The rapid expansion of renewable energy, particularly wind and solar power, has made reliable forecasting critical for power system operations. While recent deep learning models have achieved strong average accuracy, the increasing frequency and intensity of climate-driven extreme weather events pose severe threats to grid stability and operational security. Consequently, developing robust forecasting models that can withstand volatile conditions has become a paramount challenge. In this paper, we present R$^2$Energy, a large-scale benchmark for NWP-assisted renewable energy forecasting. It comprises over 10.7 million high-fidelity hourly records from 902 wind and solar stations across four provinces in China, providing the diverse meteorological conditions necessary to capture the wide-ranging variability of renewable generation. We further establish a standardized, leakage-free forecasting paradigm that grants all models identical access to future Numerical Weather Prediction (NWP) signals, enabling fair and reproducible comparison across state-of-the-art representative forecasting architectures. Beyond aggregate accuracy, we incorporate regime-wise evaluation with expert-aligned extreme weather annotations, uncovering a critical ``robustness gap'' typically obscured by average metrics. This gap reveals a stark robustness-complexity trade-off: under extreme conditions, a model's reliability is driven by its meteorological integration strategy rather than its architectural complexity. R$^2$Energy provides a principled foundation for evaluating and developing forecasting models for safety-critical power system applications.
A population of Galactic gamma-ray binaries is currently emerging due to ever increasing sensitivity of gamma-ray observatories. The detection of very high energy (VHE) photons with energies well above 10 TeV from a dozen of sources and the estimated power of those sources make them potentially interesting cosmic ray accelerators. Multi-wavelength observations of gamma-ray binaries revealed that most of them include a young massive star in pair with a relativistic companion, either a black hole or energetic pulsar. Fast stellar winds interacting with powerful relativistic outflows from pulsars or the black hole jets in microquasars are favorable sites for VHE particle acceleration. To estimate the expected number of gamma-ray binaries, we present results of population synthesis calculations of Galactic binaries in which a young massive OB- or Be-star is accompanied by a pulsar capable of producing a powerful relativistic outflow. The distributions over the binary eccentricities, orbital periods, Be-disk inclinations, and the pulsar braking energy losses are taken into account. Conditions for a binary to accelerate VHE particles, radiate and absorb the non-thermal photons that may reach the observer are discussed. We model the anisotropic structure of the zone of interaction of the relativistic pulsar wind with the strongly magnetized massive star's wind. The stellar winds with strong ($\sim$ Gauss) magnetic fields at $\sim$ AU distances colliding with powerful pulsar outflows are capable of accelerating particles up to PeV energies at some orbital configurations and phases. The strong magnetic field in the interaction region produces a highly anisotropic structure of the particle accelerator and emitter in the pulsar outflow. The anisotropic radiation pattern may affect the gamma-ray photon absorption and the number of the observed gamma-ray loud systems.
Climate change poses significant threats to oceans, leading to ocean acidification, sea level rise, and sea ice loss and so on. At the same time, oceans play a crucial role in climate change mitigation and adaptation, offering solutions such as renewable energy and carbon sequestration. Moreover, the availability of diverse ocean data sources, both remote sensing observations and in situ measurements, provides unprecedented opportunities to monitor these processes. Remote sensing data, with its extensive spatial coverage and accessibility, forms the foundation for accurately capturing changes in ocean conditions and developing data-driven solutions. This review explores the dual relationship between climate change and oceans, focusing on the impacts of climate change on oceans and ocean-based strategies to combat these challenges. From the artificial intelligence perspective, this study systematically analyzes recent advances in applying deep learning techniques to understand changes in ocean physical properties and marine ecosystems, as well as to optimize ocean-based climate solutions. By evaluating existing methodologies and identifying knowledge gaps, this review highlights the pivotal role of deep learning in advancing ocean-related climate research, outlines existing current challenges, and provides insights into potential future directions.
Hanuman Sahay Jat, Kailash Prajapat, Shivani Khokhar
et al.
