overall battery capacity) to exclusively supply the load demand (called battery energy buffer). In these conditions, an almost 100% self-sufficiency of the microgrid can be achieved by a hydrogen generator with the lowest size considered (2.4 kW), a hydrogen storage volume of 10 m3 and a fuel cell, mainly able to completely cover the night loads, of 1.5 kW. This sizing leads to a Levelized Cost of Electricity (LCOE) for the hydrogen section of about 10.5 €/kWh. [Display omitted] • Investigation of a hydrogen storage into off-grid systems characterized by seasonal loads. • Determination of the hydrogen storage sizing through parametric analysis. • Optimal integration with batteries found by imposing an energy buffer equal to 50%. • Through the hydrogen storage, self-sufficiency higher than 99% is achieved. • Significant energy surcharges detected to ensure 100% green electricity. [ABSTRACT FROM AUTHOR]

This paper presents a powerful performance optimization approach for load frequency control in the off-grid mode of a delayed fuel cell microgrid with PI controller. The new approach based on stability boundary locus is used. Thanks to this, the search is only carried out in PI control parameter space keeping stable the system in the proposed optimization. The superiority of the proposed approach in terms of iteration history, fitness values, and time responses of the system is presented by comparing the results obtained. According to the results obtained, the proposed approach reaches the global minimum in a very short number of iterations and achieves this by both guaranteeing the stability of the system and providing the desired time response performances compared to the other method. • The stability of a delayed fuel cell microgrid system is a very important issue, especially in off grid mode. • A delayed fuel cell microgrid system should provide the desired quality of energy to the load. • The optimization approach provides the desired quality of energy to the load ensuring the stability of the microgrid. • Proposed optimization approach has great advantages in terms of iteration history and fitness value. [ABSTRACT FROM AUTHOR]

The production of green hydrogen through electrolysis, utilizing renewable energies, is recognized as a pivotal element in the pursuit of decarbonization. In order to attain cost competitiveness for green hydrogen, reasonable generation costs are imperative. To identify cost-effective import partners for Germany, given its limited green hydrogen production capabilities, this study undertakes an exhaustive techno-economic analysis to determine the potential Levelized Cost of Hydrogen in Germany, Norway, Spain, Algeria, Morocco, and Egypt for the year 2050, which represents a critical milestone in European decarbonization efforts. Employing a stochastic approach with Monte Carlo simulations, the paper marks a significant contribution for projecting future cost ranges, acknowledging the multitude of uncertainties inherent in related cost parameters and emphasizing the importance of randomness in these assessments. Country-specific Weighted Average Cost of Capital are calculated in order to create a refined understanding of political and economic influences on cost formation, rather than using a uniform value across all investigated nations. Key findings reveal that among the evaluated nations, PV-based hydrogen emerges as the most cost-efficient alternative in all countries except Norway, with Spain presenting the lowest Levelized Cost of Hydrogen at 1.66 €/kg to 3.12 €/kg, followed by Algeria (1.72 €/kg to 3.23 €/kg) and Morocco (1.73 €/kg to 3.28 €/kg). Consequently, for economically favorable import options, Germany is advised to prioritize PV-based hydrogen imports from these countries. Additionally, hydrogen derived from onshore wind in Norway (2.24 €/kg to 3.73 €/kg) offers a feasible import alternative. To ensure supply chain diversity and reduce dependency on a single source, a mixed import strategy is advisable. Despite having the lowest electricity cost, Egypt shows the highest Levelized Cost of Hydrogen, primarily due to a significant Weighted Average Cost of Capital. • LCOH projections for 2050 were determined through Monte Carlo Simulation. • PV-based hydrogen is the most cost-effective option in all countries except Norway. • Spain exhibits the lowest LCOH, followed by Algeria and Morocco. • Country-specific WACC variations significantly impact the LCOH. • Importing from Spain, Algeria, Morocco (PV), and Norway (onshore) is advised. [ABSTRACT FROM AUTHOR]

