Coupling dalapon biodegradation with electricity generation in microbial fuel cells.

Sustainable treatment of acetaminophen wastewater with simultaneous water recovery and energy generation in osmotic microbial fuel cells.

Microbial fuel cell-powered electrosorption coupled system for tetracycline removal and antibiotic resistance genes control.

Mechanochemistry synthesis of Al-fumarate MOF towards high-performance in solar-driven interfacial water evaporation and electricity generation

Hydrodynamic intensification of red soil microbial fuel cells: enhanced Acid Red 73 degradation and bioelectricity generation under free-fall influent.

Temperature-dependent performance of Li Mg ferrite for renewable electricity generation via hydroelectric cells

Electricity generation coupled with hydrogen production at room temperature by lignin-derived aldehyde flow fuel cell based on Ag-CuxO/CF electrocatalyst

Hydrovoltaic electricity generators (HEGs) have emerged as a class of distinctive green energy harvesters that convert the energy of ubiquitous water, including moisture and liquid water, into electricity through water-induced interfacial processes at solid-gas and solid-liquid interfaces. While early studies primarily focused on empirical material screening, recent advances have increasingly demonstrated that HEG performance is governed by the coupled interplay between interfacial chemistry and architectural design. Chemically, proton dissociation, surface charge regulation, ion-solvation dynamics, and electric double layer formation govern charge separation and transport at molecular scale. Architecturally, hierarchical and deliberately engineered structures, including pore design, asymmetric configurations, and multilayer coupling, can be implemented across one- to three-dimensional device formats, thereby dictating water distribution, ion diffusion, and mechanical integrity. This review establishes a unified chemical-architectural framework that correlates interfacial molecular processes with device-level architectures and macroscopic electrical outputs. We categorize representative HEG systems according to their structural motifs while elucidating the governing physicochemical mechanisms that drive energy conversion. Building on this foundation, we identify key design principles and functional objectives toward high-output, durable, and multifunctional HEGs. Finally, we outline future directions emphasizing synergistic chemical control and architectural innovation to realizing intelligent, sustainable, and scalable hydrovoltaic energy systems.

In response to the growing need for accurate forecasting of electricity generation from PV installations, which is crucial both for enhancing self-consumption and for balancing the power grid, this study presents a comparative analysis of selected machine learning models. The research focuses on the XGBoost algorithm and LSTM neural networks, applied to predict PV energy production based on meteorological data and historical generation records from four medium-sized PV installations (30–50 kWp) located in Poland. Meteorological data were retrieved from open sources and combined with actual production measurements to build the training dataset. This paper discusses the challenges posed by these data at the given latitude, as well as issues related to processing data from newly launched installations. The performance of both approaches was evaluated in short- and medium-term forecasting, with particular attention to prediction accuracy, robustness to noisy data, and the ability to capture nonlinear relationships.

Directional propulsion of Leidenfrost droplets is effectively achieved on asymmetric microgroove arrays fabricated via one-step femtosecond laser direct writing. This is realized by simply setting the spacing of the laser scanning lines slightly smaller than the width of a single microgroove during the line-by-line laser scanning process. Because a femtosecond laser can process any given material, this method for driving Leidenfrost droplets is applicable to a wide range of material substrates, as experimentally verified on superhard alloys (magnesium alloy and titanium alloy), semiconductors (monocrystalline silicon and silicon carbide), and ceramics (aluminum nitride, sapphire). The proposed strategy also enables the transport of Leidenfrost droplets on composite surfaces made of different materials as well as the directional propulsion of various volatile liquids. Leveraging the controlled motion of Leidenfrost droplets and the flexible design of surface microstructures via femtosecond laser processing, several applications designed for extremely high-temperature environments have been realized, such as the trapping of Leidenfrost droplets, targeted cooling, self-rotation of Leidenfrost droplets (converting thermal energy into mechanical energy), and electricity generation.

