Effects of mechanical properties of the underburden on induced seismicity along a basement fault during hydrogen storage in a depleted reservoir.

This study models the geomechanical deformation of a depleted gas field, wherein gaseous hydrogen is stored in a North Sea reservoir, and is cyclically injected and withdrawn. A fault is modeled within the underburden, and its slip is investigated during a three year storage period. Parametric simulations are conducted to study the influence of the underburden mechanical properties, such as Young's modulus, Poisson's ratio, and permeability on induced seismicity. The fault is predominantly in stick during the bulk of the injection, storage, and withdrawal periods, but minor fault slip ( < 4 mm) occurs shortly after a change in operational regime. The Young's modulus of the underburden unit has the strongest control on fault slip. To reduce the seismic hazard, an underburden with low Young's modulus ( < 15 GPa), high Poisson's ratio ( > 0.25), low Biot coefficient, and low permeability ( < 1 × 10 - 19 m2) is found to be most suitable for hydrogen storage.

ZnIn2Se4 nanoparticles photocatalyst for efficient solar fuel production.

Selecting a suitable photocatalyst to establish the Z-scheme heterojunction which is accompanied by effective photogenerated hole and electron separation, is one of the advantageous strategies for efficient photocatalytic solar energy conversion. Therefore, we prepared a ZnIn2Se4 nanoparticles photocatalyst to build a double Z-scheme heterojunction with mixed-phase TiO2 nanofibers, boosting photocatalytic solar fuel preparation. The result of X-ray photoelectron spectroscopy confirmed the existence of interfacial chemical bonds and internal electric fields. The interfacial Ti-Se bond is regarded as a channel and the internal electric field serves as the driving force for electron transfer. And the composite photocatalyst exhibits a great hydrogen evolution rate of 0.11 mmol g-1 h-1. From a forward-working perspective, this work proposes a ZnIn2Se4 nanoparticles photocatalyst for efficient solar fuel conversion, promoting the application of bimetallic selenide photocatalyst in the field of photocatalysis.

Infrastructure adequacy for electricity trading in East Africa.

A well-designed and managed electricity supply infrastructure system is essential for integrated energy market. This paper tracks progress on infrastructure for electricity trading in East Africa integrated electricity market. Using data on electricity infrastructure targets in Master Plan 2013-2023 and actual infrastructure delivered by 2022, we conducted earned value analysis (EVA) to establish whether the completed generation and transmission infrastructure can adequately facilitate electricity trading across EAC countries. Findings show that by 2022 the region had realized 54% of the 12,567MW planned generation capacity and 211% of transmission network targets. Investment inflows for infrastructure have been faster than anticipated with actual variance of 325%. This triggered 47% earned value in surplus load worth US$357million of trade, despite actual electricity trading not happening at the same pace. We construed some merit-order conditions for iterative planning to synchronize generation infrastructure with transmission infrastructure for trade efficiency.

A self-powered and self-sensing knee negative energy harvester.

Wearable devices realize health monitoring, information transmission, etc. In this study, the human-friendliness, adaptability, reliability, and economy (HARE) principle for designing human energy harvesters is first proposed and then a biomechanical energy harvester (BMEH) is proposed to recover the knee negative energy to generate electricity. The proposed BMEH is mounted on the waist of the human body and connected to the ankles by ropes for driving. Double-rotor mechanism and half-wave rectification mechanism design effectively improves energy conversion efficiency with higher power output density for more stable power output. The experimental results demonstrate that the double-rotor mechanism increases the output power of the BMEH by 70% compared to the single magnet-rotor mechanism. And the output power density of BMEH reaches 0.07 W/kg at a speed of 7 km/h. Furthermore, the BMEH demonstrates the excitation mode detection accuracy of 99.8% based on the Gate Recurrent Unit deep learning model with optimal parameters.

19th-century thermosiphon ventilation and its potential for heat recovery in buildings today.

A forgotten thermosiphon scheme is found in Montreal's former Royal Victoria Hospital and traced back to the original Center Block of Canada's Parliament Hill. This discovery inspires an investigation into the fluid mechanics of heat recovery with buoyancy ventilation, where interior spaces are arranged in an open thermal loop with heat exchange through partition walls. Flow visualizations with physical models are used to corroborate the archival evidence and show how the historical scheme worked. The scheme is then generalized, defining a criterion for steady unidirectional flow (λ>1) and a heat recovery limit when room temperatures upstream and downstream reach equilibrium (ε≤50%). This mathematical model is validated experimentally, demonstrating steady flow (λ∼2.21) close to the efficiency limit (ε∼0.40) with a balanced thermal design (NTU∼1). Further analysis shows significant heating savings are possible in mildly cold seasons compared to natural displacement (74%) and natural mixing (60%) ventilation.

Data-driven multi-objective optimization for electric vehicle charging infrastructure.