[Display omitted] • The load capacity curve hypothesis for green energy and ecological footprints. • Employ GMM and Panel quantile regression for 2000–2020. • Greener energy consumption (GEC) determines load capacity factor (LCP) unlike to ecological footprint and bio-capacity. • Renewable and Clean energy is recommended to address climatic vulnerability. • Study discusses policy-related implications for sustainable development. This paper examines the load capacity curve hypothesis by the tourism and renewable energy from top tourism economies in the World. We employ the data from 2000 to 2020 and applied the panel GMM and panel quantile regression to arrive at our empirical findings. The results of the two models demonstrate the non-validity of the Load Capability Curve (LCC) hypothesis and the significant role of touristic arrival (TRA) and greener energy consumption (GEC) on the load capacity factor (LCP) by contrasting the ecological footprint per capita and bio-capacity. Furthermore, renewable and clean energy is recommended to address air pollution and climatic vulnerability. Thus, the empirical results of the current study provide acumens for policymakers of top tourism economies to consume green innovation technologies to counterbalance the environmental and socio-economic issues induced by the tourism sector without halting economic growth and sustainable tourism development. The study discusses policy-related implications for sustainable development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Environmental impact modeling under EKC and LCC hypotheses examined. • LM-Bootstrap Cointegration tests and Drisscoll-Kraay regressions applied. • Financial development and load capacity factor are negatively correlated. • Environmental quality is adversely affected by economic expansion. • EKC and LCC hypotheses are valid for BRICS countries. Multiple human activities influence the physical environment, interacting with humans and their surroundings. Our existence depends on various natural resources harvested or extracted from the environment, such as food, materials, and energy. This paper examines the environmental impact modeling and efficient use of nuclear and renewable energy while considering the roles of energy efficiency under the assumptions of EKC and LCC hypotheses in BRICS countries from 1990 to 2018. The long-run estimates are based on LM-Bootstrap Cointegration tests and Drisscoll-Kraay regressions. Financial development has been observed to have a negative relationship with the load capacity factor. However, interestingly, financial development has been found to have a positive relationship with CO 2 emissions. Our findings indicate that the load capacity factor negatively correlates with economic growth in the long run—an increase in economic growth results in a reduction in load capacity. The long-term impact of economic expansion is a decline in environmental quality. Moreover, economic growth square is positive in the load capacity factor and negative in the case of CO 2 emissions. This situation reflects the EKC and LCC hypothesis in the BRICS countries. When income increases, environmental quality initially decreases, and once a certain level of income is reached, ecological sensitivity and green technologies increase the load capacity factor. The negative coefficient value in the case of economic growth and the positive coefficient value of the square root of economic growth determines the LCC hypothesis, which is U shape relationship. On the other hand, the negative coefficient value of economic growth followed by the positive coefficient value of the square root of economic growth determines the existence of the EKC hypothesis in the BRICS countries, which is an inverse relationship. Research limitations and future directions are discussed. [ABSTRACT FROM AUTHOR]

[Display omitted] • The determinants of the load capacity factor are investigated in top SDGs nations. • Distributional heterogeneity controlled using novel Method of Moments Quantile Regression. • Technology, renewable energy, globalization and environmental tax increase the load capacity factor. • Economic growth curb load capacity factor. • Interaction between renewable energy and technology innovation reduces load capacity factor level. The acceptance of Sustainable Development Goals (SDGs) in 2015 shifted the consideration toward sustainability-linked aspects in both developed and developing nations. Currently, the most critical ecological and economic problems are associated to ecological contamination and sustainability. Thus, this current study employs load capacity factor (LCF), a comprehensive ecological assessment tool that captures both the ecological foot (demand side) and biocapacity (supply side) of the ecosystem to scrutinize the effect of environmental taxes, technological innovation and renewable energy on LCF while regulatory for globalization and economic progress in the case of top ten SDGs nations. The paper used MMQR to capture the influence of each variable at different quantiles from 1994 to 2018. The study's outcome shows that technology innovation, environmental Tax, renewable energy and globalization are negative and significant in reducing LCF within the countries. Again, the study exposed that economic growth contributes positively to LCF. However, the interaction term between technology innovation and renewable energy upsurges the LCF across all the quantiles. Moreover, the panel Dumitrescu and Hurlin causality test results revealed a one-way causality exists between economic growth and LCF, running from economic growth to LCF. The outcomes of this research will have a significant impact on the route that policymakers in these nations take in reaching net zero emissions. [ABSTRACT FROM AUTHOR]

Liquid organic hydrogen carriers (LOHC) can be used as a lossless form of hydrogen storage at ambient conditions. The storage cycle consists of the exothermic hydrogenation of a hydrogen-lean molecule at the start of the transport, usually the hydrogen production site, becoming a hydrogen-rich molecule. This loaded molecule can be transported long distances or be used as long-term storage due to its ability to not lose hydrogen over long periods of time. At the site or time of required hydrogen production, the hydrogen can be released through an endothermic dehydrogenation reaction. LOHCs show similar properties to crude oils, such as petroleum and diesel, allowing easy handling and possibilities of integration with current infrastructure. Using this background, this paper reviews a variety of aspects of the LOHC life cycle, with a focus on currently studied materials. Important factors such as the hydrogenation and dehydrogenation requirements for each material are analysed to determine their ability to be used in current scenarios. Toluene and dibenzyltoluene are attractive options with promising storage attributes, however their dehydrogenation enthalpies remain a problem. The economic feasibility of LOHCs being used as a delivery device were briefly analysed. LOHCs have been shown to be the cheapest option for long distance transport (>200 km), and are cheaper than most at shorter distances in terms of specifically transport costs. The major capital cost of an LOHC delivery chain remains the initial investment for the raw materials and the cost of equipment for performing hydrogenation and dehydrogenation. Finally, some studies in developing the LOHC field were discussed, such as microwave enhancing parts of the process and mixing LOHCs to acquire more advantageous properties. • Efficient hydrogen storage solution for sustainable energy transportation and storage. • Enables safe and cost-effective hydrogen transportation and distribution networks. • Promotes renewable energy integration through versatile and scalable storage capabilities. • Facilitates decarbonization efforts by enabling long-term, stable hydrogen supply chains. [ABSTRACT FROM AUTHOR]