Intensive rice-based systems in the Indo-Gangetic Plain of India face critical sustainability challenges, including high energy use, excessive greenhouse gas (GHG) emissions, and unsustainable groundwater exploitation. This study evaluates productivity and environmental footprints (energy, water, and carbon) to foster environmental stewardship through conservation agriculture-based climate-smart agriculture practices (CSAPs). Six scenarios (Sc) were analyzed: conventional till (CT) rice-wheat (CT-RW, Sc 1); CT rice-zero till (ZT) wheat-ZT mungbean (CTR-ZTWM, Sc 2); ZT direct-seeded rice-ZTWM (ZTRWM, Sc 3); ZT maize-ZTWM (ZTMWM, Sc 4); Sc 3 with subsurface drip (SSD) irrigation (ZTRWM-SSD, Sc 5); and Sc 4 with SSD (ZTMWM-SSD, Sc 6). The CSAPs (Sc 3-Sc 6) outperformed Sc 1 with respect to key performance parameters. Sc 6 (ZTMWM-SSD) achieved the maximum rice equivalent yield (8.25 t ha⁻¹), a 22.2 % increase over Sc 1. Wheat yield in Sc 6 reached to 6.34 t ha⁻¹, corresponding to a 22.1 % enhancement compared to Sc 1, resulting in a total system yield of 16.73 t ha⁻¹, representing a 35.6 % increase over Sc 1. For system-wide partial factor productivity of N, Sc 5 showed 51.4 % improvement, while Sc 6 achieved the highest increase of 69.7 %, reflecting significant gains in nitrogen use efficiency. The CSAPs scenarios markedly improved system water productivity, resulting in a decreased water footprint, which was lowest in Sc 6 (189 L kg⁻¹) compared to Sc 1 (1642 L kg⁻¹). Energy dynamics revealed that Sc 6 was the most efficient among all the scenarios. With an energy input of 30,360 MJ ha⁻¹, it produced energy output of 471,633 MJ ha⁻¹, and recorded the highest energy use efficiency (15.69). In terms of environmental sustainability, CSAPs (Sc 3, Sc 4, Sc 5 and Sc 6) exhibited lower system net global warming potential (GWPn), compared to CT-based scenarios (Sc 1 and Sc 2), reflecting a significantly reduced carbon footprint. These results highlight the potential of CSAPs to enhance productivity and profitability while minimizing environmental impacts, making CSAPs critical to the future of sustainable agriculture in North-western India.
The integration of renewable energy sources into community microgrids is increasingly critical for reducing reliance on utility grids and promoting local energy consumption. In this paper, an improved rule-based energy management system (EMS) for the community microgrid is proposed, which optimizes energy scheduling to enable the system to achieve real-time supply-demand balance in both the grid-connected mode and the off-grid mode. To realize the proposed EMS function, an improved peer-to-peer fast settlement transaction mode is proposed, which is suitable for both the grid-connected mode and the off-grid mode, and fuzzy control is employed to manage complex real-time control problems. Additionally, a non-communication internal price transmission mechanism is proposed to ensure the EMS operates effectively even under harsh communication conditions. Simulation results show that the proposed EMS can rapidly respond to supply-demand variations, maintaining near self-sufficiency in both the grid-connected and the off-grid mode. Specifically, the EMS achieves at least a 55% reduction in the net exchange power deviation from zero and the supply-demand ratio (SDR) deviation from the target level of 1 compared to the rule-based EMS used for comparison. Overall, the proposed EMS demonstrates excellent real-time performance, robustness, and immunity to communication issues.
Jayachandra Malavatu, Peter Kepplinger, Bernhard Faessler
The objective of this study is to address the power imbalance between supply and demand caused by the adoption of electric vehicles and renewable energy sources. Due to power imbalance at the point of common coupling, additional peaks and valleys will be created. The novelty of this work lies in proposing a hybrid energy storage system that combines power-dense and energy-dense batteries, optimized using a Norm-2 approach, to mitigate these imbalances effectively. The methodology involves a simulation study using realistic distribution grid load curves, focusing on two case studies. The results of this study reveal that, with an optimally sized energy storage system, power-dense batteries reduce the peak power demand by 15 % and valley filling by 9.8 %, while energy-dense batteries fill the valleys by 15 % and improve the peak power demand by 9.3 %. Furthermore, a hybrid energy storage system outperforms and is useful for multiple grid applications when compared with a single type of energy storage system. The study identifies an optimal capacity share of 40 % power-dense and 60 % energy-dense batteries as providing an effective balance between power and energy requirements. The findings highlight the proposed system successfully manages not only the highest peaks and valleys, but also intermediate fluctuations caused by renewable energy and electric vehicle integration. During a one-year simulation using a hybrid energy storage system, peak power demand decreased by 11 %, peak-to-average ratio improved by 12 %, and power variance was reduced by 29 %, indicating more stable and efficient grid performance compared to without any storage system.
Farzin Hosseinifard, Milad Hosseinpour, Mohsen Salimi
et al.