This paper presents a data-driven methodology combining simulation and multi-objective optimization to efficiently implement transportation policy commitments, using as a case study the electric vehicle (EV) charging infrastructure in Newcastle upon Tyne, United Kingdom. The methodology leverages a baseline simulation model developed by our industry partner, Arup Group Limited, to estimate EV demand and quantities from 2020 to 2050. Four future energy scenarios are considered, and a multi-objective optimization approach is employed to determine the optimal types, locations, and quantities of charging points, along with the corresponding total capital and operational expenditures and charging point operating hours. Quantitatively, the variations of the portions of different types of charging points for the four scenarios are relatively small and within 3% range of the total number of charging points. The optimal solutions put priority on the slower charging points, with faster charging points having smaller portions each around 10%-13%.

Techno-economic analysis of residential building heating strategies for cost-effective upgrades in European cities.

The energy crisis in Europe requires cost-effective evaluations of residential heating strategies to reduce costs and mitigate greenhouse gas emissions. This research studied different heating systems in China and Europe. Based on heating energy surveys, simulation models were developed and further expanded for European cities. Monte Carlo analyses were conducted to understand the heating demand and utility costs in Rome, Madrid, and Athens. The sensitivity analysis found that electrifying heating systems with heat pumps can reduce household heating costs and mitigate European cities' dependence on natural gas. However, the high upfront investment may hinder the cost-effective deployment of high-performance heat pump systems. Building envelope retrofits can also provide plausible energy savings despite relatively long payback periods. Financial incentive analyses were conducted to quantify how fiscal measures can improve technologies' techno-economic performance. Finally, the paper provided policy recommendations on future building cost-effective retrofits and heating electrification in Europe.

Clustering and dynamic recognition based auto-reservoir neural network: A wait-and-see approach for short-term park power load forecasting.

This paper proposes a novel clustering and dynamic recognition-based auto-reservoir neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park microgrids. In CDbARNN, the available load sets are first decomposed into several clusters via K-means clustering. Then, by extracting characteristic information of the load series input to CDbARNN and the load curves belonging to each cluster center, a dynamic recognition technology is developed to identify which cluster of the input load series belongs to. After that, the input load series and the load curves of the cluster to which it belongs constitute a short-term high-dimensional matrix entered into the reservoir of CDbARNN. Finally, reservoir node numbers of CDbARNN which are used to match different clusters are optimized. Numerical experiments conducted on STLF of an actual industrial park microgrid indicate the dominating performance of the proposed approach through several cases and comparisons with other well-known deep learning methods.

Blue and green ammonia production: A techno-economic and life cycle assessment perspective.

Blue and green ammonia production have been proposed as low-carbon alternatives to emissions-intensive conventional ammonia production. Although much attention has been given to comparing these alternatives, it is still not clear which process has better environmental and economic performance. We present a techno-economic analysis and full life cycle assessment to compare the economics and environmental impacts of blue and green ammonia production. We address the importance of time horizon in climate change impact comparisons by employing the Technology Warming Potential, showing that methane leakage can exacerbate the climate change impacts of blue ammonia in short time horizons. We represent a constrained renewable electricity availability scenario by comparing the climate change impact mitigation efficiency per kWh of renewable electricity. Our work emphasizes the importance of maintaining low natural gas leakage for sustainability of blue ammonia, and the potential for technological advances to further reduce the environmental impacts of photovoltaics-based green ammonia.

A high-efficient and salt-rejecting 2D film for photothermal evaporation.

The solar-driven desalination is seen as a sustainable way to combat water scarcity. However, the solar steam generation efficiency has long been restricted by the high vaporization enthalpy of water and low energy density of natural sunlight. We introduced graphene oxide (GO) cross-linked with polyethyleneimine (PEI) as the photothermal material, with the enriched ammonic functional groups in modified GO membrane (GPM) activating water molecules to evaporate with much lower energy consumption. The vaporization enthalpy at the air-film interface is reduced up to 42% in GPM film by tuning the thermodynamic states of water. Consequently, GPM film enables a high evaporation rate of 2.48 kg m-2 h-1 with 95.7% energy conversion efficiency under 1 sun. With the aid of positive charges introduced by hydrolysis of PEI, the GPM exhibits excellent salt resistance and delivers an evaporation rate around 1.8 kg m-2 h-1 when treating 20 wt % NaCl solution.

Achieving the UN's sustainable energy targets through dynamic operating limits.