A range of existing and newly developed hydrogen standards, certification and labelling (SCL) schemes aim to promote the role of 'renewable', 'clean' or 'green' hydrogen in decarbonising energy transitions. This paper analyses a sample of these SCLs to assess their role in the scaling up of renewable hydrogen and its derivatives. To analyse these hydrogen SCLs, we embellish a novel conceptual framework that brings together Sustainability, Systems Thinking, and Governance (SSG) literatures. The results reveal noteworthy scheme differences in motivation, approach, criteria, and governance; highlighting the complex, interconnected, and dynamic reality within which energy systems are embedded. We consider whether the sustainable utilisation of renewable hydrogen is well-served by the proliferation of SCLs and recommend an SSG-informed approach. An SSG approach will better promote collaboration towards an authoritative, global, multistakeholder, compromise on hydrogen certification that balances economic considerations with social, and environmental dimensions. [Display omitted] • There is a proliferation of hydrogen standards, certification and labelling (SCL) schemes. • We analyse a sample of SCLs to investigate their role in scaling green hydrogen. • We employ a Sustainability, Systems Thinking, and Governance (SSG) approach. • We recommend an SSG lens to help craft a global multistakeholder initiative. [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 investigates an innovative study on the performance of a hybrid condensing boiler-fuel cell micro-cogeneration system for single residential applications. It combines experimental and dynamic numerical investigations including renewable energy for green hydrogen production and solid storage. Basing on the hourly measurements for a typical winter day, a dynamic model was developed and validated against different home heating need. Highlighting the influence home energy needs on fuel cell operating mode, it shows that single homes with low energy demand led to reduce the fuel cell's yearly operation below 50 %. In low-energy homes, a reduction in fuel cell size led to an increase in yearly operation to 60-70 % and achieved a 55 % electrical coverage rate. The integration of metal hydride hydrogen solid storage, powered by renewable sources, is emphasized. This setup increases heat demand, influencing fuel cell operation. In winter season, fuel cell operated for an average of 611 h, compared to 61 h in summer. System efficiency averaged 91 % annually, peaked at 95 % in summer, and sustained 93 % when both FC and condensing boiler were switched on. [Display omitted] • Modeling of a hybrid Boiler-Fuel Cell MCHP system for residential energy management. • Calibration of the FC-MCHP model using experimental data from a real MCHP unit. • Electrical and thermal coverage of the FC-MCHP unit with green hydrogen production and storage under different scenarios. • Strategies for optimal hydrogen consumption in in residential application. • Impact of solid-state hydrogen storage on FC-MCHP consumption and thermal management. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and use of a control system for a renewable energy production plant based on hydrogen. The proposed control system aims at ensuring the stability and smooth functionality of the plant, which consists of a (i) photovoltaic system connected to an electrolyzer through a battery, (ii) a DC/DC step down transformer, and (iii) an electrolyzer heat exchange system. In this study, solar irradiance is the main system input, and hydrogen production the main output. Since the system utilizes solar energy as input, it depends on the random input of solar irradiance, ambient temperature, and wind flow. Furthermore, the electrolyzer's functionality is subject to several operational variables including cell voltage, current, temperature, and pressure. The electrolyzer heat exchange system operates at specified water temperatures and flow rates. The DC/DC output voltage, and therefore the voltage supplied to the electrolyzer, is regulated by changing its duty cycle. To regulate hydrogen production in the renewable energy production plant, an efficient control system is required. Accordingly, in this work, a control system is designed accounting for three different electrolyzer technologies, alkaline, PEM (proton exchange membrane), and E-TAC (electrochemical - thermally activated chemical water splitting). Subsequently, the effectiveness of the control system is analyzed using Matlab and Simulink models. The main results indicate that the battery is a crucial element in the whole system as it supplies the necessary energy to the electrolyzer. By regulating the appropriate components, the proposed control system proved capable of minimizing power fluctuations and increasing system efficiency up to 20% depending on ambient conditions. Additionally, the results indicate E-TAC and PEM efficiencies 13% and 7% higher than alkaline, respectively. [Display omitted] • Design of a control system for three different electrolyzer technologies. • Use of control systems for electrolyzer voltage and heat exchange. • Photovoltaic system connected to an electrolytic one through a battery. • Proposed control system increases system efficiency up to 20%. [ABSTRACT FROM AUTHOR]