Carbon Capture Utilization and Storage (CCUS) has become a cornerstone in reducing industrial emissions, mainly through Enhanced Oil Recovery (EOR) in underground reservoirs. Conventional post-combustion carbon capture (PCC) systems, however, face significant energy penalty challenges. This study introduces an innovative solar-assisted approach to optimize the EOR factor while reducing the energy penalty. The proposed system uniquely integrates solar tower heliostats and photovoltaic (PV) systems with up to 7 h of energy storage, marking a dual solar energy integration as the core innovation. This hybrid configuration reduces the energy penalty factor from 21.2 % to 7.4 %. To further enhance operational efficiency, the study incorporates a novel compression stream configuration with process integration into the PCC system. Machine learning models, including linear regression, random forest, decision tree, and XGBoost, were employed to model and predict EOR factors using CO2 streams from a large-scale carbon capture unit at the Abadan power plant in Iran. The decision tree model achieved superior performance with an R² of 0.98 and accurately forecasted an increase in EOR factor from 19 % to 43.16 %. By combining solar-driven energy systems with advanced CO2 capture and predictive modeling, this study establishes a sustainable and energy-efficient framework for EOR enhancement. The dual integration of solar towers and PV systems represents a significant leap in reducing fossil fuel dependence and carbon emissions while demonstrating practical applicability in high-emission regions like Abadan.
Microorganisms are one of the main driving forces of the cycle of carbon and other life elements in the underground environment. The natural environment is the comprehensive result of these microorganisms. In contrast, the study of coal reservoir microorganisms is mostly under laboratory conditions, which limits people's understanding of the symbiotic relationship between microorganisms, the interaction between microorganisms and the environment, and the distribution differences of microbial communities in the region. Similarly, the carbon cycle of the underground environment driven by essential microorganisms in coal reservoirs cannot be further studied. The geochemical process of underground methane generation and oxidation is critical in discussing the production and consumption of biomethane in the underground environment and the metabolic behavior of microorganisms. For this reason, we conducted biogeochemical tests and microbial sequencing on the water produced by coalbed methane wells in the south of the Qinshui Basin to analyze and improve the understanding of the distribution difference and metabolic behavior of microbial communities in coal reservoirs. The concentration of Cl − and HCO 3 − in the detention environment in the study area increases, while the concentration of SO 4 2− , NO 3 − , NO 2 − , and Fe 3+ decreases with the increase of coal seam depth, reflecting the distribution difference of hydrochemical environment and redox conditions of the underground reservoir in the study area. The results of microbial sequencing showed microbial methanogenesis in the study area, but it could also be consumed by microbial oxidation simultaneously. The microbial communities related to methane production and consumption had diversity distributions similar to geochemical parameters and geographical patterns. Methanogens and dissolved inorganic carbon isotopes confirmed the potential of in situ methane generation. Still, biomethane's enrichment and accumulation conditions and the impact of aerobic/anaerobic oxidation of methane need further study.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Fuel cells are one of the most promising renewable energy sources, offering advantages like reliability, eco-friendliness, and low pollutant emissions, which have spurred rapid advancements in power generation technologies. However, fuel cells face significant challenges, including high initial costs, limited fuel availability, and the difficulty of maintaining operation at the maximum power point, which hinders their use in stand-alone applications. In this paper, a Modified Particle Swarm Optimization (MPSO) method is proposed for maximum power point tracking (MPPT) to optimize the power output of Proton Exchange Membrane Fuel Cells (PEMFCs). The proposed method dynamically adjusts to key operational parameters such as cell temperature, hydrogen partial pressure, and membrane water content, areas that have not been comprehensively addressed in previous research. In this paper, an MPSO algorithm-based MPPT tracking approach without a PID controller is proposed to achieve the maximum power point (MPP) of a PEMFC. Under rapid temperature fluctuations in the fuel cell, the proposed MPSO MPPT method achieved a maximum power of 1223.5 W with an average of 5.66 iterations. In comparison, the meta-heuristic particle swarm optimization (PSO) method and the conventional perturb and observe (P&O) method achieved maximum power outputs of 1218.5 W and 1213.65 W, respectively, with PSO requiring 12.33 iterations. Additionally, the proposed approach showed improvements in power efficiency by 2.47 %, 2.87 %, and 13.58 % for the Jaya algorithm. demonstrating effective MPPT tracking under different operating conditions and perturbations. The MPSO method is implemented in the Simulink/MATLAB environment and is compared with the Perturb & Observe (P&O) and Conventional PSO (CPSO) methods. The results demonstrate that the proposed MPSO approach outperforms these traditional techniques in terms of tracking speed, efficiency, and stability under varying conditions. This successful implementation lays a strong foundation for future integration into real-world PEMFC systems.
