Granted by the suitable characteristics of its electronic band structure, high electron mobility, and remarkable durability, P(NDI2OD-T2) stands out among conjugated polymers and is regarded as a prominent candidate for electron accepting materials in the third generation of photoelectrochemical and photovoltaic devices. However, a comprehensive understanding of exciton-polaron interactions, which provides the key to optimizing the efficiency of these devices, is lacking. In this study, we fabricated P(NDI2OD-T2) into a film working electrode of an electrochemical cell and changed its surrounding electrochemical environments by exerting different electric biases. Transient absorption (TA) spectroscopy was employed synchronously to interrogate the corresponding exciton dynamics. Careful inspection of the TA spectra of P(NDI2OD-T2) under different electrochemical conditions gives detailed insights into the interactions between excitons and polarons. Our results highlight that polarons have a significant influence on exciton behavior. Merely applying cathodic biases without efficiently doping the polymer yields excited-state dynamics similar to that in the neutral state. However, in the presence of polarons, the lifetime of excitons is significantly reduced by ∼5 times. Through analyzing the TA spectra and kinetics of P(NDI2OD-T2) at -1.0 and -1.2V (vs Ag/Ag+ reference electrode), an efficient exciton-polaron quenching effect was identified. Furthermore, we report the absorption features of the excited states of polaron, i.e., charged excitons (trions) in P(NDI2OD-T2). We propose that bipolarons and such charged excitons may have similar electronic structures as they manifest resemblance in their absorption features. Our strategy of integrating ultrafast spectroscopy and electrochemistry to investigate the exciton-polaron interactions in polymers provides an effective methodology in research fields of photoelectrochemistry and photovoltaics, more broadly applicable beyond soft materials.

• Interval-valued kernel PCA is proposed for robust PV fault detection under uncertainty. • Interval information is preserved to improve robustness to noise and nonlinearity. • Experimental validation is performed on a grid-connected PV system with real faults. • Results show reduced false alarms, missed detections, and detection delays. • The method enables reliable uncertainty-aware monitoring of PV energy systems. This paper proposes an interval-valued kernel principal component analysis framework for fault detection in photovoltaic systems operating under uncertain conditions. The proposed approach preserves the interval-valued structure of measurement data throughout the entire monitoring process, allowing an explicit representation of data variability and uncertainty while enhancing fault sensitivity. Experimental validation is carried out using real measurement data acquired from a grid-connected photovoltaic system subjected to three representative fault types: a one-phase sensor fault exhibiting ramp behavior, nonhomogeneous partial shading corresponding to an intermittent fault, and open-circuit faults in the photovoltaic array characterized by step variations. The proposed method is comparatively evaluated against conventional principal component analysis, kernel principal component analysis, and the vertex-based kernel principal component analysis extension. The results demonstrate that the interval-valued framework consistently reduces false alarm rates, improves fault detection accuracy, and ensures timely fault identification. Quantitatively, the proposed approach achieves improvements of approximately 61% for the Hotelling statistic, 80% for the squared prediction error, and 94.5% for the combined monitoring index across the aggregated loss function, confirming its robustness and effectiveness for reliable fault detection in photovoltaic systems.

Advanced LSTM-based approach for fault detection and shading pattern identification in solar photovoltaic system using the Internet of Things

The analysis of current-voltage (J-V) characteristics is essential for understanding charge transport, injection barriers, and performance metrics in organic photovoltaic (OPV) devices. However, most available approaches either require advanced programming skills or focus on a limited subset of models. This work introduces a free, cross-platform Python-based tool that integrates multiple theoretical frameworks for J-V curve analysis through a user-friendly graphical interface. The software implements modules for illuminated curves, enabling the extraction of J sc , V oc , fill factor (FF), power conversion efficiency (PCE), and resistive losses, as well as modules dedicated to dark curves, including the Mott-Gurney law, the circuital model, and the Richardson-Schottky formalism, which allow estimation of carrier mobility, effective mobility, saturation current density, and injection barrier height. Graph customization options are included to generate publication-ready figures directly within the program. The tool was validated against experimental data, providing reliable results consistent with theoretical expectations. Future releases will expand its scope by adding a multiplot functionality for the simultaneous comparison of multiple datasets and a lifetime analysis module to monitor the temporal evolution of PCE, V oc , J sc , FF, and carrier mobility. By combining rigor, accessibility, and extensibility, the proposed tool contributes to the systematic characterization and optimization of next-generation organic solar cells. Program Title: JV Analysis Hub - OPVTools CPC Library link to program files: https://doi.org/10.17632/k8h975xxp6.1 Developer’s repository link: https://github.com/kaikeMp/jv-analysis-hub Licensing provisions: MIT Programming language: Python > = 3.8 Supplementary material: The program includes graphical output and parameter extraction tools (Jsc, Voc, FF, PCE, Rs, Rsh). Nature of problem: The software aims to analyze experimental current-voltage (J-V) data from organic photovoltaic (OPV) devices. J-V curves are essential for evaluating photovoltaic parameters such as short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and power conversion efficiency (PCE). The challenge lies in accurately extracting these parameters from noisy datasets under various illumination conditions such as dark, low-light, and illuminated. Furthermore, the software must handle multiple input files and apply different theoretical models (e.g., Mott-Gurney, Richardson-Schottky) to fit the experimental data. Solution method: The program implements several theoretical models to extract key parameters from J-V curves. The models include the Mott-Gurney law for space-charge-limited current (SCLC), Richardson-Schottky for thermionic emission, and an equivalent circuit model for dark J-V curves. The software uses non-linear curve fitting techniques to optimize parameters such as series resistance (Rs), shunt resistance (Rsh), and charge-carrier mobility ( μ ). It supports batch processing for multiple datasets and generates interactive plots for visual inspection of the results. The graphical interface allows users to upload datasets, configure analysis parameters, and visualize the fitted curves with key points marked (e.g., Jsc, Voc, MPP). Additional comments including restrictions and unusual features:

Energy transitions increasingly depend on collective decision-making (CDM) by households, firms, and civic organizations that co-invest, co-operate, and co-govern local energy resources. Yet mainstream energy models still treat behavior as that of a single, perfectly rational actor, under-representing the social dynamics of CDM. This paper presents a decade-scale (2015-2025) systematic review of CDM modeling at community scale. Using a broad search string pairing synonyms of “collective decision” with “energy” across leading databases, hundreds of records were screened and 84 peer-reviewed studies retained that simulate interactions among multiple autonomous actors. A supplementary search-robustness check indicates that broader retrieval adds more game-theoretic and governance-oriented studies, but does not materially alter the main patterns. Methodologically, agent-based simulation is a major strand, alongside game-theoretic formulations, hybrid optimization-agent approaches, and an emerging line of multi-agent reinforcement learning. Most studies focus on electricity communities, especially solar PV, battery storage, and peer-to-peer trading, while heat, mobility, and multi-energy integration remain limited. Peer influence and heterogeneity are often represented, whereas governance processes and diverse actor roles are still rarely modeled. Empirical calibration and validation are improving but remain uneven, limiting policy credibility in practice. The review identifies three field-defining gaps: (i) limited behavioral realism and weak validation; (ii) thin representation of governance and institutional change; and (iii) weak multi-scale socio-technical integration across sectors and levels. In response, the Multi-Scale Integrated Collective-Energy Decision (MICED) framework is proposed as a modular way forward for linking heterogeneous actors, group-decision processes, and socio-technical system dynamics. Together, the review and framework outline an agenda for more credible, equity-aware, and policy-relevant analysis of community-scale energy transitions. • Decade-scale systematic review of energy-related collective decision-making models. • Key gaps: weak behavioral realism, governance, and multi-scale coupling. • Propose framework MICED: multi-scale, learning agents, and explicit governance.

In the last ten years, the North African Countries (NAC) have reached remarkable rates of the development of renewable energy in last ten years, with an overall growth of the EU’s renewable generation up to 40%. This can be attributed to 4.5 GW of new onshore and offshore wind, PV and solar thermal capacity that has been added to the system. If we take hydropower out of the mix, capacity from renewable sources is up by nearly 560% (with the overall total growing just 80%). This has happened, incredibly enough, at a time of seismic activity from socio-political earthquakes in the area. The purpose of this article is to provide full overview of renewable energy in NAC, assessing its resource potential, deployment, investment, and policies. By assessing key drivers, challenges, and opportunities, the article will provide strategic suggestions and policy recommendations that could help to accelerate renewable energy growth and long-term sustainability in the region. With respect to data collection, this study primarily investigates the adoption trajectory and current status of renewable energy deployment across the NAC, drawing on evidence synthesized from IRENA, the IEA, and the World Bank. In addition, the article incorporates an assessment of the region’s prospective capacity for renewable energy installations, contextualizing deployment patterns against the underlying resource potential and enabling conditions. The region, statistically, has considerable renewable energy potential, with estimates of annual generation of 147,729, 181,718, and 168,205 TWh for CSP, PV, and wind, respectively. By 2040, Algeria's solar PV could achieve a levelized cost of energy (LCOE) of only €0.019/kWh and wind energy in Morocco could come in at a range of €0.077 - €0.111/kWh. Energy efficiency measures could also save a total of 35 TWh of electricity consumption per year by 2040, amounting to an 8.6%, or 35 TWh less than the current demand. The total consumption savings would be a total of 437 TWh and savings of $39.7 billion in electricity bills. In addition, these savings will amount to emissions • Mapping the technical potential of solar, wind, green hydrogen, and storage technologies across North Africa. • Assessing investment requirements and financial feasibility for large-scale deployment. • Presenting transition scenarios from 2025–2040 based on realistic energy demand, CO 2 reduction pathways, and grid integration constraints. • Identifying key opportunities and challenges for accelerating the clean energy transition. • Providing strategic policy recommendations for regional decision-makers and international partners.