The development of sustainable energy sources has become a major challenge for society. Green hydrogen, produced through the electrolysis of water using renewable energy sources, offers a potential solution to reducing our dependence on fossil fuels. The paper examines the integration of green hydrogen into various sectors, such as transportation, industry, power generation, and heating, highlighting its potential to decarbonize traditionally carbon-intensive areas. Furthermore, it analyses the strategies and policies employed by the European Union, Australia, Japan, the United States, and Canada to drive the development and adoption of green hydrogen technologies. The challenges and barriers that need to be addressed to fully realize the potential of green hydrogen, such as technological limitations, infrastructure development, costs and economic feasibility, regulatory and policy frameworks, and public perception and acceptance, have been investigated. Recommendations for overcoming these challenges and accelerating the adoption of green hydrogen technologies are provided, and the importance of research and development in this sector is emphasized. • Green hydrogen offers a sustainable solution to reduce fossil fuel dependency, decarbonizing key sectors. • Analyzed policies from the EU, Australia, Japan, the US, and Canada to foster green hydrogen technologies. • Discussing challenges: green hydrogen potential, tech limits, infrastructure, costs, regs, and public views. • Emphasized the importance of R&D and offers accelerating the adoption of green hydrogen technologies. [ABSTRACT FROM AUTHOR]

In this paper, a novel stochastic risk-averse mixed-integer linear programming (MILP) model is developed for optimal electricity procurement of hydrogen fuel stations (HFSs) with responsive hydrogen demands. In the developed model, HFS operator may procure its required electricity through day-ahead (DA) market, bilateral contracts, a contract with withdrawal penalty (CWP) and balancing market. The HFS is committed to inject a pre-specified volume of hydrogen into a natural gas network. In the developed stochastic model, the uncertainties in hydrogen demand and DA market prices are characterized as random variables and modeled as scenarios. The developed model is a two-stage model in which the decisions on transactions with bilateral contracts are the first-stage decision variables and the other decision variables belong to the second stage. The risk of suffering from high procurement costs is modeled using the Conditional Value at Risk (CVaR). The results confirm the efficiency of the proposed model both in terms of reduction in HFS expected cost and risk alleviation. The results show that the transaction through bilateral contracts decreases the expected cost and CVaR respectively by 6% and 3%. The results also indicate that the participation in the balancing market decreases the expected cost and CVaR respectively by 6% and 1.6%. As per results, in scenarios in which DA market prices are lower than CWP prices, HFS operator prefers to pay a penalty fee and withdraw from CWP. The sensitivity of HFS profit to the weight factor of risk metric and the participation factor of responsive demands are assessed. [Display omitted] • A model is developed for electricity procurement of hydrogen fuel stations (HFSs). • The uncertainties in demands and market prices are considered. • HFS procures electricity by day-ahead and balancing market and bilateral contracts. • Impact of different markets and transactions on HFS profit is assessed. • Sensitivity of HFS profit to weight of risk metric is assessed. [ABSTRACT FROM AUTHOR]

Renewable energies are available as clean sources to replace fossil fuels. Providing continuous power without compromising the environment through hybridizing solar and biomass source is one of the promising solutions. This paper seeks to address thermodynamic, economic, and environmental analysis of a multi-generation system with the aim of supplying heating, cooling, electricity, fresh-water, and hydrogen. To do this, the considered system is divided into on-peak, mid-peak, and off-peak periods and modeled based on the consumption pattern at different hours of the day and night. Due to the availability of renewable energy during peak consumption periods, biomass, solar, and hybrid biomass-solar energies are used as energy sources. This leads to a reduction in biomass consumption and carbon dioxide emissions through storing approximately 10 tons of biomass during the day. A sensitivity analysis of the factors affecting the system's function indicated that a growth in solar radiation from 600 to 1000 W/m2, results in a 35% improvement in exergy efficiency, a 2.7% raise in the total cost rate, and a 32.4% drop in CO 2 emissions of the system. Moreover, by changing the biomass flow rate from 0.5 to 1.5 kg/s, the exergy efficiency and total cost rate improved by 9.28%, 1.3%, respectively, and the CO 2 emissions rate increased from 0.41 to 1.14 tons/MWh. In addition, focusing on addressing economic and environmental concerns, the optimization of the proposed hybrid system is performed in two categories of objective functions. In the second category optimization, exergy efficiency, fresh water production, total cost rate and CO 2 emissions are determined as 31.75%, 74.75 kg/s, 324.60 $/h and 3.55 tons/MWh, respectively. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Renewable energy generation on energy security in G7 countries. • The panel ARDL, QQR, and Dumitrescu-Hurlin causality analyses were employed. • The impact of renewable energy generation on energy security was analyzed. Energy security has been brought into the foreground of political and economic agendas nowadays. However, this variable has been only in very few papers employed as dependent or independent variable. In most research so far, energy security is implied and is proxied by certain variables. The aim of this study is to investigate the relationship between energy generation from renewable energy sources and energy security in the G7 countries for the period 1980–2018 by using the panel autoregressive distributed lag, panel quantile-on-quantile regression and the Dumitrescu-Hurlin panel Granger causality analyses. The results reveal that there is a uni-directional causal link from renewable energy to energy security in the short-run and generating energy from renewable sources significantly reduces the risks that threaten energy security in the long run. Furthermore, quantile-on-quantile regression estimates indicate that, for the quantile above 0.5 in terms of renewable energy generation, energy security risks are reduced, and secure access to energy is strengthened in the G7 countries. The diverse implications generated by each methodology are particularly relevant today, given the current energy crisis in Europe and the pressing need to increase the share of renewable energy generation to mitigate energy security risks. [ABSTRACT FROM AUTHOR]

Design of hybrid Nano-Grid system (HNGS) necessitates a careful thought to benchmark reliable and cost effective solutions for remote areas. Optimal sizing of HNGS using Honey Badger Algorithm (HBA) for futuristic residential area in Mahendragarh, Haryana, India is presented in this paper. The HNGS comprise combination of solar panels, wind turbines, storage batteries, diesel generator, inverter and residential load components. The primary objective functions considered in this paper are Cost of Electricity (COE) and Net Present Cost (NPC), in continuation these problems are stated as multi objective optimization issues. Numerous permutations of diverse resources were examined to assess parameters such as NPC, COE, battery storage, land utilization, emission and potential employment opportunities. HBA is almost independent of traps in the local minimum, therefore the results obtained are superior as compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Biogeography Basic Optimization (BBO), Improved Artificial Ecosystem Optimization (IAEO) and Grey Wolf Optimization (GWO) as well as HOMER software, in order to determine the quality of solution the proposed HBA algorithm has obtained. HNGS system can be readily and efficiently optimized using the suggested HBA algorithm a detailed sensitivity analysis is done to better understand the system behavior for a wider region use. • A well optimized HNGS design is cost-effective, highly reliable and simple system for remote areas. • An optimization problem is formulated and solved by means of Honey Badger algorithm. • The optimization aim is to find HNGS size in terms of number of individual sources which supplies demand successfully. • Outcomes show that HBA effectively optimizes HNGS by lowering the NPC and CoE, ensuring the electricity supply. • The HBA's results has been compared to GA, PSO, BBO, IAEO, and GWO, as well as HOMER software. • Sensitivity analysis has been used to explore the impact of weather and component parameters on COE and NPC. [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]

As stated in Sustainable Development Goals number 6 and 13, clean water and sanitation and energy-related carbon emissions as climate action issues have emerged as serious issues within the United Nations. Around 150 countries rely on seawater desalination plants as their water resource. Reverse osmosis membrane technology is the most widely used seawater desalination technology for water supply. Taking water resources, developing renewable energy, and recycling resources have become the United Nations' core sustainable development goal. Therefore, this paper aims to provide an overview of renewable energy in reverse osmosis, especially newly developed renewable energy such as wave power. This paper includes technical issues, environment, and economic impact. In addition, compared with other energy sources, photovoltaic and hydropower energy utilization leads to a lower carbon footprint. Both energy produce a carbon footprint of around 0.08 kg CO 2 eq/m3. Control systems and energy storage management are the challenges in developing integrated renewable energy with reverse osmosis. Suitable pretreatment and the effect of direct use of renewable energy on the reverse osmosis process, especially on membrane and pump lifetime, should also be considered in future studies. [Display omitted] • Fluctuations and intermittence in wind and PV would affect on the process performance. • Energy storage management helps to keep the system stable • Water storage can assist in having enough water and store extra • PV and hydropower energy produce carbon footprint around 0.08 kg CO 2 eq/m3. [ABSTRACT FROM AUTHOR]

The energy sector prioritizes innovative clean energy production. This research explores a versatile hybrid solar-biomass trigeneration system, integrating organic Rankine cycle (ORC), a heat pump, and a proton exchange membrane electrolyser. Sustainable and adaptable, it produces electricity, heating, and hydrogen for Indian paper and pulp industries. The study assesses the system's thermodynamic, economic, and environmental feasibility. Initially, parametric analysis and first layer multi-objective optimization (MOO) are performed to select the efficient working fluid and operating temperatures. Benzene outperforms other fluids, achieving an energy utilization ratio (EUR) of 0.27 and exergy efficiency of 75 %. A typical heat requirement of 250 kW for processing 150 kg of paper necessitates collector areas of 3500 m2 and 4500 m2 in Aurangabad and Tiruchirappalli, respectively, and biomass waste of 0.05 kg/s. The combined system performance during day time and night time operation was studied by altering the critical solar radiation, ambient temperature and biomass waste flow rate. The second layer MOO revealed an optimum EUR of 0.25 and exergy efficiency of 9 % at the lowest cost of 33 $/h for biomass waste operation. In comparison to the conventional electricity-powered system, the proposed system would reduce CO 2 emissions by 90 kg/h to 270 kg/h. [Display omitted] • Solar and biomass waste powered systems proposed for paper and pulp applications. • ORC integrated heat pump system for heating, electricity, and green H 2 generation. • Collector area and biomass flow rate are designed for daily paper output of 300 kg. • Benzene is an efficient working fluid, produced 815 kW of heat and 3 kg/h of H 2. • The recommended system reduced CO 2 emissions by 90 kg/h to 270 kg/h. [ABSTRACT FROM AUTHOR]