Corporate renewable power purchase agreements (PPAs) are long-term contracts that enable companies to source renewable energy without having to develop and operate their own capacities. Typically, producers and consumers agree on a fixed per-unit price at which power is purchased. The value of the PPA to the buyer depends on the so called capture price defined as the difference between this fixed price and the market value of the produced volume during the duration of the contract. To model the capture price, practitioners often use either fundamental or statistical approaches to model future market prices, which both have their inherent limitations. We propose a new approach that blends the logic of fundamental electricity market models with statistical learning techniques. In particular, we use regularized inverse optimization in a quadratic fundamental bottom-up model of the power market to estimate the marginal costs of different technologies as a parametric function of exogenous factors. We compare the out-of-sample performance in forecasting the capture price using market data from three European countries and demonstrate that our approach outperforms established statistical learning benchmarks. We then discuss the case of a photovoltaic plant in Spain to illustrate how to use the model to value a PPA from the buyer's perspective.
Khaled Osmani, Ahmad Haddad, Thierry Lemenand
et al.
PhotoVoltaic (PV) systems are often subjected to operational faults which negatively affect their performance. Corresponding to different types and natures, such faults prevent the PV systems from achieving their nominal power output and attaining the required level of energy production. Regarding the operational optimization of PV systems, this paper aims primarily at surveying and categorizing different types of PV faults, classified as electrical, internal, and external, where each is thoroughly investigated: internal faults occur at the PV cellular level, and can either be short circuit, open circuit, bridging, or bypass diode faults. External faults on the other side are mainly classified as temporary (i.e., clouds shading, snowstorms, etc.) or permanent (e.g., glass breakage, frame defects, etc.) mismatch faults. Lastly, electrical faults involve common circuitry problems, such as short circuits (e.g., line to ground, line to line, etc.), power processing units’ faults (e.g., inverter faults), and arc faults. As for the detection methods, six major fault detection methods are investigated for the AC side of the PV system with twenty-nine total AC based fault detection methods. On the other hand, eleven major fault detection methods are surveyed for the DC side of PV systems with seventy-three total DC based fault detection methods. The investigated methods are critically analyzed, and compared relevantly to each other, within the mutual sub-sets. The resulting tabulated comparative data assessments for PV faults (i.e., cause-effect relationships, impact on the PV system performance), as well as for faults detection methods (i.e., priority for application, etc.) compose a rich background for related PV systems’ performance security fields, where a nexus future work is also suggested.
Abstract In‐based halides always present low photoluminescence quantum yield (PLQY) because of poor absorption, limiting their potential applications in luminescence‐related fields. In this work, zero‐dimensional MA4InCl7 [MA+: CH3NH3+] halides with different Sb3+ doping level are prepared through solvent evaporating method. The Sb3+‐doped MA4InCl7 shows a broadband yellow emission with full width at half‐maximum of 180 nm and a high PLQY of 84%. Such broadband emission originates from the self‐trapped excitons demonstrated by experimental results and theoretical calculations. Additionally, the Sb3+‐doped MA4InCl7 is further employed to fabricate white‐light‐emitting diodes, which possesses high color rendering index of 91 and excellent operating stability up to 400 h. Moreover, flexible Sb3+‐doped MA4InCl7 films are also prepared as x‐ray scintillators, exhibiting low detection limit of 63.3 nGyair/s and high spatial resolution of 10.0 lp/mm. Thus, this work provides guidance to design perovskite‐based devices with bright luminescence and x‐ray detection with excellent flexibility.
Severe deformation of surrounding rock and excess-gas are the main problems faced in mining of high-gas coal seam. This paper analyzes the deformation characteristics and mechanical model of surrounding rock in high-gas coal seam, and proposes the control technology of surrounding rock deformation and gas prevention and control. Based on this, the entry automatically formed by roof cutting (EAFRC) surrounding rock control technology and constant resistance large deformation anchor cable (CRLDA) support control technology in Shaqu coal mine are put forward. At the same time, the surrounding rock stress and gas migration law of the working face under traditional mining method and EAFRC mining were compared and analyzed. Through the field engineering test, the monitoring and analysis of surrounding rock deformation and gas concentration, the average surrounding rock deformation of roof cutting roadway is 310 mm, and the gas concentration of retained roadway by roof cutting is 0.31%. Through the research in this paper, the surrounding rock stability and gas control of the working face have been realized, and the non-pillar mining of EAFRC has ensured the safe mining of high gas working faces, which provides a reference for the mining of similar mines in non-pillar mining. At the same time, the technical system of EAFRC in non-pillar mining was established, which promoted the development and application of non-pillar mining.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources