Thermophotovoltaic (TPV) devices are solid-state energy conversion systems that generate electricity from infrared thermal radiation emitted by a high-temperature source. These systems have significant potential for energy storage, industrial waste heat recovery, and renewable energy applications, particularly in concentrated solar power and nuclear reactors. Despite advances in TPV efficiency, the challenge of increasing power density has remained largely unaddressed, restricting the practical implementation of TPVs in moderate-temperature applications (700–1100°C). Current TPV technologies primarily rely on far-field configurations, where a macroscopic vacuum or gas-filled gap separates the thermal emitter and photovoltaic (PV) cell. However, this setup fundamentally limits power density due to Planck’s blackbody radiation law, which governs thermal emission. Near-field TPV has been proposed as a solution, leveraging evanescent wave coupling across nanometric vacuum gaps to enhance radiative heat transfer. However, near-field TPV suffers from practical challenges, including fabrication complexity, stringent surface smoothness requirements, and scalability limitations.  Another critical limitation in current TPV designs is the reliance on ultra-high temperatures to boost power output. Most TPV devices with high power density operate at emitter temperatures exceeding 1500°C, requiring materials with extreme thermal stability. These high temperatures contribute to rapid material degradation, reducing system longevity and making large-scale implementation challenging. Additionally, efficient TPV energy harvesting at lower temperatures remains an unsolved problem, preventing TPVs from being effectively integrated into many industrial processes where moderate temperatures dominate.

To this account, new research paper published in Energy & Environmental Science and conducted by Dr. Mohammad Habibi, Sai Yelishala, Yunxuan Zhu, and Professor Longji Cui from the University of Colorado Boulder together with Dr. Eric Tervo  and Dr. Myles Steiner from the National Renewable Energy Laboratory developed a novel approach: the zero-vacuum-gap TPV (z-TPV) concept. This architecture eliminates the vacuum gap and replaces it with a high-index, thermally insulating dielectric spacer, such as fused quartz. By allowing the transmission of high-wavevector modes previously inaccessible in conventional far-field devices, this method enables significant power density enhancements without requiring extreme emitter temperatures or complex nanofabrication techniques. Unlike near-field TPV, which relies on vacuum-separated nanogaps, z-TPV provides a practical solution for large-area, manufacturable devices while still harnessing high power output. To experimentally validate the zero-vacuum-gap TPV concept, the researchers fabricated and tested TPV devices using fused quartz as the spacer, tungsten and graphite as the emitters, and InGaAs as the PV cell. The experimental setup involved heating the emitter using an electrical heater while measuring the power generation and heat flux at different emitter temperatures (700–1100°C). Power output was compared between conventional far-field TPV devices and the newly designed z-TPV system. Device fabrication followed a multi-step process to ensure accurate structural integrity. Thick emitter films of tungsten and graphite were sputtered onto end-polished fused quartz rods with a roughness of approximately 1–5 nm. These rods were then bonded to the PV cells using an optically transparent epoxy layer (10 µm thick), ensuring an effective gapless interface between the dielectric spacer and the PV cell. A ceramic glue was applied to secure thermal contact between the heater and the emitter, while thermocouples were positioned to monitor temperature stability. A microchannel cold plate heat exchanger was used to maintain PV cell temperature, reducing heat accumulation that could otherwise degrade performance.

The authors demonstrated a two-fold increase in power density in z-TPV compared to the traditional far-field configuration. Remarkably, the graphite emitter surpassed the blackbody limit for conventional gap-integrated far-field TPVs, achieving power densities comparable to near-field TPV devices with a 200-nm vacuum gap. The enhancement was attributed to the ability of the dielectric spacer to convert evanescent waves into propagating waves, thereby increasing radiative energy transfer.  To ensure robustness, the researchers measured current-voltage (J–V) characteristics using a four-wire method. The maximum power output was calculated using P_max = I_sc * V_oc * FF, where I_sc represents the short-circuit current, V_oc the open-circuit voltage, and FF the fill factor. The results showed that even at lower emitter temperatures, z-TPV achieved significantly higher current densities than conventional far-field TPV, demonstrating its suitability for moderate-temperature applications. Further analysis revealed that the z-TPV concept offers a scalable and cost-effective solution for increasing TPV power density. By optimizing the spacer material, such as using amorphous silicon (a-Si) instead of fused quartz, the researchers predicted an order-of-magnitude improvement in power output. Simulations indicated that z-TPV could achieve power densities equivalent to near-field TPV with a 25-nm vacuum gap, a scale that is virtually unattainable with existing microfabrication techniques.

A key limitation identified in z-TPV was heat conduction through the dielectric spacer, which contributed to energy loss and potential PV cell heating. However, extending the spacer length from 2 cm to 10 cm significantly mitigated conductive losses, improving the overall energy conversion efficiency to nearly 19%. Additional thermal insulation layers were incorporated to minimize unwanted conduction, and alternative spacer materials with lower thermal conductivity were explored through modeling. Future optimizations include refining the thickness and optical properties of the spacer to balance power density and efficiency further.

 The findings of this study represent a fundamental shift in TPV technology, demonstrating that high power densities can be achieved without requiring ultra-high emitter temperatures or complex near-field architectures. By introducing the zero-vacuum-gap TPV concept, this work provides a scalable and manufacturable alternative to existing TPV technologies, offering a practical route for waste heat recovery and renewable energy applications.  In real-world applications, z-TPV has the potential to unlock previously inaccessible heat sources, such as moderate-temperature industrial processes (chemical, steel, and cement industries), residential heat cogeneration, and space-constrained energy storage systems. The ability to generate high power density at lower emitter temperatures can also extend TPV viability to lightweight and portable power generation systems. Given its compatibility with large-area manufacturing, z-TPV could be integrated into waste heat harvesting in industrial plants, significantly improving energy efficiency and reducing carbon emissions. Future research should focus on optimizing spacer materials to further enhance power output and efficiency. Amorphous silicon, for example, could enable a 17-fold power density enhancement, rivaling the best near-field TPV devices. Additionally, integrating high-efficiency PV cells with minimized optical and thermal losses could push z-TPV efficiency beyond 30%, making it a viable competitor to traditional energy conversion methods. Another crucial direction is improving spectral control to reduce sub-bandgap photon losses in the dielectric spacer. Selective emitters, structured metasurfaces, and embedded optical filters could help refine the emission spectrum, ensuring better spectral matching with the PV bandgap. Combined with new PV materials featuring ultra-low bandgap energy, z-TPV could be adapted for lower-temperature energy harvesting, broadening its application scope. Ultimately, the introduction of zero-vacuum-gap TPV presents a transformative opportunity for energy conversion technologies. By balancing power density, efficiency, and manufacturability, this approach provides a compelling alternative to existing TPV architectures. As further advancements in materials and device integration emerge, z-TPV could play a critical role in the next generation of sustainable energy solutions.

Multi-energy (ME) systems refer to the integrated and coordinated use of different forms of energy, such as electricity, heat, gas, and renewables, within a single framework or infrastructure. These systems are designed to enhance energy efficiency, reduce environmental impact, and improve the resilience and reliability of energy supply. ME systems combine various energy sources like solar, wind, natural gas, and electricity. This integration allows for more efficient use of resources as different energy types can complement each other. These systems often include technologies for energy storage (like batteries or thermal storage) and conversion (such as heat pumps or electrolyzers for hydrogen production), enabling the flexible use of energy in different forms.  Advanced control systems, often powered by artificial intelligence and machine learning, are used for optimizing the operation, distribution, and consumption of different energy types in real-time. ME systems can significantly improve overall energy efficiency because they utilize waste heat from one process for another (like using waste heat from electricity generation for heating), The diversity of energy sources in ME systems can enhance resilience against supply disruptions and adaptability to changing energy demands or market conditions. Moreover, by optimizing the use of renewables and reducing reliance on fossil fuels, ME systems can help in reducing greenhouse gas emissions and other environmental impacts. Although they can be complex and costly to implement, ME systems can offer long-term economic benefits through improved efficiency and reduced energy costs.

In a new study published in the Journal Frontiers in Energy Research led by Professor Shuqing Zhang, Xianfa Hu, Shaopu Tang from the Electrical Engineering Department, Tsinghua University together with Xianggang He from the Power Grid Planning Research Center at Guizhou Power Grid Co Ltd and Haibo Li and Donghui Zhang from the Tsinghua Sichuan Energy Internet Research Institute presented a detailed investigation on the dynamic coupling in ME systems, specifically focusing on the interaction and integration of different forms of energy such as electrical, thermal, and gas energies. The authors highlighted the challenges in modeling and simulating dynamic coupling in ME systems and proposed novel approaches for effective analysis and simulation.

The team began by thoroughly analyzing the structural components and model characteristics of various energy forms within ME systems. This included examining the elements and components, system structure, and features of models and algorithms for ME components. A significant part of the study was dedicated to understanding the mechanisms of dynamic coupling across different energy forms. This involved studying the conversion relations among different energy forms, structural characteristics of ME coupling, and the time scale and intensity of interaction and coupling. The researchers proposed key techniques for hybrid simulation across energy forms. This included developing a subsystem partitioning scheme, ME coupling modeling, and interface equivalent modeling. They also focused on variables and sequences for hybrid simulation interaction.