Despite the world's relentless efforts to achieve the United Nations' sustainable energy target by 2030, the current pace of progress is insufficient to reach the objective. Continuous support and development across various domains of the energy sector are required to achieve sustainability targets. This article focuses on the potential of dynamic operating limits to drive the world's sustainability efforts, specifically in addressing critical challenges of distribution networks of the power system by progressively setting the nodal limits on the active and reactive power injection into the distribution network based on data-driven computer simulation. While the importance of dynamic operating limits has recently been recognized, its crucial role in the residential energy sustainability sector, which requires a significant push to provide universal energy access by 2030, has not been adequately investigated. This perspective explains the fundamental concepts and benefits of dynamic operating limits in encouraging the adoption of distributed renewable energy resources in the residential sector to support the United Nation's sustainable energy objective. Additionally, we discuss the limitations of computing this limit and applying it to the electricity network and some motivational models that can encourage electricity customers to come forward to address the challenges. Finally, we explore new research and implementation prospects for designing comprehensive, dependable, accountable, and complementary dynamic operating limit programs to accelerate the attainment of sustainable energy targets.

Toward next-generation fuel cell materials.

The fuel cell's three layers-anode/electrolyte/cathode-convert fuel's chemical energy into electricity. Electrolyte membranes determine fuel cell types. Solid-state and ceramic electrolyte SOFC/PCFC and polymer based PEMFC fuel cells dominate fuel cell research. We present a new fuel cell concept using next-generation ceramic nanocomposites made of semiconductor-ionic material combinations. A built-in electric field driving mechanism boosts ionic (O2- or H+ or both) conductivity in these materials. In a fuel cell device, non-doped ceria or its heterostructure might attain 1 Wcm-2 power density. We reviewed promising functional nanocomposites for that range. Ceria-based and multifunctional semiconductor-ionic electrolytes will be highlighted. Owing to their simplicity and abundant resources, these materials might be used to make fuel cells cheaper and more accessible.

Death spiral of the legacy grid: A game-theoretic analysis of modern grid defection processes.

Decreasing costs of distributed generation and storage, alongside increasing network charges, provide consumers with a growing incentive to defect from the main grid. On a large scale, this may lead to price inflation, hindrance of the energy transition, and even a "death spiral" - a domino effect of disconnections. Here, we develop a game-theoretic framework that demonstrates how conflicting interests among consumers - an aspect that previous studies overlooked - may lead to complex dynamics of grid defection. Our results reveal that although individual consumers benefit from staying connected at the distribution level, the defection of small energy communities from the grid may lead to the defection of larger communities. We also demonstrate that centralized design approaches may lead to inefficient outcomes, e.g., redundant grid expansions, because of the inherent inability to predict potential defections. However, we indicate how, by properly incorporating defection considerations into the grid's design, social welfare can be improved.

Conspiracy spillovers and geoengineering.

Geoengineering techniques such as solar radiation management (SRM) could be part of a future technology portfolio to limit global temperature change. However, there is public opposition to research and deployment of SRM technologies. We use 814,924 English-language tweets containing #geoengineering globally over 13 years (2009-2021) to explore public emotions, perceptions, and attitudes toward SRM using natural language processing, deep learning, and network analysis. We find that specific conspiracy theories influence public reactions toward geoengineering, especially regarding "chemtrails" (whereby airplanes allegedly spray poison or modify weather through contrails). Furthermore, conspiracies tend to spillover, shaping regional debates in the UK, USA, India, and Sweden and connecting with broader political considerations. We also find that positive emotions rise on both the global and country scales following events related to SRM governance, and negative and neutral emotions increase following SRM projects and announcements of experiments. Finally, we also find that online toxicity shapes the breadth of spillover effects, further influencing anti-SRM views.

Integration of daytime radiative cooling and solar heating.

In recent years, sustainable energy development has become a major theme of research. The combination of solar heating and daytime radiative cooling has the potential to build a competitive strategy to alleviate current environmental and energy problems. Several studies on the combination of daytime radiative cooling and solar heating have been reported to improve energy utilization efficiency. However, most integrations still have a low solar/mid-infrared spectrum regulation range, low heating/cooling performance, and poor stability. To promote this technology further for real-world applications, herein we summarize the latest progress, technical features, bottlenecks, and future opportunities for the current integration of daytime radiative cooling and solar heating through the switch mode (including electrical, thermal-responsive, and mechanical regulations) and collaborative mode.

Toward evaluating the effect of technology choices on linkages between sustainable development goals.

Linkages between the Sustainable Development Goals (SDGs) have sparked research interest because a better understanding of SDG co-benefits may enable faster progress on multiple sustainability fronts. However, SDG linkages are typically analyzed without considering the technologies used to implement a primary SDG, which may have secondary effects on other SDGs. Here, we outline an approach to study this problem by connecting the industries and services required to produce a technology to the United Nations SDG indicator framework, using SDG7 and four energy technologies as an illustrative case. We find that all technologies in our set involve potential co-benefits with SDGs 1, 8-10, 12-13, and 17, and trade-offs with SDGs 6, 8-9, 11-12, and 14-15. Deployment services primarily induce co-benefits; manufacturing has mixed impacts. Our work sheds light on the technology characteristics (e.g., scale, high- or low-tech) that influence linkages while also pointing to SDG-relevant characteristics not captured by UN indicators.