Hybrid renewable energy systems (HRESs), mixing conventional and renewable power sources and occasionally storage units, have become the norm regarding electricity generation. Robust long-term planning of such systems requires stakeholders to test different layouts and system configurations, while their operational management relies on forecasting surpluses and deficits to achieve optimal decision making. However, both tasks, which in fact constitute a flow allocation problem across power networks, are subject to multiple peculiarities, arising from the nonlinear dynamics of the underlying processes, subject to numerous technical and operational constraints. Interestingly, a mutual problem emerges in water resource systems, also comprising network-type storage, abstraction and conveyance components. In this vein, triggered from well-established simulation approaches from the water domain, we introduce a generic (i.e., topology-free) and time-agnostic framework, the key methodological elements of which are: (a) the graph-based representation of the power fluxes; (b) the effective handling of energy uses and constraints through virtual nodes and edges; (c) the implementation of priorities via proper assignment of virtual costs across all graph components; and (d) the configuration of the overall problem as a network linear programming context, which allows the use of exceptionally fast solvers. Specific adjustments are required to address highly complex issues within HRESs, particularly the representation of conventional thermal and pumped-storage hydropower units, as well as the power losses across transmission lines. The modeling approach is stress-tested by means of configuring a hypothetical HRES in a non-interconnected Aegean island, i.e., Sifnos, Greece.

This article examines the role of decarbonization in achieving the sustainable development goals of the Republic of Belarus. It identifies the main sources of greenhouse gas emissions and strategic areas for their reduction. Particular attention is paid to measures to improve the efficiency of the existing energy system, the development of renewable energy (biomass and biogas), and electricity generation at the Belarusian Nuclear Power Plant. It is emphasized that the transition to low-carbon technologies contributes to improved environmental quality, the fulfillment of international commitments (the Paris Agreement) to slow global warming, and provides economic and social benefits.

This study examines the evolution of the European Union’s (EU) energy security and import dependence over the period 2014–2023, shaped by global energy price shocks, the COVID-19 pandemic, and Russia’s war against Ukraine. This research aims to explore how the structure of energy imports, domestic production capacities, and the composition of electricity generation shape the vulnerability of EU Member States. It highlights that energy is not only an economic input but also a determinant of social stability and political space. The analysis is based on Eurostat data for 27 Member States. This study combines several methods: panel regression to explore the structural determinants of energy dependence, absolute and relative volatility indicators to measure exposure to shocks, and K-means clustering to map heterogeneity across Member States. The comparison between the pre-2020 and post-2020 periods serves as a robustness check. The results point to three main conclusions. First, natural gas and oil imports remain the primary source of dependency, while domestic electricity generation and balanced gas supply mitigate vulnerability. Second, based on volatility, smaller Member States—particularly the Baltic States and Malta—are disproportionately exposed to shocks. Third, Member States can be grouped into three clusters, although the post-2020 crisis has partly rearranged the grouping of countries. The policy lesson is clear: reducing energy dependency requires diversification, targeted support for smaller Member States, strengthening crisis management capacities, and accelerating the green transition. Energy security and sustainability are not contradictory but mutually reinforcing objectives that will determine the future resilience of the EU.

<div>The integration of electric vehicle charging station (EVCS) and renewable distribution generation (RDG) in the grid affects the grid voltage, power losses, and system instability in the distribution system, therefore the article presents an approach for optimal placement and sizing of EVCS and RDG using an optimization approach named as modified particle swarm optimization (MOPSO) in radial distribution network (RDN). The efficacy of the optimization approach is demonstrated under both balanced and unbalanced dynamic load conditions in the IEEE 33-bus system. The influence of EVs and RDG on the RDN is analyzed by considering the maximum possible cases, e.g., 13 different scenarios, which replicate real-world scenarios. These results are validated using DIgSILENT Power Factory Software. The proposed research also covers Techno-Economic Assessment using HOMER software, which may enhance visibility of the renewable distribution generation importance in the current scenario.</div>