The authors highlighted how different forms of energy interact and transform within these systems, providing a comprehensive view of the dynamic interactions. They also introduced an innovative hybrid simulation approach, which is a major advancement in modeling and simulating dynamic interactions in ME systems. This approach allows for more accurate and efficient simulation of these complex systems. The implications of this research are extensive for practical applications. It offers a pathway to optimize energy distribution and consumption, leading to more efficient and sustainable energy management. The findings are crucial for policymakers and industry stakeholders, offering them insights to develop strategies that effectively leverage the interconnectedness of different energy forms. Moreover, this research lays the groundwork for further studies and technological developments in the field of energy systems, particularly in enhancing the operational efficiency and sustainability of ME systems.

In summary, the new study by Professor Shuqing Zhang and colleagues provided a comprehensive and detailed analysis of dynamic coupling in multi-energy systems, introducing new methodologies for simulation and offering insights with significant practical and industrial implications.

The increasing global demand for space cooling and HVAC systems has raised concerns about their significant energy consumption and associated CO2 emissions. To address this issue, researchers at the University of Cambridge, led by Professor David Fairen-Jimenez, have made groundbreaking advancements in the development of energy-efficient adsorbent-based HVAC technologies. Their findings, published in the Journal Advanced Materials, introduce the concept of monolithic metal-organic frameworks (MOFs) as a solution to overcome the limitations of traditional sorbent materials and improve the performance of HVAC systems.

Traditional HVAC systems, including refrigeration and dehumidification processes, contribute to approximately 10% of global electricity use and generate approximately 1.13 gigatonnes of CO2 emissions annually. The demand for cooling systems is projected to triple by 2050, necessitating a paradigm shift towards energy-efficient technologies. Traditional adsorbent materials, such as silica gels and zeolites, have limitations in terms of tunability and regeneration energy requirements, making them unsuitable for sustainable HVAC systems.

MOFs offer remarkable improvements in water adsorption and regeneration energy requirements, making them promising candidates for HVAC applications. Unlike traditional sorbent materials, MOFs can be precisely designed at the molecular level, resulting in high porosity and optimal MOF-water interactions. With over 100,000 MOF structures compared to around 1,000 silica gels and zeolites, MOFs offer a diverse range of materials for various applications.

The deployment of MOFs in HVAC applications has been hindered by challenges related to MOF powder processing. Traditional shaping techniques result in a loss of surface area and porosity, reducing the performance of MOFs. However, the researchers have developed a sol-gel approach to synthesize three high-density, shaped, monolithic MOFs: UiO-66, UiO-66-NH2, and Zr-fumarate. These materials exhibit exceptional volumetric gas/vapor uptake, overcoming previous limitations in MOF-HVAC deployment.

To assess the performance of the monolithic MOFs, the researchers conducted extensive experiments and analyses. Small-angle X-ray scattering and lattice-gas models were used to visualize the monolithic structures and accurately predict their adsorption characteristics. The monolithic MOFs demonstrated high water adsorption capacities, facile desorption at low temperatures, and high coefficient of performance (COP) for chillers. The fragile MOF, Zr-fumarate, achieved a bulk density of 0.76 gcm−3 without compromising adsorption performance and exhibited a COP of 0.71 at a low regeneration temperature (≤ 100 °C).

The development of monolithic MOFs represents a significant advancement in the field of HVAC technologies. These shaped materials offer improved volumetric adsorption capacities compared to their powdered and pelletized counterparts. The research provides a viable route towards industrial production of MOFs in high-density, shaped bodies while maintaining their full adsorption performance. The findings have the potential to revolutionize the design and implementation of energy-efficient adsorbent-based HVAC systems.

The research conducted at the University of Cambridge has showcased the potential of monolithic metal-organic frameworks (MOFs) in revolutionizing energy-efficient HVAC systems. The development of high-density, shaped MOFs overcomes the limitations of traditional sorbent materials, enabling enhanced water adsorption capacities and low-temperature regeneration. These advancements pave the way for sustainable cooling solutions with reduced energy consumption and environmental impact. As further.

Lithium-ion batteries (LiBs) is the predominant commercial form of rechargeable battery widely used in personal electronics, electric vehicles and grid storage systems. The performance LIBs rely on the effectiveness of the cathode which can influence their capacity, lifespan, and charging rate. Nickel manganese cobalt oxide (NMC) cathodes are commonly used due to their high energy density and stability, however, optimizing the performance of these cathodes is still a challenge and one method to enhance cathode performance is to improve the electron and ion transport pathways within the cathode material. Carbon black (CB) is commonly added to cathodes as a conductive additive to facilitate electron transport, although its role in ionic transport within the cathode matrix is less clear. To this end, new study published in Journal of Materials Chemistry A and conducted by PhD candidate Donghyuck Park, Dr Peter Sherrell, Fangxi Xie and led by Professor Amanda Ellis from The University of Melbourne in the Department of Chemical Engineering, the researchers developed a novel method to modify CB  surface using mild oxidation treatments by using hydrogen peroxide (H₂O₂) and nitric acid (HNO₃) which resulted in introducing specific oxygen-containing functional groups onto the CB surface without significantly compromising its structural properties. They found using Raman spectroscopy that the H₂O₂-treated CBs maintained a consistent D/G intensity ratio which indicates minimal defect generation. In contrast, the 70% HNO₃ treatment significantly increased defect density as shown by a higher D/G ratio. The authors performed X-ray photoelectron spectroscopy analysis and demonstrated an increase in oxygen content on the CB surface with specific functional groups (hydroxyl, carbonyl, and carboxyl) that changes based on treatment conditions. For instance, the H₂O₂ treatment resulted in an increase in the hydroxyl and carbonyl groups whereas HNO₃ treatment introduced carboxyl groups. They also evaluated the impact of the modified CBs on the electrochemical performance of NMC622 cathodes and found using cyclic voltammetry tests at a scan rate of 0.1 mV s⁻¹ that cathodes with carbonyl-modified CB (CB=O) showed the highest peak currents, which means faster lithium-ion intercalation and deintercalation reactions. Moreover, they assessed the rate performance of the cathodes at various C-rates. At 0.75C, both the CB=O cathode and CB with hydroxyl groups (CB-OH) delivered a substantial improvement in performance compared to the pristine CB cathode. In contrast, the cathode with carboxyl groups (CB-COOH) showed much poorer performance. The authors also performed electrochemical impedance spectroscopy measurements and showed the CB=O cathode to display lower impedance across all frequency ranges, which indicates reduced resistance at the cathode-electrolyte interface and more efficient charge transfer processes. Furthermore, the researchers proposed a mechanism for the observed enhancement in rate performance. According to the authors, the presence of carbonyl groups on the CB surface improved the wettability of the electrolyte and facilitated better contact and interaction between the electrolyte and the cathode active material. This increased the electrochemically active surface area and enhanced the efficiency of lithium-ion transport. The carbonyl groups likely reduced the energy barrier for lithium-ion movement at the electrode-electrolyte interface which results in lower overpotentials and faster charge/discharge reactions. Their proposed mechanism was supported by the observed overpotential measurements where the CB=O cathode demonstrated the lowest overpotential during both charging and discharging processes and indicated that less energy was required to achieve high-rate performance.  Additionally, the team tested their innovative modified CBs with other common cathode materials such as NMC811, lithium manganese oxide, and nickel cobalt aluminum oxide and showed improved rate performance across all these different cathode materials. Their findings highlight the versatility of the proposed surface modification approach and its potential for widespread application in various lithium-ion battery technologies.

In conclusion, Professor Amanda Ellis and colleagues provided valuable knowledge on how surface functional groups on CB can influence electrochemical processes within the cathode which can guide future research and development in optimizing conductive additives for LiBs. There are practical implications of the authors’ findings, first, the use of mild chemical treatments is a cost-effective method for improving battery performance which can be easily integrated into existing manufacturing processes without significant additional costs. Moreover, the new method is simple and effective which makes it feasible for large-scale production and battery manufacturers to adopt these modifications and produce improved and high-performance batteries. Furthermore, the reported improved rate performance expands the potential applications of LIBs to be used in high-power tools, fast-charging consumer electronics, and high-capacity energy storage systems for renewable energy integration. Finally, with the enhanced battery performance we expect broader adoption of electric vehicles and renewable energy systems which will ultimately contribute to reduced environmental impact and the transition to a more sustainable energy future.