An overview of deterministic and probabilistic forecasting methods of wind energy.

In recent years, a variety of wind forecasting models have been developed, prompting necessity to review the abundant methods to gain insights of the state-of-the-art development status. However, existing literature reviews only focus on a subclass of methods, such as multi-objective optimization and machine learning methods while lacking the full particulars of wind forecasting field. Furthermore, the classification of wind forecasting methods is unclear and incomplete, especially considering the rapid development of this field. Therefore, this article aims to provide a systematic review of the existing deterministic and probabilistic wind forecasting methods, from the perspectives of data source, model evaluation framework, technical background, theoretical basis, and model performance. It is expected that this work will provide junior researchers with broad and detailed information on wind forecasting for their future development of more accurate and practical wind forecasting models.

Parametric optimization and structural feature analysis of humic acid extraction from lignite.

Humic acid (HA) is a complex organic compound made up of small molecules. A variety of raw materials are used to manufacture HA, due to which the structure and composition of HA vary widely. In this study, nitric acid oxidation of two coal samples from Lakhra (Pakistan) was followed by HA extraction using 2.5, 3.0 and 3.5% KOH solutions. The impact of different operating parameters such as; the effect of KOH concentrations, KOH-coal proportion, extraction time and pH range influencing the HA extraction efficiency was optimally investigated. Commercial HA applications possess numerous challenges, including valuable applications and sub-optimal extraction techniques. A significant limitation of conventional experimental methods is that they can only investigate one component at a time. It is necessary to improve the current processing conditions, this can only be achieved by modelling and optimization of the process conditions to meet market demands. A comprehensive evaluation and prediction of HA extraction using Response Surface Methodology (RSM) are also being reported for the first time in this study. The maximum HA extraction efficiency of 89.32% and 87.04% for coal samples 1 and 2 respectively was achieved with the lowest possible pH of 1.09 (coal sample 1) and 1(coal sample 2), which is remarkably lower as compared to those reported in the literature for conventional alkaline extraction process. The model was evaluated for two coal samples through the coefficient of determination (R2), Root Means Square Error (RMSE), and Mean Average Error (MEE). The results of RSM for coal sample 1 (R2 = 0.9795, RMSE = 4.784) and coal sample 2 (R2 = 0.9758, RMSE = 4.907) showed that the model is well suited for HA extraction efficiency predictions. The derived humic acid from lignite coal was analyzed using elemental analysis, UV-Visible spectrophotometry and Fourier-transformed infrared (FTIR) spectroscopy techniques. Scanning Electron Microscopy (SEM) was applied to analyze the morphological modifications of the extracted HA after treatment with 3.5% KOH solution. For agricultural objectives, such as soil enrichment, enhancing plant growth conditions, and creating green energy solutions, this acquired HA can be made bioactive. This study not only establishes a basis for research into the optimized extraction of HA from lignite coal, but it also creates a new avenue for the efficient and clean use of lignite.

The potential of direct air capture using adsorbents in cold climates.

Global warming threatens the entire planet, and solutions such as direct air capture (DAC) can be used to meet net-zero goals and go beyond. This study investigates using DAC in a 5-step temperature vacuum swing adsorption (TVSA) cycle with adsorbents' Li-X and Na-X, readily available industrial zeolites, to capture and concentrate CO2 from air in cold climates. From this study, we report that Na-X in cold conditions has the highest known CO2 adsorption capacity in air of 2.54 mmol/g. This combined with Na-X's low CO2 heat of adsorption, and fast uptake-rate in comparison to other benchmark materials, allowed for Na-X operating in cold conditions to have the lowest reported DAC operating energy of 1.1 MWh/tonCO2. These findings from this study show the promise of this process in cold climates of Canada, Alaska, Greenland, and Antarctica to be part of the solution to global warming.

Electricity systems in the limit of free solar photovoltaics and continent-scale transmission.

Solar photovoltaics, with sufficient power generation potential, low-carbon footprint, and rapidly declining costs, could supplant fossil fuels and help produce lower-cost net-zero emissions energy systems. Here we used an idealized linear optimization model, including free lossless transmission, to study the response of electricity systems to increasing prescribed amounts of solar power. Our results show that there are initially great benefits when providing solar power to the system, especially under deep decarbonization scenarios. The marginal value of additional solar power decreases substantially with increasing cumulative solar capacities. At costs near today's levels, the modeled zero-emission electricity system with free solar generation equaling twice the annual mean demand is more costly than a carbon-emitting natural-gas-based system supplying the same electricity demand with no solar. Taking full advantage of low-cost solar will depend on developing and deploying low-cost approaches to temporally shift either energy supply (e.g., storage) or electricity loads (e.g., load-shifting).