Abstract The UK Government plans to bring forward the decarbonisation of British electricity generation from 2035 to 2030, and the UK National Energy Systems Operator has provided details of how this may be achieved by adaption of the previous strategy for 2035. At the same time, there is decreasing public confidence in and support for the policies for net-zero. It is timely to reconsider from first principles the 2035 plans as adapted for 2030. Analysing the 2023 electricity generation data, we find that the problems caused by the variability and intermittency of wind and solar generation have been grossly underestimated. Difficulties of both surpluses and shortfalls of renewable generation are more serious and more frequent than assumed. We model paths to 2030 with simplifications designed to reveal the underlying principles more clearly. Key findings are that expansion of electrification before 2030 aggravates the worst problem of variability (shortfalls) without any proven savings yet in carbon emissions; it should be undertaken only after decarbonisation. Abatement of the CO2 emissions from fossil gas use by carbon capture and sequestration – if feasible – should be introduced and expanded as fast as possible both as an immediate and independent action to rapidly reduce CO2 emissions and also to handle the shortfalls. Sheddable loads such as electrolysis of hydrogen and synthesis of hydrocarbons need to be introduced very soon to start to exploit the surpluses of renewable generation as they begin to arise. Already, curtailment of surplus generation in 2025 has cost over £1 billion and our model predicts a cumulative cost of about £20 billion by 2030 in the absence of sheddable loads.

This study evaluates the viability and potentiality of solar energy for sustainable power generation in College of Environmental Studies, Kaduna Polytechnic, Nigeria. Descriptive survey design will be adopted. The study population comprises of 4569 respondents. A sample of 400 will be drawn using purposive sampling technique. The data will be analyzed using descriptive statistics of frequency counts and simple percentage. This study evaluates the viability and potential of solar energy as an alternative source of power generation at the College of Environmental Studies, Kaduna Polytechnic, and Kaduna, Nigeria. The increasing demand for reliable electricity supply within tertiary institutions, coupled with the persistent challenges of grid instability and rising energy costs, necessitates the exploration of sustainable and renewable energy solutions. Solar energy, owing to Kaduna State’s high solar irradiance and favorable climatic conditions, presents a promising option for decentralized and clean power generation. The research adopts a mixed-method approach involving on-site energy audit, load assessment of academic and administrative buildings, solar radiation data analysis, and preliminary system design simulations. Data were collected through field measurements, institutional electricity consumption records, and secondary meteorological sources. The study estimates the average daily energy demand of the College and compares it with the potential photovoltaic (PV) energy output based on available rooftop and open-space areas suitable for solar panel installation. Findings indicate that the College possesses significant rooftop and land area suitable for photovoltaic deployment, with adequate solar insolation levels to support sustainable energy production throughout the year. Financial analysis reveals that although the initial capital investment is substantial, long-term benefits include reduced operational costs, lower dependence on grid electricity and diesel generators, decreased carbon emissions, and improved energy reliability. The study further highlights environmental, economic, and social benefits associated with adopting solar technology within the institution. The research concludes that solar energy is both technically feasible and economically viable as a supplementary or alternative energy source for power generation at the College of Environmental Studies, Kaduna Polytechnic. It recommends phased implementation, institutional energy policy development, and integration of solar systems into academic research and training to enhance sustainability and energy security.

A photovoltaic (PV) and battery-based energy system can provide the necessary and sufficient electric power to off-grid power system networks due to the technological advancements in both performance improvement and lower system cost. The absence of reactive power in direct current (DC) power system networks has several advantages over corresponding alternating current (AC) power system networks. In this paper, we have investigated a case study for the PV farm coupled with a battery energy storage system (BESS) as a stand-alone power system network in the Red Sea New City, Kingdom of Saudi Arabia. The study consists of two cases, which are the DC battery coupling configuration for the AC power network system and the end-to-end DC (EEDC) configuration for the power network system. Using the same size of solar PV farm and battery storage, we have compared the performance of the two case configurations of different power system networks after thirty years of operation. The results show that implementing the EEDC power system network has a major advantage in improved energy efficiency of the power system (directly related to cost-effectiveness) and lower capital investment of the power system that includes electric power generation, transmission, distribution, and utilization for all applications, including artificial intelligence-based data centers.