Machine learning can play a crucial role in improving lithium batteries by enhancing their performance, safety, and longevity. Battery Management Systems is responsible for monitoring and controlling various aspects of battery operation, including state-of-charge, state-of-health, and temperature management. Machine learning algorithms can analyze real-time data from sensors within the battery system to optimize battery performance, predict degradation, and enable proactive maintenance. By learning from historical data and patterns, machine learning algorithms can improve the accuracy of state estimation, leading to more efficient and safe battery operation. Machine learning models can analyze data collected during battery operation, such as voltage, current, temperature, and cycling profiles, to predict the remaining useful life of the battery. These models can capture complex degradation mechanisms and factors affecting battery lifetime, allowing for proactive decisions such as adjusting charging/discharging profiles or replacing batteries before failure. Moreover, machine learning can optimize battery performance by developing algorithms that analyze data on cell chemistry, materials, and operating conditions. These models can identify optimal charging and discharging protocols, temperature management strategies, and electrode compositions to enhance energy density, power output, and overall efficiency of lithium batteries. Machine learning algorithms can also enable rapid exploration and optimization of battery designs, reducing the time and cost of materials discovery and development. Furthermore, machine learning techniques can accelerate the discovery and design of new battery materials with improved properties. By leveraging large datasets and computational simulations, machine learning algorithms can predict the behavior and performance of new materials, helping researchers narrow down the search space and focus on the most promising candidates. This can significantly speed up the development of novel lithium-ion battery chemistries, electrolytes, and electrode materials with enhanced energy storage capabilities. Machine learning can also enhance the accuracy and efficiency of battery modeling and simulation. By training models on large datasets of experimental data and physics-based simulations, machine learning algorithms can learn complex relationships between input parameters and battery behavior. These models can be used to simulate battery performance under different operating conditions, optimize system designs, and guide decision-making processes.

Argyrodite is a class of materials that has gained significant interest in the field of energy storage due to its unique properties. Argyrodites are a type of solid-state electrolyte material, which means they can conduct ions without the need for a liquid electrolyte. This characteristic makes them promising candidates for use in advanced energy storage devices such as lithium-ion batteries and solid-state batteries. One of the main advantages of argyrodite-based materials is their excellent ionic conductivity. They exhibit high Li+ or Na+ conductivity, which is crucial for efficient ion transport in energy storage systems. This high conductivity is attributed to the presence of disordered crystal structures and the ability of these materials to accommodate mobile ions within the lattice.

Additionally, argyrodites have wide electrochemical stability windows, meaning they can withstand high voltages without decomposition. This property is particularly important for battery applications, as it allows for the use of high-voltage cathode materials, resulting in increased energy densities. Arising from their solid-state nature, argyrodite-based electrolytes offer several advantages over traditional liquid electrolytes used in lithium-ion batteries. First, they eliminate the need for flammable and volatile organic solvents, improving the safety of the energy storage device. Second, solid-state electrolytes can potentially enable the use of metallic lithium anodes, which have higher energy densities compared to conventional graphite anodes. The use of argyrodites in energy storage extends beyond lithium-ion batteries. They are also being explored for solid-state sodium-ion batteries, which are considered as alternative energy storage systems to lithium-ion batteries due to the abundance and lower cost of sodium. Argyrodite-based materials have shown promise in facilitating the transport of sodium ions, making them suitable for sodium-ion battery applications.

Despite the advantages, there are still challenges associated with the practical implementation of argyrodites in energy storage devices. One of the major obstacles is achieving high ionic conductivity at room temperature. While argyrodite materials exhibit excellent conductivity at elevated temperatures, there is ongoing research to enhance their performance at ambient conditions. In a new research study published in the peer-reviewed journal Nature Materials, Duke University researchers led by Professor Olivier Delaire uncovered for the first time the atomic mechanisms that make a class of compounds called argyrodites attractive candidates for both solid-state battery electrolytes and thermoelectric energy converters.

As the world moves toward a future built on renewable energy, researchers must develop new technologies for storing and distributing energy to homes and electric vehicles. While the standard bearer to this point has been the lithium-ion battery containing liquid electrolytes, it is far from an ideal solution given its relatively low efficiency and the liquid electrolyte’s affinity for occasionally catching fire and exploding. These limitations stem primarily from the chemically reactive liquid electrolytes inside Li-ion batteries that allow lithium ions to move relatively unencumbered between electrodes. While great for moving electric charges, the liquid component makes them sensitive to high temperatures that can cause degradation and, eventually, a runaway thermal catastrophe. Many public and private research labs are spending a lot of time and money to develop alternative solid-state batteries out of a variety of materials. If engineered correctly, this approach offers a much safer and more stable device with a higher energy density at least in theory.

While there is no commercially viable approach yet to solid-state batteries, one of the leading contenders relies on argyrodites. These compounds are built from specific, stable crystalline frameworks made of two elements with a third free to move about the chemical structure. While some recipes such as silver, germanium and sulfur are naturally occurring, the general framework is flexible enough for researchers to create a wide array of combinations.

The authors used one promising candidate made of silver, tin and selenium (Ag8SnSe6). Using a combination of neutrons and X-rays, the researchers bounced these extremely fast-moving particles off atoms within samples of Ag8SnSe6 to reveal its molecular behavior in real-time. They also developed a machine learning approach to make sense of the data and created a computational model to match the observations using first-principles quantum mechanical simulations. The results showed that while the tin and selenium atoms created a relatively stable scaffolding, it was far from static. The crystalline structure constantly flexes to create windows and channels for the charged silver ions to move freely through the material.

The results and, perhaps more importantly, the approach combining advanced experimental spectroscopy with machine learning, should help researchers make faster progress toward replacing lithium-ion batteries in many crucial applications. According to the authors, this study is just one of a suite of projects aimed at a variety of promising argyrodite compounds comprising different recipes. One combination that replaces the silver with lithium is of particular interest to the group, given its potential for EV batteries.

Overall, machine learning provides powerful tools for data analysis, pattern recognition, and optimization in the context of lithium batteries. By leveraging these techniques, engineers can accelerate the development, improve the performance, and ensure the safety of lithium-ion battery technologies, enabling the advancement of various applications such as electric vehicles, portable electronics, and grid-scale energy storage. The study serves to benchmark our machine learning approach that has enabled tremendous advances in our ability to simulate these materials in only a couple of years. According to the authors, the new method will allow quickly simulate new compounds virtually to find the best recipes these compounds have to offer. Moreover, argyrodites represent a class of solid-state electrolyte materials with significant potential for use in energy storage. Their high ionic conductivity, wide electrochemical stability window, and compatibility with metallic anodes make them attractive candidates for advanced battery technologies, including both lithium-ion and sodium-ion batteries. Continued research and development efforts aim to address the remaining challenges and unlock the full potential of argyrodites in energy storage applications.

Wave energy is a promising source of renewable energy due to its high energy density, predictability, and wide availability. Wave Energy Converters (WECs) are devices designed to harness the kinetic and potential energy of ocean waves and convert it into usable forms of energy, typically electricity. They are a type of renewable energy technology that taps into the vast energy resources present in the world’s oceans. The basic idea behind WECs is to capture the movement of waves and convert it into mechanical motion, which is then transformed into electrical power through generators However, the efficiency and cost-effectiveness of WECs have posed challenges, limiting their maturation compared to wind and solar energy technologies. One avenue for improving WEC efficiency is the exploration of multi-degree-of-freedom (multi-DOF) systems with hybrid power take-off (PTO) mechanisms. To this note and in a new study published in the peer-reviewed Journal Energy Conversion and Management, where Associate Professor Xingxian Bao, Fumiao Li from China University of Petroleum, Huihui Sun from Qingdao Marine Science and Technology Center, and Professor Hongda Shi from Ocean University of China collaborated with Professor Gregorio Iglesias from University College Cork presented a novel multi-DOF wave energy converter (PUWEC) with hybrid PTO systems.

Unlike traditional single-DOF WECs that rely on capturing energy from either pitch or heave motion, the PUWEC introduces a groundbreaking approach to enhance wave energy conversion. The PUWEC’s design incorporates prismatic pairs in the hydraulic cylinder and universal pairs in the gear transmission system. These PTO systems are matched to the buoy’s motion in heave and pitch, respectively, resulting in improved power capture. The innovative concept revolves around the simultaneous capture of kinetic and potential energy of the floating body through multi-DOF motion.

To analyze the PUWEC’s performance, the authors developed a numerical simulation framework based on ANSYS-AQWA, a comprehensive software suite for marine dynamic analysis. This framework enables the dynamic analysis of floating bodies in various wave conditions, taking into account nonlinear hydrostatic and Froude-Krylov forces. The secondary development of ANSYS-AQWA allows for the incorporation of the PUWEC’s unique multi-DOF behavior and hybrid PTO systems. Validation of the numerical model was achieved through model experiments in a wave tank, affirming its predictive capabilities for the dynamic behavior of the PUWEC.

The research team conducted a series of parametric studies to explore the effects of P-PTO and U-PTO parameters on the captured power of single-DOF PWEC and UWEC under different wave conditions. The results yielded optimal damping coefficients for both P-PTO and U-PTO systems. Importantly, the PUWEC demonstrated a clear advantage in terms of captured power compared to single-DOF systems. The authors found that the pitch motion is the primary driver of energy capture in the PUWEC, contributing significantly to its improved efficiency.

The authors further investigated the trajectory analysis of the float’s center of gravity, shedding light on the intricate relationship between multi-DOF motion and power capture. The trajectory coefficient was introduced as a metric to evaluate the energy capture characteristics of the PUWEC. Their findings demonstrated that a higher trajectory coefficient corresponds to better wave energy absorption, providing insights into optimizing multi-DOF WEC design. Additionally, the working space analysis of the PUWEC indicated that the actual performance of the device was well-aligned with the design expectations, reinforcing its feasibility and potential for real-world application.

Xingxian Bao and colleagues’ comprehensive analysis of the proposed multi-DOF wave energy converter with hybrid PTO systems showcases a promising solution for enhancing wave energy conversion efficiency. According to first and corresponding author, Dr. Xingxian Bao, the PUWEC’s innovative design, coupled with the advanced numerical simulation framework, offers valuable insights into the complex dynamics of multi-DOF motion and its impact on power capture. The study findings emphasize the significance of optimizing PTO parameters and trajectory characteristics to achieve maximum wave energy absorption. Ultimately, the PUWEC holds the potential to drive advancements in wave energy technology and contribute to the global pursuit of sustainable energy sources.

The search for effective sustainable energy solutions has gained increasing momentum as the world struggles with the challenges posed by climate change and the limited availability of fossil fuels. One promising avenue for addressing these issues is the development of thermal energy storage systems, which can effectively store and release thermal energy, making it possible to harness intermittent and renewable energy sources like solar and wind power efficiently. These systems are vital for ensuring a stable and reliable energy supply, especially in regions where these renewable sources are abundant. Thermal energy storage (TES) encompasses various technologies designed to store thermal energy in different forms, including sensible heat storage (SHS), latent heat storage, and thermochemical heat storage. Among these, SHS is the most established and widely employed method due to its maturity and cost-effectiveness. However, the quest for high-performance SHS materials at a reasonable cost is ongoing, as the existing materials often fall short in terms of thermophysical properties and economic feasibility. A recent study published in the Journal of Cleaner Production, led by student Junlei Wang and Dr. Yun Huang from the State Key Laboratory of Multiphase Complex Systems at the Institute of Process Engineering – Chinese Academy of Sciences. The authors investigated the potential of utilizing recycled solid waste resources, specifically steel slag, as a sensible heat storage material for thermal energy storage. Moreover, it introduces a novel modification process using sodium carbonate (Na₂CO₃) to enhance the thermal properties of steel slag.

Solid waste materials have gained recognition as valuable resources for thermal energy storage applications, primarily because they offer a dual benefit. Firstly, they can enhance the efficiency and cost-competitiveness of renewable energy systems and waste heat recovery processes. Secondly, they contribute to the reduction of processing costs for solid waste management and mitigate environmental problems associated with landfills. Several types of solid waste materials, including fly ash, asbestos waste, concrete, and metallurgical slag, have been extensively studied for their potential in medium-high temperature heat storage systems. Metallurgical slag, particularly steel slag, holds great promise as a sensible heat storage material. Steel slag is a byproduct of steelmaking processes, characterized by its porous structure and excellent thermal stability. It primarily consists of oxides such as calcium oxide (CaO), silicon dioxide (SiO₂), iron oxide (Fe₂O₃), alumina (Al₂O₃), magnesium oxide (MgO), and other compounds. Despite its potential, steel slag has been underutilized, with less than 30% of the annual production in China being effectively utilized. Most of it ends up in landfills, posing significant environmental challenges. Previous research efforts have focused on electric arc furnace steel slag, demonstrating its stability at high temperatures and its potential for sensible heat storage applications. However, little research has explored the use of converter steel slag, and there have been limited attempts to modify steel slag to improve its thermal performance.

In the study conducted by Wang and Dr. Huang, converter steel slag was examined for its suitability as a sensible heat storage material, and a novel Na₂CO₃ modification process was developed to enhance its thermal properties. The authors had several key findings emerged from their research, first the X-ray fluorescence analysis revealed that the main elements of steel slag  in terms of oxides are CaO, SiO₂, Fe₂O₃, Al₂O₃, and MgO. X-ray diffraction analysis of the slag both before and after sintering identified various mineral phases. After sintering, the primary phase of the modified steel slag was identified as Na₂CaSiO₄, indicating that molten Na₂CO₃ reacted chemically with the steel slag at high temperatures. They conducted thermodynamic analysis of the possible reactions between steel slag components and carbonates, specifically Li₂CO₃, Na₂CO₃, and K₂CO₃, provided insights into the modification process. Na₂CO₃ was chosen for the experimental study due to its cost-effectiveness and potential for enhancing thermal properties. Thermodynamic calculations suggested that Na₂CO₃ could react with steel slag components to form Na₂CaSiO₄, enhancing its thermal performance. Moreover, the researchers tested the thermal cycle stability of various sodium carbonate-modified steel slags (SMSs), and SMS-35 was identified as having the best thermal cyclic stability. SMS-35 exhibited no significant changes in morphology after 200 thermal cycles, making it a promising candidate for further investigation. Furthermore, when the researchers conducted differential scanning calorimetry and thermogravimetric analysis to evaluate the thermal properties of both steel slag and SMS-35. The equivalent specific heat of SMS-35 was found to be 25.32% higher than that of steel slag in the range of 400–900°C. Furthermore, the thermal conductivity of SMS-35 significantly surpassed that of steel slag, showing a 32.7% increase at 500°C. These improvements are attributed to the modification process, which altered the material’s composition and structure, resulting in enhanced thermal performance. According to the authors, after 200 thermal cycles, SMS-35 exhibited excellent thermal cycle stability. Its morphology remained unchanged, and the phase composition was consistent before and after the thermal cycles. The equivalent specific heat values for SMS-35 across different temperature segments tended to be consistent after thermal cycling, indicating that the internal structure of SMS-35 became more stable, and the reaction between steel slag and Na₂CO₃ became more complete.

In conclusion, the study led by Wang and Dr. Huang presents a compelling case for the utilization of steel slag as a sensible heat storage material for thermal energy storage applications. Through the innovative modification process using Na₂CO₃, the thermal properties of steel slag were significantly enhanced, making it a promising candidate for concentrated solar power and industrial waste heat recovery systems.

South Asia harbors about 2 billion of the global population, making the region more sensitive to environmental issues-related effects. For instance, due to the natural catastrophes caused by environmental issues, the region has suffered a loss totaling about $149 billion between 2002 and 2018. Nevertheless, the energy demand has significantly increased owing to economic expansion. However, this need has not been adequately met despite the region’s abundant supply of renewable energy.

The rapid growth in the global population, coupled with environmental depletion of natural resources, is a major concern for the global economy. Several contemporary socio-economic issues like poverty, increased urbanization, inadequate access to clean water and sluggish socio-economic development have been linked to environmental deterioration. Like other countries, South Asian nations are also dealing with these problems. For instance, rapid increase in the urban population in the last decades has put tremendous strains on the available natural resources, leading to environmental issues.

Green economic growth has been hailed as an effective strategy for addressing environmental issues. It can promote environmental conservation while mitigating some negative impacts of environmental degradation. While the main objective of green economy is to reduce carbon emissions, this has remained far from reality despite the consistent promises from the global community. Global warming and climate change continue to wreak havoc on nearly all aspects of socio-economic development. While South Asia nations are seeing increased green economic growth, more efforts are still needed.

Compared to the effects of globalization, financing research and development (R&D) in environment protection and renewable energy could significantly benefit the creation of environmental sustainability and provide a stimulus for green economic growth. Although numerous studies have linked growing affluence to environmental degradation, it is worth noting that environmental protection would contribute to innovation, green technologies, energy efficiency and beneficial environmental regulations. All these can be achieved through green financing, a vital driver of industrial structural change and green economic development.

Herein, Dr. Wei Fang from Guangdong Academy of Agricultural Sciences, Dr. Zhen Liu from Nanjing Normal University and Dr. Ahmad Romadhoni Putra from Universitas Gadjah Mada studied the impact of R&D, renewable energy and industrialization on green economic growth. Specifically, the study focused on the South Asian region from 2008 to 2020. A two-step ordinary least square was adopted to address the endogeneity of this complex connection. Furthermore, the initiative’s impact on carbon emissions and energy usage was assessed using the mediating effect analysis approach. Their work is currently published in the journal, Renewable Energy.

The authors showed that upgrading the development of industrial structures and R&D initiatives is instrumental in promoting green economic growth, including reducing carbon emissions and stimulating green economic recoveries. Based on the findings, the region saved approximately 33.4% of its energy and reported a 35.2% reduction in carbon emissions. The potential benefits of the policy’s affecting framework were illustrated using the mediation impact findings. Technology spillover exhibited the most responsibility to minimizing tiers of carbon emissions, coupled with improvements in the changes in the industrial structure. The authors identified three key ways South Asian nations could use to influence the global economy: upgrading the industrial structures, fostering green international commerce and boosting technological growth.

In summary, the new study investigated the role of R&D in stimulating green economic growth and industrial structural improvements. The findings offered more knowledge and insights into the reality of the global advances toward green economic development and which could help conserve the environment, reduce the emissions of greenhouse gases, and ensure efficient energy consumption. In a statement to Advances in Engineering, Dr. Wei Fang said their findings would contribute to the green and sustainable development of the global economy.

Energy sharing involves the distribution and allocation of energy resources among multiple users or entities. This process often requires solving complex optimization problems to ensure efficient and fair distribution of energy, considering factors like supply-demand balance, cost, and environmental impact. A Virtual Power Plant (VPP) is an innovative concept in the energy sector, primarily focusing on the decentralized generation and distribution of electricity. Unlike traditional power plants that rely on large-scale, centralized generation facilities, a VPP operates by linking and coordinating various small-scale, distributed energy resources (DERs). These resources can include solar panels, wind turbines, small hydroelectric generators, battery storage systems, and even controllable loads like HVAC systems in large buildings.

VPPs integrate energy produced from multiple, geographically dispersed sources. This approach can enhance the resilience of the power grid against localized failures or disruptions. It VPPs can quickly respond to changes in energy demand or supply, improving overall grid stability and efficiency by simply managing a diverse mix of energy sources. Moreover, VPPs rely heavily on modern information and communication technologies. These technologies allow for the remote and automated control of the connected DERs, optimizing energy production and distribution in real-time. VPPs are particularly effective in integrating renewable energy sources into the power grid. By coordinating various renewable sources, VPPs can mitigate issues like the intermittent nature of solar or wind power. Furthermore, VPPs can adjust the energy load by controlling certain DERs, like industrial cooling systems, reducing the demand during peak times or increasing it when excess energy is available. Indeed, VPPs represent a shift towards a more distributed, intelligent, and sustainable power grid, aligning with the global trend of increasing renewable energy adoption and the push for smart grid technologies.

Indeed, the transition from traditional electricity consumers to proactive prosumers in VPPs has been accelerated by the integration of DERs. This shift has enabled prosumers to share surplus energy, fostering a more dynamic and decentralized energy market. However, this evolution presents significant challenges, particularly in terms of communication and coordination among a vast number of participants. In a new study published in the IEEE Transactions on Smart Grid by Cheng Feng, Dr. Kedi Zheng, Yangze Zhou, and led by Professor Qixin Chen from the Department of Electrical Engineering at Tsinghua University together with Professor Peter Palensky at Intelligent Electric Power Grids, TU Delft, the authors addressed the challenges of integrating a large number of prosumers (both energy producers and consumers) into VPPs, which are becoming increasingly important in modern energy grids. This study has received strong support from the National Key R&D Program project of China, “Key Technologies for Aggregation, Interaction and Control of Virtual Power Plants with Enormous Flexible Resources” (2021YFB2401200). The key issue is managing communication congestion that arises with massive prosumer participation. The researchers proposed solution, an online partial-update algorithm using the alternating direction method of multipliers (ADMM), aims to streamline this process. By selectively updating a subset of prosumers in each ADMM round, it balances the need for efficient energy sharing with the limitations of communication bandwidth. This approach is not only innovative but also practical, considering the growing trend towards decentralized energy resources and the critical need for efficient energy management in VPPs.

The ADMM is an optimization algorithm that is used in various fields, including energy management and sharing. When applied to energy sharing, ADMM-based approaches facilitate efficient, decentralized decision-making in distributed energy systems, such as microgrids or virtual power plants. ADMM breaks down a complex optimization problem into smaller, more manageable sub-problems. This is particularly useful in energy systems where multiple independent entities (like different households or businesses) are involved. Each entity in the network solves its optimization problem locally. This involves determining how much energy to generate, store, or consume based on local conditions and constraints. After local optimization, entities communicate their decisions to a central coordinator or exchange information with each other. The goal is to ensure that the collective decisions align with overall system objectives (like minimizing costs or maximizing renewable energy use). ADMM can be used to optimally distribute energy generated from renewable sources among different users in a microgrid. It also helps in coordinating distributed energy resources to optimize energy production and distribution. There are significant benefits of energy sharing, for instance ADMM allows for decentralized decision-making, which is vital in distributed energy systems where centralized control might be impractical or inefficient.: It can handle large-scale problems involving many entities, making it suitable for complex energy networks.

The key concern addressed by the researchers is the potential for communication network congestion, which could lead to increased negotiation waiting times and risk missing crucial market deadlines. This problem is especially pronounced in large-scale energy sharing scenarios involving thousands of prosumers. The proposed solution is an innovative online partial-update algorithm for energy sharing, based on the ADMM. The algorithm focused on managing the interaction between the VPP and prosumers by selecting only a subset of prosumers for ADMM updates in each round. Significant contributions have been made in the following areas.

• This approach aims to prevent the communication congestion that can result from massive simultaneous update requests，where the number of prosumers participating in updates is deliberately restricted. A partial-update ADMM algorithm is developed for energy sharing in the VPP, which only requires a subset of the prosumers to involve in distributed iterations every round to minimize the convergence time.
• The study also designs a fair and efficient scheduling policy t for determining the optimal number of prosumers to participate in these updates, ensuring a balance between efficiency and fairness. Meanwhile, the proposed system prioritizes convergence-critical prosumers in the update process while ensuring that all prosumers receive sufficient opportunities for participation.
• The research conducts techniques investigation to enhance the online performance of the complete partial-update ADMM algorithm with scheduling. The objective is to minimize additional computational and communication overheads, allowing the algorithm to operate effectively in real-time environments.

Numerical studies support the effectiveness of this approach, highlighting its potential to reduce overall convergence time significantly while maintaining fair and efficient energy sharing.

In a statement to Advances in Engineering, the authors said “the team emphasized on a novel partial-update asynchronous energy sharing algorithm effectively tackles the challenges posed by communication network congestion and massive prosumer access. They successfully proposed a unique scheduling policy, balancing efficiency and fairness in prosumer participation, enhancing the overall performance of energy sharing in VPPs. It has proven capable of managing over 10,000 prosumers within a VPP framework. Impressively, it reduces negotiation time for distributed VPP energy sharing by 30%-50%, while simultaneously ensuring the optimality of the VPP energy sharing plan. This novel approach offers a scalable, efficient, and practical solution to the evolving challenges in VPPs. Therefore, it is essential for this work to receive extensive attention and discussion within the academic and engineering communities”. In conclusion, the research work represents a significant step forward in addressing the challenges of large-scale energy sharing in VPPs. It introduces a practical and scalable solution to optimize communication and coordination among a growing number of prosumers, ensuring the efficient and fair distribution of energy resources.

In the future, with the development of smart cities, new digital assets will give electric end-users more edge computing and decision-making capabilities. The core technology achievements in this study will play an important role in dealing with high-frequency interaction problems of large-scale end-users under heterogeneous communication, and have great application prospects.

Biomass is a promising alternative to fossil resources, offering the potential to reduce our dependence on fossil fuels while mitigating adverse environmental impacts. In a new study published in the journal of Green Chemistry by Xiaohong Ren, Dr. Zhuohua Sun, Dr. Jiqing Lu, Dr. Jinling Cheng, Professor Panwang Zhou, Xiaoqiang Yu, Professor Zeming Rong, and Professor Changzhi Li, the scientists from Dalian University of Technology, Beijing Forestry University  and Dalian Institute of Chemical Physics, etc., developed a novel method that eliminates the need for external hydrogen in biomass conversion processes, thereby significantly improving the efficiency, sustainability, and economic viability of biorefineries.

Hydrogen is a renewable and zero-emission energy resource, but it does not occur naturally and must be produced from hydrogen-containing compounds, such as water or organic materials. Traditional methods for hydrogen production involve fossil fuels, which counteract the objective of achieving a sustainable and green biorefinery industry. Additionally, alternatives like water electrolysis, although environmentally friendly, face economic challenges that limit large-scale adoption. Various biomass conversion methods, such as hydrodeoxygenation (HDO), hydrogenation, and reductive depolymerization, are currently employed in biorefineries. These processes typically require a hydrogen-rich atmosphere to facilitate the transformation of biomass-derived feedstocks into valuable products. However, the conventional sources of hydrogen, primarily derived from fossil fuels like natural gas through steam methane reforming, are not aligned with the sustainability goals of the biorefinery industry.

The authors introduced an innovative concept of hydrogen-free biomass conversion. This new approach seeks to replace the need for externally supplied hydrogen with an in-situ generation mechanism, thus enhancing the atom economy, reducing operational costs, and improving overall process safety. In their previous work, the researchers demonstrated the exceptional performance of a nano-porous Ni catalyst in selectively converting guaiacol to cyclohexanol under a hydrogen atmosphere. This achievement laid the foundation for the current study, where they aimed to use the same catalyst to selectively convert guaiacol to phenol in the absence of external hydrogen. The results were excellent, with guaiacol conversion rates of up to 41.5% and phenol selectivity of 100% achieved at 160°C. These remarkable outcomes were further improved at 190°C, with a guaiacol conversion rate of 90.5% while maintaining a 90.3% phenol selectivity.

The researchers began by comparing the catalytic activity of different heterogeneous catalysts. Nano-porous Ni exhibited superior performance with high guaiacol conversion rates and exclusive selectivity for phenol. Other transition metals, including Fe, Co, and Cu, displayed limited activity, emphasizing the unique catalytic properties of nano-porous Ni. They conducted detailed analysis, including Density Functional Theory (DFT) calculations, confirmed the critical role of the nano-porous Ni catalyst in water activation and subsequent hydrodeoxygenation processes. Water splitting on the catalyst’s surface initiated the reaction, leading to the cleavage of the aryl ether bond in guaiacol.

Moreover, the researchers investigated the source of hydrogen crucial for the reaction. Control experiments using different solvents revealed that water played a significant role in supplying hydrogen for the in-situ hydrodeoxygenation of guaiacol. Isopropanol was also identified as a potential hydrogen donor, but water’s hydrogen supply capacity was comparatively weaker. Furthermore, they proposed reaction mechanism which involves the generation of hydrogen from water by nano-porous Ni. Hydrogen radicals facilitate the cleavage of the aryl ether bond in guaiacol, resulting in the production of methanol and phenol. Subsequently, aqueous-phase reforming of in-situ generated methanol generates additional hydrogen, promoting further hydrodeoxygenation of guaiacol to phenol.

In conclusion, the study by Professor Changzhi Li and the team at the Dalian University of Technology represents an important milestone in the field of sustainable energy production. They successfully demonstrated a “hydrogen-free” HDO strategy using nano-porous Ni catalysts, exhibiting high phenol selectivity from guaiacol. The system involves water chemo-splitting, selective C–O bond cleavage, and aqueous-phase methanol reformation. Characterization and DFT calculations confirmed the catalyst’s crucial role in these processes.  Their innovative approach to hydrogen-free biomass conversion offers a promising alternative to conventional biorefinery processes, addressing the challenges of hydrogen sourcing and economic feasibility. By leveraging the unique catalytic properties of nano-porous Ni, the researchers have demonstrated the feasibility of an “H₂-free” upgrading process, achieving partial deoxygenation and molecular upgradation without the need for external hydrogen. This research has far-reaching implications, not only for the biorefinery industry but also for the broader renewable energy sector. It opens doors to more sustainable and economically viable methods of biomass conversion, aligning with global efforts to transition toward greener and cleaner energy sources.

Traditional energy systems rely mainly on fossil fuels such as coal, oil, and natural gas, have played a crucial role in meeting global energy demand in the past. However, they also resulted in negative impact on the environment and human health. Some of the key challenges posed by traditional energy systems, which result in global warming and climate change due to their polluting nature, include greenhouse gas emissions, various air pollutants, including sulfur dioxide (SO₂), nitrogen oxides (NO_x), particulate matter, and volatile organic compounds. These pollutants can degrade air quality, leading to respiratory problems, cardiovascular diseases, and premature deaths. Moreover, extracting and processing fossil fuels can result in water pollution through spills and leaks. Additionally, the disposal of wastewater from extraction processes can contaminate water sources, affecting aquatic life and human communities that rely on clean water.

The global pursuit of sustainable and environmentally friendly energy sources has intensified in recent years, driven by the growing awareness of the detrimental impacts of climate change caused by traditional energy systems reliant on fossil fuels. As a response to this urgent challenge, renewable energy sources (RES) such as wind, solar, and hydroelectric power have gained significant attention for their potential to reduce carbon emissions. Among these, hydrogen has emerged as a promising clean energy carrier, offering a wide range of applications and the potential for long-term energy storage.

A new study published in the peer-reviewed Journal of Cleaner Production conducted by Associate Professor Zhi Yuan, Professor Weiqing Wang, and PhD candidate Ji Li from Xinjiang University. The study explored the development of an innovative hydrogen-based energy system (HES) that incorporates power-to-gas (P2G) technology to enhance energy efficiency and address the carbon emission problem on both the demand and supply sides.

In their effort to develop an integration of hydrogen-based systems with power-to-gas technology to create an innovative and comprehensive HES-P2G model, the authors focused on stochastic programming to optimize the system and achieve both economic and environmental goals. By recycling carbon emissions through P2G and converting them into methane, the model reduces greenhouse gas emissions and increases energy efficiency. The proposed HES-P2G model also employs the epsilon-constraint method and fuzzy satisfying technique to attain a trade-off solution between carbon emissions and operating costs.

The model formulation is a critical component of the study, as it details the key components of the proposed HES-P2G model and outlines the relationships between these components. The optimization problem is formulated to address the day-ahead operation of the energy system, considering uncertainties through stochastic programming. The model comprises several key elements, including hydrogen-related components (FC, EL, compressor, and HTS), renewable energy sources (WT and PV), water storages (HWS and CWS), and an absorption chiller (AC) for cooling provision. The model is solved using mixed-integer linear programming (MILP) with the aid of the piecewise linearization method to address non-linear efficiencies.

The authors emphasized the advantages of the proposed HES-P2G model over other hydrogen-based systems. Notably, the model allows for the comprehensive recycling of emitted carbon through P2G, reducing the system’s dependency on the upstream grid and significantly lowering greenhouse gas emissions. By employing the epsilon-constraint method, the model finds a trade-off solution that balances economic and environmental goals, making it an essential component of achieving a net-zero energy system. The results of the study demonstrate that the implementation of P2G in the HES leads to a substantial reduction in carbon emissions (14.2%) and operating expenses (11.7%) compared to a case without P2G. The study concludes with a profound understanding of the potential and significance of the proposed HES-P2G model in advancing clean energy production and reducing carbon emissions. By integrating hydrogen-based systems with P2G technology, the model offers a comprehensive solution to the challenges posed by traditional energy systems. The efficient recycling of carbon emissions and the simultaneous achievement of economic and environmental goals underscore the model’s sustainability and effectiveness.

The successful integration of renewable energy sources with hydrogen-based systems, coupled with innovative power-to-gas technology, holds the key to unlocking a cleaner and more efficient energy landscape. As society shifts towards a net-zero carbon future, initiatives such as the HES-P2G model pave the way for a sustainable energy transition that prioritizes economic growth and environmental responsibility.

In conclusion, the study by Zhi Yuan and colleagues marks a significant milestone in the pursuit of clean energy solutions. The proposed HES-P2G model demonstrates the potential of integrating hydrogen-based systems with P2G technology to address the carbon emission problem and enhance energy efficiency. As the world continues to grapple with the consequences of climate change, the findings of this study offer a promising pathway towards a sustainable and greener future.

Transitioning to a low-carbon energy system is vital for mitigating climate change, preserving ecosystems, and ensuring a healthier and more resilient future for the planet and its inhabitants. Governments, industries, and every person all play critical roles in this transformative process. The researchers’ innovative HES-P2G model represents a crucial step towards achieving sustainable energy production and reducing carbon emissions, offering hope for a greener and more resilient planet. As the global community unites to combat climate change and transition to a low-carbon future, the integration of renewable energy sources with hydrogen-based systems remains at the forefront of cutting-edge engineering and technological advancements. With continued research and collective effort, the vision of a clean and sustainable energy future can become a reality.

The rapid development and transition to sustainable and renewable energy have increased the demand for longer-duration and large-scale energy storage technologies. Among these technologies, compressed air energy storage (CAES) has drawn considerable attention as an ideal solution. Different CAES-based concepts have been proposed. Underwater compressed gas energy storage (UWCGES) is a flexible and scalable energy storage technology suitable for renewable energy firms in coastal, islands, and offshore regions. It has emerged as a vital facilitator of the renewable energy transition.

Although the UWCGES has been extensively studied, only a few studies have focused on the crucial issues of liquid accumulation that often occurs in underwater gas transmission pipelines. The liquid accumulation is attributed to the condensation and precipitation of water vapor after reaching the pressure dew point due to the decrease in temperature and the increase in pressure with the increase in the water depth. This results in the formation of slug flow, which affects the operation and performance of UWCGES systems. To effectively control the liquid accumulation movement and enhance the gas transmission efficiency, it is important to analyze the slug formation and correctly predict the critical slugging velocity.

For hilly-terrain pipelines, the interfacial instability is a possible cause of the slugs associated with the movement of the liquid accumulation under zero net liquid flow (ZNLF). However, the liquid accumulation movement in hilly-terrain pipes under ZNLF is poorly understood. This has been attributed to two main reasons. First is the lack of a highly adaptable and reliable slug velocity model for accurate analysis of the slug flow formation mechanism. Second is the rare or possible inaccurate classification of the causes of liquid slugs.

Herein, Ph.D. candidate Chengyu Liang, Professor Wei Xiong, and Associate Professor Zhiwen Wang from Dalian Maritime University in collaboration with Professor Rupp Carriveau and Professor David Ting from the University of Windsor conducted a thorough experimental and modeling investigation of the liquid accumulation flow process, slug formation and critical slugging behavior in a hilly-terrain pipeline under ZNLF. In their approach, the liquid slug formation process was divided into three stages based on the amount of liquid accumulation, pipe inclination, gas volume, and the horizontal length of the liquid film. Additionally, the absorption, merging , and outflow of the liquid slug in the pipeline were analyzed and its formation mechanism was divided into three categories. The work is currently published in the journal, Journal of Energy Storage.