Growing demand for electricity due to economic development contributes to increased greenhouse gas production, especially CO 2. However, emissions can be limited by enhancing the efficiency of primary energy conversion, such as integrating geothermal energy into coal-fired power plants. Therefore, this paper proposes replacing conventional feed-water heaters with geothermal preheaters to create a hybridized system. This study was based on a numerical model validated at a selected Polish power unit. The model was subsequently calibrated for off-design conditions to facilitate partial load analysis. The obtained characteristics outperformed those of the non-hybrid unit, generating over 18 MW of electric power output. Such an improvement could potentially boost the unit's net efficiency by more than 2.6 %. This enhancement is significant as power units typically operate under part load for approximately 90 % of the time, hence the need to evaluate the performance characteristics of hybridized units in those states. Furthermore, the research outlines the potential decrease in the plant's CO 2 emission factor, with reductions reaching up to 6.5 % under off-design conditions. Based on a gap analysis of the existing literature, this paper's comprehensive partial load evaluation serves as a new addition to research on hybridized systems. • An in-depth analysis of feed-water heaters operation is performed. • Turbine performance calculations for both design and off-design states is validated. • The comprehensive study of hybridized systems integrates three major factors. • Hybrid geothermal-fossil power cycle is vital for specific CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

In recent times, the escalating global demand for sustainable and renewable energy sources has catalyzed the exploration and development of innovative technologies, among which floating photovoltaic (FPV) systems emerge as a particularly promising solution. These systems exploit solar energy by deploying PV panels on water surfaces. These systems, offer several advantages, including their independence from land use constraints, enhanced energy efficiency due to the cooling effect of water, and the potential for synergy with various energy sources. Expanding on prior literature reviews, this paper provides a focused review of the latest developments in FPV systems, cutting-edge technologies, challenges faced the FPV in marine settings, extending to an economic analysis for comprehensive feasibility assessment, and market potential from diverse angles. The primary objective is to promote additional research and applications of FPV to guarantee the safety and stability of FPV systems under challenging marine environmental conditions, aiming to enhance overall efficiency, reliability, and the widespread adoption of FPV projects, while concurrently reducing investment costs. Highlighting its importance for energy-deficient African countries, the paper underscores the significance of integrating FPV systems into the continent's energy strategies for widespread adoption, to bolster energy security and environmental sustainability across the continent. [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]

This paper utilizes the skill-biased technological change and routine-biased technological change hypotheses, along with the theory of firms' innovation behavior, to establish a comprehensive analytical framework. This framework aims to elucidate the mechanisms through which technological innovation, industrial evolution, and education affect the labor skill structure in China manufacturing. The empirical results that have been gathered can be succinctly described in the following manner: (1) Research and development innovation stimulates the demand for high-and low-skilled labor, and, the rising of total factor productivity reduces the demand for low-skilled labor, confirming the labor-friendly effect of product innovation and the labor-saving effect of process innovation. (2) The cyclical pattern of fluctuating demand for low-skilled labor can be attributed to the industrial concentration, driven by technological advancements. Nevertheless, the phenomenon of cyclicality has not been observed for high-skilled labor. (3) There exists a paradoxical relationship between higher education and industrial demand, wherein education and skill are mismatched. (4) There exists the SBTC in China manufacturing resulted from process innovation. However, the SBTC does not explain the cause or mechanism behind the relative productivity shift and the higher demand for educated workers. This paper examines several policy implications including R&D innovation, employment, and education. • SBTC/RBTC and firms' innovation behavior theories are incorprated in a unified framework. • R&D inputs promote the demands for high- and low-skilled labor, and rising TFP reduces the demand for low-skilled labor. • Industrial concentration leads to a cyclical pattern of the demand for low-skilled labor rather than for high-skilled labor. • There exists a paradoxical relationship between higher education and skill demand. [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]

Carbon emission flow is an effective tool to obtain carbon emission distribution in power systems, which can guide the active carbon reductions of consumers through proper incentive schemes. With the increasing penetration of renewable energy, the existing single-valued carbon emission flow model based on deterministic forecasted power outputs cannot describe the impacts of renewable energy uncertainties on carbon flows. Therefore, this paper mainly focuses on the assessment of the probabilistic carbon emission flow through the power systems considering the impacts of load and renewable energy uncertainties. In this paper, the probabilistic carbon emission flow (PCEF) is innovatively proposed. With the PCEF, consumers can make better energy use plans considering their own risk appetites for carbon emission payment. Firstly, the impact factors of the PCEF are modeled, including load and renewable energy uncertainties and integrated carbon intensity of thermal plants. The uncertainties of load, wind power and photovoltaics are modeled based on a p-box method while the uncertainty of hydropower is modeled based on the multi-state model. Besides, the integrated carbon intensity model of thermal plants are proposed considering the carbon intensity variations during the operating, start-up, and shut-down stages. Furthermore, the probabilistic carbon emission evaluation framework is proposed to assess the probabilistic carbon distribution. In this framework, novel probabilistic carbon indices are defined to precisely characterize the carbon emission situation with probabilistic representations. A solution method based on an adaptive interval point estimation (AIPEM) algorithm is proposed to efficiently solve the developed evaluation problem. Tests on an IEEE-30 node system, and a real provincial power system in China validate the effectiveness and application of the proposed model. The results showed that the PCEF can be a useful tool for obtaining the probabilistic representation of carbon emission flow and guiding the consumers' active carbon reduction in power systems. • Probabilistic carbon emission flow is proposed considering multiple uncertainties. • Integrated carbon intensity model for thermal plants is developed. • A p-box model is used to describe the uncertainties of load and renewable energy. • PCEF can help consumers balance the cost and carbon risk when making decisions. [ABSTRACT FROM AUTHOR]