This study presents a comprehensive sustainability assessment of power generation configurations integrating blue hydrogen combined cycles with different carbon capture technologies. Four scenarios were systematically evaluated through process simulation and multi-criteria decision analysis, encompassing technical, economic, environmental, and social indicators. The assessed configurations comprise a conventional natural gas combined cycle, representing the current carbon-based benchmark, alongside three innovative blue hydrogen combined-cycle pathways incorporating distinct carbon capture technologies. The results demonstrate pronounced trade-offs among the evaluated scenarios: although the conventional configuration exhibits superior economic performance, it presents the least favorable environmental outcome, with a carbon intensity approximately an order of magnitude higher than that of the hydrogen-based alternatives. In terms of electricity generation potential, hydrogen-fired combined cycles achieve comparable energy performance, delivering 0.103 kWh per kJ of hydrogen relative to 0.116 kWh per kJ of natural gas. For all evaluated configurations, the chemical absorption case achieved the highest overall sustainability performance, attaining a sustainability degree of 1.41, corresponding to a 25% improvement over the conventional process. The findings of the present work underscore the potential of integrating blue hydrogen combined cycles to substantially improve the sustainability of electricity generation while supporting decarbonization pathways within the energy sector.

Decarbonising the building stock relies on the strategies of efficiency, sufficiency and consistency. Building stock energy scenarios (BSES) help evaluate the effect of these measures by modelling the existing building stock and using appropriate inputs, but must also account for boundary conditions, such as the structure of the energy system. In renewable energy (RE)-based systems, high summer generation contrasts with winter building stock demand, creating a seasonal gap. This study presents two BSES for Austria: a BAU scenario with standard decarbonisation measures (HVAC change and renovation rates) and a BEST scenario with more ambitious rates. Three energy system configurations are considered: (A) a demand-independent energy system based on current data of the Austrian electricity generation, (B) a RE-based generation system in terms of net-annual balance with the energy demand but connected with surrounding countries, and (C) an autarkic RE-based system with seasonal storage (based on green hydrogen). The key performance indicators (KPIs) used to assess the decarbonisation of a system are the equivalent CO 2 emissions, the load cover factor (LCF) and the required PV generation to reach energy autarky. The results show that the assumption of the energy system structure has a strong impact on the effectiveness of different measures. Hence, the choice of the KPIs is sensitive with respect to the boundary conditions. A building stock within a RE-based domestic energy system relying on energy imports to cover the winter gap cannot be considered fully decarbonised, if the import electricity mix is not known. On the other hand, an autarkic system is not feasible if the domestic demand exceeds the RE potential. The RE mix of the generation system, along with the load characteristics, has an impact on the winter gap magnitude, consequently influencing the energy imports or the seasonal storage requirements.

The rapid growth of renewable electricity generation introduces technical challenges that were previously uncommon. These include, for example, problems with exceeding the permissible voltage values in network nodes, overloading of transformers and line sections located behind the transformer, as well as balance problems. This article proposes an original methodology for eliminating these problems. Four objective functions reflecting different operator priorities were used. Attention is drawn to the increasing importance of the development of electricity storage. The results confirm that coordinated optimisation of voltage regulation, energy storage, and flexible load management enables increased renewable energy connection capacity while reducing power losses and improving the grid voltage profile. The case study results demonstrate the effectiveness of the proposed approach under the considered operating scenarios. The proposed tool can support network operators in managing MV grid operation under the considered scenarios. The ongoing energy transition requires network operators to react quickly to emerging problems. Therefore, advanced computational methods are needed to mitigate operational risks and respond to emerging constraints.