The research team showed that the movement of liquid accumulation was affected by changes in gas velocity. Increased inclination angle and liquid accumulation volume were more likely to produce slug flow. Theidealized liquid slug unit could be divided into four regions bubble, liquid slug tail, liquid slug body, and head of liquid slug , leading to continuous absorption, merging, and outflow of the liquid slugs. At small gas velocities, liquid slugs were formed by convective extrusion and wave growth, and they consisted of five processes: slugging, backflow, liquid slug leaving, squeezing, and liquid bridge formation. At high gas velocity, the liquid slug was mainly formed by wave merging and was associated with the generation of small waves at the interface of the liquid and gas.

Furthermore, a theoretical model for the critical slugging velocity was established and the relationships between the gas velocity, liquid film thickness, and maximum growth factor were detailed. As a result, the critical slug velocity corresponding to the maximum growth factor was determined by analyzing the Kelvin-Helmholtz instabilities. The proposed model not only predicted the critical slug velocity effectively but also improved the liquid movement control in the pipeline. In addition, a reduction in energy loss during the transmission of the gas and an improvement in energy storage efficiency was reported.

In summary, the collaborative research team reported the liquid flow characteristics in a hilly-terrain pipeline under ZNLF. The predictions were consistent with the experimental data with a maximum error below 10%. In a joint statement to Advances in Engineering, the authors said their study would contribute to developing advanced high-performance UWCGES to facilitate the transition to renewable and sustainable energy.

Energy security is pivotal for reliable and sustainable socio-economic development. Recently, the generation and consumption of renewable energy have become key drivers to enhancing global energy security. Likewise, the current power system is undergoing a significant transformation towards renewable energy not only to improve energy security but also to reduce greenhouse gas emissions and improve sustainability. This has resulted in intense research and installation of distributed energy resources (DERs) like wind turbines and solar cells.

The large-scale installation of DERs enhances effective energy consumption management and flexibility as it uses localized renewable energy well. Consequently, it benefits the grid and the DER owners, who can sell their excess energy for profits. DER investment has been encouraged by implementing tariff-based schemes, which despite holding great potential, have been criticized for being less competitive and offering limited benefits. As an alternative, peer-to-peer (P2P) energy trading marketing consisting of P2P service providers and energy buyers and sellers has been fronted as a promising model in the energy sharing market.

In a P2P market, both peers can buy/sell to achieve financial gains and flexible energy management without the involvement of utility companies. Energy storage devices like batteries remain a vital component of the residential P2P energy trading market. Despite the environmental and economic benefits of P2P market, the high investment costs of batteries remain a barrier. Although shared energy storage (SES) scheme has been deemed an effective business model due to the benefits of economy of scale, there is still a limited understanding of residential P2P energy markets embedded with SES. Moreover, there are a number of research gaps related to the physical factors, stakeholder structure and pricing mechanism that affect the successful implementation of the P2P market.

On this account, PhD candidate Boshen Zheng, Professor Wei Wei and Professor Shengwei Mei in collaboration with Professor Yue Chen from The Chinese University of Hong Kong and Professor Qiuwei Wu from Technical University of Denmark proposed a novel P2P energy trading market embedded with residential SES units. These units were installed on the energy consumer side and all the participants were considered self-interested and rational in terms of their decisions to buy/sell energy amongst their peers. The market participants and market equilibrium as well as the feasible equilibrium solution strategies, were modeled. The work is published in the journal, Applied Energy.

The authors showed that the implementation of SES could provide more flexible energy consumption strategies for both energy buyers and sellers, as illustrated by the market equilibrium problem consisting of two intertwined games. The first game between energy buyers and sellers was described as non-cooperative, considering the conflicts between these two groups. The second game that comprised energy buyers/SES users was described as a generalized Nash equilibrium problem based on the scarcity of the SES resources. The interactions between the two games led to the problem equilibrium, resulting in endogenous allocation of SES storage capacity and determination of the P2P transaction price.

The market equilibrium problems were solved using Karush-Kuhn-Tucker (KKT) optimality conditions derived from the optimal problems of all the participants coupled with linearization techniques. After concatenating together all the conditions, the market equilibrium was cast as a mixed-integer linear program, where the SES capacity allocation and P2P transactional prices were also determined from the market equilibrium. The P2P2 prices were also influenced by the demand and supply relationships, and they reflected the value of the energy resources. Furthermore, introducing SES could reduce costs and ensure fairness in capacity allocation.

In summary, the design of a P2P energy trading mechanism embedded with SES units for residential consumers and local DERs owners considering the energy pricing mechanisms and stakeholder structure was reported. The benefits of this business model for the transactive energy market included enabling sharing of excess energy with peers, improved competition, improved energy consumption management, higher financial benefits, and enhanced energy security. In a statement to Advances in Engineering, the authors explained that P2P energy trading benefits all participants and is a promising business model for improving the consumption of local renewable energy and overall energy security.

The prime objective of the UN sustainable development goals (SDGs) is to achieve sustainable development in a balanced and integrated manner regarding the three key sustainability dimensions: environmental, social and economic. The energy sector, which is tied to SDG number 7: ensuring affordable, reliable and sustainable clean energy for all, is expected to contribute significantly. Clean and sustainable energy adoption is expected to drastically reduce the carbon footprint of the energy sector. To this end, different countries, especially developed economies, have invested significantly in various forms of clean energy, such as wind energy.

Whereas plenty of publications claim renewables can achieve sustainable development, there are many unanswered questions regarding their effectiveness in balancing the three dimensions of sustainability. For example, previous findings from the United States and German energy transition have produced contradicting results, revealing the difficulty of achieving a low-carbon grid. While the transition could reduce the emissions by a large percentage, the associated tradeoff effects on economic- and social dimensions of sustainability remain unresolved. Moreover, it is expensive, unreliable, and unaffordable in the long term. This warrants a thorough investigation into these issues.

Wind energy is one of the most important renewable energy sources owing to its broader geographical applicability. While it can nominally reduce emissions by 20 – 40% provided it is not puncturing peat and bogs, wind energy-related emissions have not fallen significantly. This leads to an unpopular question of whether wind energy reduces or increases climate gas emissions considering that it requires balancing power, which in most OECD countries is fossil. In addition, considering the entire system, wind might fail to reduce life cycle emissions, especially if it is greater than the displaced life-cycle emissions induced by wind, which is the case in most wind energy grids.

Herein, Professor Jan Emblemsvåg from the Norwegian University of Science and Technology investigated the sustainability of wind energy balanced by fossil energy. Specifically, the author tested the hypothesis that a wind energy grid balanced with gas power plants would reduce emissions than replacing the wind energy with the gas power plants in the same grid considering the life-cycle emissions. The model was constructed based on high-resolution grid data derived from the Irish grid which is ideal because it has the highest share of wind energy on an independent synchronous grid. The data covered four years and comprised the input from 828 Life-Cycle Assessment to enable detailed demand, supply and life-cycle emissions analysis. Monte Carlo simulations were used to sample the model 10000 times to improve result reliability. The work is currently published in the journal, Applied Energy.

According to Professor Emblemsvåg wind energy is renewable but not economically feasible towards low-carbon grids. Although wind energy reduced emissions by 10 – 20%, which supported the hypothesis, achieving a reliable and affordable low-carbon grid using current technology, emissions targets and costs are infeasible. Consequently, it is almost impossible for grid operators to accept high wind penetrations and expect to build affordable carbon grids. This would require higher alternative costs that may further compromise the system’s reliability. Moreover, this is more technically infeasible in smaller grids because the wind production is often too low regardless of the wind capacity installed and is mainly experienced when the balancing power is not based on fossil fuels.

Furthermore, it was worth noting that wind energy is highly dependent on balancing power, such that its emission impacts cannot be analyzed independently. These results are transferable to other grids with large penetration winds balanced using fossil energy – even the world’s largest grid, the European grid. The results contradicted the popular assumption that VREs are sustainable, which is incorrect because they are not independent energy sources as they fluctuate and require balancing. Their sustainability is therefore closely linked to the sustainability of their balancing power.

In summary, the impact of high wind penetration in grids with fossil balancing was analyzed through empirical data and simulations. Overall, the study concluded that wind energy, though renewable, is generally not sustainable when balanced by fossil-driven plants. Thus, it is important for policymakers to concentrate on what is sustainable and not what is renewable. In a statement to Advances in Engineering, Professor Jan Emblemsvåg provided more insights into the sustainability of wind energy which would contribute to improving the sustainability of wind energy and other renewable energy sources stating that “just because a power source has no direct emissions when used, it is the total, systemic life-cycle impact that counts, and because wind energy is not an independent source of energy it must be analyzed including balancing power and system reliability requirements. Over the last 20 years, humanity has reduced its share of fossil primary energy consumption by using wind and solar power by merely 3 percentage points after spending more than 2.5 trillion USD. This says it all”.

Climatic ramifications as a consequence of global pollution have tilted the scale in favor of renewable energy resources. Consequently, it is anticipated that high penetration of renewable distributed generation in medium voltage distribution network will bring challenges in power system operation due to the uncertainty and variability of renewable energy. Additionally, the intermittent nature of renewable energy can potentially intensify the volatility of power flow. To date, numerous studies have assessed the mathematical description of the uncertainty characteristics of renewable distributed generation and the corresponding models of distribution networks management, some of which are optimization-based. In addition, published literature shows that active power management is mainly considered when dealing with the uncertainty in renewable-rich active distribution networks, while reactive power control is commonly applied in the voltage regulation schemes. However, in distribution networks where the R/X ration is relatively high, the active and reactive power are highly coupled in the power flow and network operation.

To this end, researchers from the College of Energy and Electrical Engineering at Hohai University in China: Dr. Junpeng Zhu and Professor Yue Yuan together with Professor Weisheng Wang at the China Electric Power Research Institute developed new active distribution network management scheme coordinating active/reactive power dispatch and network reconfiguration in a multi-stage framework, integrated with centralized and local control methodologies considering the different timescales and communication limits in each management stage. Their goal was to promote the renewable energy consumption and mitigate the system uncertainty through the development of a multi-stage management scheme for renewable-rich medium voltage distribution network. Their work is currently published in International Journal of Electrical Power and Energy Systems.

In the first stage of their approach, a day-ahead dispatch model was developed to promote the renewable distributed generation consumption and decrease the power loss. In the second stage, a model predictive control based rolling optimization model for intra-day operation was proposed to minimize the mismatch of the interactive power at the upstream Grid Supply Point between the day-ahead schedule and intra-day operation. Multiple active management elements such as network reconfiguration and soft open point were then integrated and a novel second order cone programming model for the centralized optimal power flow in the day-ahead and intra-day stages was presented. In the third stage, a decentralized P/Q(V) control strategy of renewable distributed generation inverter for real-time voltage regulation was proposed to ensure system safety and mitigate the fast voltage fluctuation.

Simulation results demonstrated that the proposed management scheme could simultaneously promote the renewable energy consumption and mitigate the system uncertainty. Specifically, it was noted that the power deviation of grid supply point could be decreased notably with a very small sacrifice of renewable distributed generation consumption. Further, the results also showed that network reconfiguration and soft open point had a notable effect in the renewable-rich active distribution network optimization, and that proposed real-time control strategy could avoid the over-limit and fast fluctuation of voltage magnitude through power regulation of RDGs and static var compensators.

In summary, the study justified the idea that in the near future, given any renewable-rich power system, the distribution feeder has the responsibility of local uncertainty management in the power interaction with upstream system. Remarkably, the effectiveness of the proposed management scheme was demonstrated in a test system integrating a standard network and real-world data of load and renewable distributed generation output profiles. In a statement to Advances in Engineering, Dr. Junpeng Zhu, first author highlighted that their work put forth invaluable contributions that will facilitate further exploration of distributed communication and control frameworks and strategies.

Cold regions in the world are divided into polar and sub-polar and are characterized by the presence of snow and ice at least part of the year. Residents of such areas are obliged to heat the living space. For instance, in china -where over 70% of the land lies in the cold region- inhabitants are subjected to heating time in excess of four months annually. Technically, such a population is predisposed to two heating methods: the central heating and the household heating. Over the years, researchers have developed modern heating systems that help circumvent the obvious disadvantages of traditional coal-powered heating techniques, i.e. low efficiency, high energy consumption and serious pollution. To this end, integrated heating system that utilizes renewable energy sources, such as: solar energy, bio-gas, geothermal and wind energy have been proposed. However, several shortfalls have been reported: such as, solar intermittency and instability for the case of solar energy, very low temperatures for biogas fermentations, etc. Therefore, in the wake of persistent campaigns to minimize consumption of fossil-based fuels, there is a growing concern among researchers on how best to heat up living spaces in such regions, where even the most abundant renewable resources seem scarce when most required.

Biomass energy is an effective supplement in low environment temperature, which has high energy density and stable energy output. With this in focus, a group of researchers from the College of Energy and Power Engineering at Lanzhou University of Technology : Professor Zhang Dong, Dr. Li Bingyang and Dr. Li Jinping, in collaboration with Dr. Zhao Qintong at the Key Laboratory of Complementary Energy System of Biomass and Solar Energy in China proposed a multiple renewable energy complementary heat pump system (MREHP). In the proposed MREHP system, the researchers aspired to use solar energy, biomass energy and air heat as the main heat source. Their work is currently published in the research journal, Solar Energy.

Technically, the proposed MREHP system was mainly composed of solar collector unit, constant temperature biogas pool unit and heat pump unit. The team adopted three modes for the system: i.e. air source (Mode 1), solar-air energy (Mode 2) and solar-biomass-air energy (Mode 3). The researchers carried out experimental studies where the average outdoor and indoor temperatures in three modes were −8.64 °C, −12.43 °C, -11.18 °C and 17.91 °C, 21.31 °C, 20.17 °C, respectively. Also, the averages coefficient of performances (COP) of the heat pump were 1.70, 2.32 and 2.26, respectively.

The authors reported that, in Mode 1, air heat input accounted for 41.09%. The proportion of air heat input in Mode 2 was seen to greatly reduce, while as the solar energy input system accounted for 40.32%. Further, the researchers reported that in Mode 3, the energy input of air heat, solar energy and biomass energy were basically equal. Overall, the utilization of renewable energy in the three modes of proposed system accounted for 41.09%, 57.34% and 55.67%, respectively.

In summary, the study presented a heat pump system based on multiple renewable energies as auxiliary heat sources. The proposed system utilized air, solar and biomass energy as auxiliary energy for heat pump systems. The researchers established that renewable energy utilization in Mode 2 and Mode 3 exceeded 50%. Remarkably, the system could meet the indoor space heating demands under different conditions. In a statement to Advances in Engineering, Professor Zhang Dong highlighted that the system reduced the consumption of electricity by utilizing renewable energy.

In recent years, the wide use of renewable energy and its acceptance has been a great success. This tremendous achievement can be credited to the increased global awareness and acknowledgment of the impact of carbon-based fuels on the environment, and the integrability of the variable renewable energy (wind power and photovoltaic) to existing power grid systems. As such, the penetration of flexible high voltage DC(HVDC) transmission technology in the smart grid has greatly impacted the configuration of power systems. Well, it is widely known that the high penetration of renewable energy and AC-DC hybrid systems are doubtlessly two essential features of future power systems and will ever change the configurations of power systems.

The test systems that reflect the true feature of a real power system are vital tools in this research area. nonetheless, there lacks a standard test system for modern transmission expansion planning research, specifically under high variable renewable energy domain. To address this, researchers from the Tsinghua University in China: Zhenyu Zhuo (PhD candidate), Dr. Ning Zhang, Jingwei Yang and Professor Chongqing Kang in collaboration with Charlie Smith at the Energy Systems Integration Group in the United States and Dr. Mark O’Malley and Dr. Benjamin Kroposki from the National Renewable Energy Laboratory in Colorado developed a new 38-bus TEP test system based on a real regional power grid in China with high variable energy penetration: the HRP-38 system. “HRP” stands for high renewable penetration. They aspired to introduce a transmission expansion planning (TEP) dedicated test system with the feature of high variable renewable energy prediction and AC-DC hybrids. Their work is currently published in the research journal, IEEE Transactions on Power Systems.

The researchers proposed a test system that considered high VRE penetration of more than 30% energy share, which would be an important feature in many power systems in the future. In the proposed approach, both AC and DC candidate lines were given to provide sufficient transmission planning alternatives. The complexity of the test system was well balanced considering calculation tractability and the ability to test the performance of the TEP planning model. The network topology, generation mix, and load characteristics were also described in detail. The investment cost of transmission assets, detailed parameter of units and other necessary information required for TEP are provided explicitly in the dataset. The codes for operation simulation applied in this paper is uploaded in the dataset linkage. Based on the programs, researchers can compare their customized planning scheme with the benchmarks present in this paper.

The authors reported that their variable renewable energy output was able to satisfy 30.1% of the load energy demand. Additionally, both AC and DC candidate lines were given to provide sufficient transmission planning alternatives. Indeed, the intricacy of the test system was seen to be well balanced considering calculation tractability and the ability to test the performance of the TEP planning model.

In summary, the study presented a 38-bus test system i.e. the HRP-38 system, dedicated to TEP. Overall, the contribution of the study was in two folds: first, the development of a medium size test system that includes hourly variable renewable energy data and an AC-DC hybrid candidate branch set, and second, the provision of several TEP schemes with different optimization settings, that could offer a benchmark with which different studies could be directly compared. In a statement to Advances in Engineering, Professor Chongqing Kang highlighted that the developed system could facilitate comparisons and collaborations between different TEP studies worldwide.

This work is one of the outcomes of a five-year Major Project under National Key Research and Development Program of China, named Fundamental Theory of Planning and Operation for Power Systems with High Share of Renewable Energy Generations (No. 2016YFB0900100), leading by Professor Chongqing Kang. The project addresses two challenges in high penetration renewable energy system: 1) how to address significant uncertainty and risks brought by the stochastic and intermittent renewable energy into the power system planning and operation. 2) the unknown control mechanisms and stability problems in power system operation due to the heavy integration of converter based renewable energy and DC transmission systems.