Given the increasing penetration of Inverter-Based Resources (IBR) and the impacts of these generators on existing fault location functions, this paper explores the potential of Machine Learning (ML) for designing one-terminal fault locators applied to systems with IBRs. For the studies, a system with a widely used topology for IBR interconnection to a transmission grid is modeled in PSCAD software, considering fault scenarios on the interconnection line with varying fault parameters, in addition to different IBR controls/topologies, grid short-circuit levels and signal noise levels. Besides the investigations on correctly choosing the attributes extracted from the signals for training ML methods, which introduces the concept of learning based on the existing method's response, the studies cover performance assessments of 35 ML methods for the fault location task, indicating the most suitable ones, and studies about the influence of the training set size on the ML methods performance. Moreover, comparative performance analyses are conducted between the ML-based methodology and state-of-the-art fault locators, highlighting the benefits of the ML-based approach in reducing errors obtained in the fault location task. Finally, the paper recommends stages for applying ML-based methods to real systems, besides providing insights and recommendations on such practical applications. • IBR significantly impacts traditional one-terminal phasor-based fault locators. • Machine learning methods can be adopted to minimize errors in the fault location task. • A new learning concept based on the response of existing methods is presented. • The practical implementation bias of machine learning methods is explored. [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]

A successful and just energy transition is subject to policies that adhere to a multi-dimensional approach to renewable energy development. To achieve success in renewable energy development, well-thought-out and efficient policies must be implemented. This paper aims to address a major shortcoming in renewable energy development in terms of understanding and recognising the policy space that promotes or hampers renewable energy growth. The paper examines the relevant literature to identify major themes and factors in renewable energy development. A thematic analysis was conducted using a hybrid inductive and deductive approach and resulted in the identification of 5 major themes namely Institutional, Environmental, Financial, Socio-cultural, Technical and 144 factors. The identified themes and factors will assist to make a policy action decisive, just, and sustainable. Recommendations including comprehensive incentivisation, regulatory support for renewable energy technologies in developing nations, environment impact assessment programs, promotion of financial transparency and opting for a just transition to benefit local and vulnerable communities. [ABSTRACT FROM AUTHOR]

Green ammonia, a promising zero-carbon energy storage vector, has never been produced using solely tidal stream energy despite its predictable characteristics. Combining tidal stream sites with a phase difference (tidal phasing) facilitates anticorrelation between these sites' power profiles, enabling a more consistent power output. Green ammonia production benefits from a steady and predictable power input. The objective of this paper is to determine if nearby tidal sites have a large enough phase difference (anticorrelation) to reduce the cost of ammonia production. Moreover, producing green ammonia from underutilized or unutilized remote and grid-constrained tidal stream sites may enable viability of these sites. This paper is the first analysis of green ammonia production using solely tidal stream energy and the first analysis of tidal phasing for green fuel production. Two UK case studies are presented in i) the North Channel of the Irish Sea and ii) Orkney. There are tidal sites in each which have a phase difference (e.g. the Mull of Kintyre/Sound of Islay, and Graemsay/North Ronaldsay with correlation coefficients of 0.17 and −0.02 respectively), utilising this phase difference reduces the levelized cost of ammonia by 8% in the Irish Sea and by 12% in Orkney. [ABSTRACT FROM AUTHOR]

In the last years, the massive deployment of new photovoltaic (PV) power plants has launched the connection of PV inverters to the electrical network. A single medium-sized ground-mounted PV plant may have thousands of these inverters linked to the grid and even more PV panels on the DC side. Upon reaching such a substantial magnitude of devices involved in grid-connected installations, the effective operation, management, predictive maintenance, and fault detection becomes increasingly challenging without integrating advanced prediction and automated anomaly detection systems. Artificial intelligence algorithms, grounded in data measurements, can be pivotal in addressing this challenge. This paper proposes several regression-based methods to predict PV plants' energy generation, which is useful for detecting transient and long-term anomalies. These models are trained using a Recursive Least Squares (RLS) method and require a minimum number of variables to yield satisfactory outcomes, which is one of the paper's contributions. They mainly rely on energy generation measurements and geolocation. Within the scope of this research, two distinct algorithms have been implemented and validated. The first algorithm, a simplified model, is engineered to analyse the daily efficiency variation, prioritizing the identification of faults and abnormal operational profiles in PV plants. On the other hand, the second algorithm adopts a more intricate model tailored to facilitate long-term diagnosis, enabling the assessment of PV efficiency degradation. In this work, both algorithms are described and their performance is validated using the historical data from more than 20 PV plants placed in different climatic regions. • Photovoltaic systems' modelling using linear regression models. • Photovoltaic fault detection using linear regression coefficients evaluation. • Automatic fault detection for photovoltaic plants with low sensors' availability. • Short-term and long-term efficiency trend evaluation of photovoltaic plants. [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]

Decarbonising the heating sector is one of the key elements to realizing the ambitious dual carbon goals of China, which is the largest carbon emitter and energy consumer globally. Currently, district heating (DH) systems have penetrated approximately 88% of the urban heating areas in Northern China. Nevertheless, around 90% of the heating demand in China still relies on fossil fuels. A larger scale integration of renewable energy and waste heat sources into the DH systems is critical for decarbonising the entire heating sector in China. However, a deeper level of comprehension is required to harness its full potential. This paper provides a thorough investigation of the status, potential, and national policy schemes of renewable energy and waste heat recovery in the DH systems of China. Combined with a critical review of recent literature on relevant areas published in both international and Chinese domestic sources, the trends, challenges, and future perspectives are discussed from scientific research and practical implementation aspects. This paper highlights the synergy of the integration of renewable energy and waste heat sources in DH, the energy efficiency improvements as well as the use of thermal storage technologies through the implementation of 4th generation district heating and smart energy systems that could offer a more economically viable pathway forward. • Focus on renewable energy and waste heat (REWH) in the district heating (DH) systems in China. • A systematic review of REWH in Chinese DH systems from status, policy, and literature aspects. • Identification of research gaps based on the review of both international and Chinese journals. • Policy recommendations proposed to promote REWH in Chinese DH systems. • The introduction of international experiences on REWH in DH for analysis in the Chinese context. [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]

Rapid growth in population and economic development in Iran and its neighboring countries has resulted in a dramatic increase in electricity demand over the past few years. A substantial amount of electricity must be produced to meet this growing demand in this region. By installing conventional fossil power plants, using renewable energy, and interconnecting with neighboring countries, this growing demand can be accomplished. The main aim of this research is to identify prospects and challenges in the electrical sector around Iran. To this end, this paper evaluates the existing and future state of the electrical sector in Iran and its neighboring countries. Despite the fact that there is still a long way to go in terms of renewable energy in this region, there are some promising targets for the future that will allow fossil fuels to be replaced effectively. Notwithstanding this, we should point out that a number of cross-border interconnection projects are being pursued by Iran and its neighbors in order to integrate their networks and supply this growing demand. Finally, this study presents an overview of the future prospects and challenges of electricity capacity expansion, cross-border interconnections, and electricity transmission corridors in this region. The review results provide comprehensive background knowledge to all the researchers in order to investigate further into the field. This paper assists energy policy makers regarding the development of sustainable energy planning. • •In addition to providing a general overview of the geographical location, population growth, and economic indicators of Iran and its neighboring countries, this paper presents the current state of the Iranian electricity sector and its neighboring countries in light of the trends in electricity production, energy mix, price, and exports and imports. • •This paper explores the existing and future states of electricity capacity expansion in the region, specifically focusing on renewable energy sources. • •The existing and future states of cross-border electricity interconnections are examined to present a comprehensive overview of the cross-border interconnections in this region. • •This region's potential electricity transmission corridors are proposed to create the opportunity to transmit electricity across countries. [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]

In the background of exhaustion of the traditional fossil energy sources and environmental deterioration, developing renewable energy has become a strategic choice for countries to achieve energy sustainable utilization and carbon neutrality target. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. However, the commonly used weight calculation methods in the existing renewable energy evaluation have obvious shortcomings, such as the randomness of the subjective method is strong and the gap in the weight allocation of the objective method is too large, which affect the reliability and accuracy of the sorting result. In this paper, a novel weight calculation method based on non-dominated sorting genetic algorithm (NSGA-II) is proposed, and is applied to renewable energy evaluation. The study in this paper focuses on methodology. Firstly, for the linear normalized and Zeros normalized evaluation data, the differences between renewable energy plans in the two normalized evaluation data are measured, and the ranking deviations of the two normalized data are calculated. Then, the multi-objective weight solving model is constructed in terms of plans differences minimization and ranking vectors deviations minimization. The NSGA-II algorithm is applied to the optimization of multi-objective functions to find the Pareto non-dominated solution, so as to determine the weight values of each evaluation index. Finally, the TOPSIS method is used to determine the relative closeness value of each plan, and the rankings of renewable energy technical plans are achieved. Based on the actual renewable energy development data of 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 popular renewable energy evaluation methods. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Apply non-dominated sorting genetic algorithm II to determine the index weights. • Compared with the existing evaluation method, the proposed method performs better. [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]