Productivity enhancement of hemispherical solar distillers using spiral tube absorber coated with Cu-NPs integrated with recycled porous filler materials and nanofluid-spiral tube collector powered by PV system

A 6.4-magnitude earthquake struck Jajarkot district in Karnali province, Nepal, on 3 November 2023, disrupting electricity supply at essential birthing centres and health posts. The loss of power disabled baby warmers and heating systems, putting mothers and babies at risk. The World Health Organization (WHO) Country Office in Nepal conducted rapid facility assessments across eight municipalities and identified three priority birthing centres with critical power deficits. In partnership with the health ministry and local authorities, WHO identified local vendors to install hybrid 5.45 kW photovoltaic systems within 17 days of contract initiation to ensure continuous, off-grid power supply. The three facilities, serving over 7500 people, are located in steep, mountainous terrain. One of the facilities had no grid connection and the two others relied on unreliable seasonal hydropower, which fails during the winter dry season. By the end of December 2023, the hybrid photovoltaic systems were operational, providing 24-hour electricity supply. Baby warmers and room heaters were in continuous use, reducing newborn hypothermia risk. Institutional deliveries increased: Pajaru Kha Health Post recorded five deliveries in two weeks following installation compared with none during the preceding winter period (October-December 2023). The installation was facilitated by rapid assessment, early engagement of pre-identified local vendors, and use of existing supply chains and technical capacity. Strong coordination among WHO, the health ministry and local governments enabled timely decision-making. Prior health cluster coordination training, clear governance mechanisms, streamlined approvals and access to emergency funding helped maintain momentum and ensure on-schedule procurement and installation.

CFD simulation of snow drifting on horizontal single-axis photovoltaic (PV) tracker array with varying tilt angles and row spacings

A novel fault detection technique for solar photovoltaic array using voltage-differential sensing approach method

Comparative simulation of gravity and battery energy storage for solar PV systems: Performance and sustainability insights

Performance analysis of solar photovoltaics assisted dryers to enhance reliability for agricultural products

Prospects for solar photovoltaics in highly renewable energy transition scenarios towards a dominant future energy source

High-Resolution Assessment of Rooftop Solar PV Potential under Extreme Solar Irradiance

Photovoltaic array reconfiguration method based on improved multi-objective black kite optimization algorithm in desert environments

A new 5D 19P digitization-fused inference to analyze the motion characteristics of output power for floating photovoltaic systems

• GIS-driven aerial remote sensing supports large-scale power distribution monitoring. • Satellite images enable detection of clandestine areas linked to energy theft. • Vegetation management can benefit from GIS-driven aerial remote sensing. • Automatic estimation of rooftop PV rated capacity to update utility database. Solutions enabled by recent technological advancements and the increased availability of free geospatial images and open-source tools have demonstrated the potential to enhance tasks in various areas; however, electric power delivery remains insufficiently explored. This paper presents a proof-of-concept study exploring novel integrations of satellite imagery and geographic information systems (GIS) data to support three key tasks of distribution utilities worldwide: identifying clandestine connections to the system (electricity theft), mapping vegetation encroachment that poses risks to the network, and detecting and estimating the installed capacity of rooftop photovoltaic systems for automatically feeding or updating the utility database. The solutions rely on open-source tools, including artificial intelligence, image processing, and color segmentation, and are validated using real data from a Brazilian utility. The results demonstrate that the integration of GIS-driven and image-based aerial remote sensing techniques offers scalable and cost-efficient alternatives to conventional inspection methods.

Impact of dual-axis elevated photovoltaic systems on bats’ activity and foraging behavior in wheat fields and hay meadows

The electrical power systems are facing rising challenges of stability and control with increasing share of intermittent renewable energy power sources. This work presents application of Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm in single unified controller for multi-objective control of DFIG-Solar PV system connected to power grid. The commonly used Proportional-Integral (PI) controllers are not suitable to address nonlinearities of single controller based hybrid DFIG and solar PV systems. At times, the latest reinforcement learning-based controllers like DDPG can be erratic and aggressive due to overestimation of the actor's control action. These aggressive actions, which cause overshoot and oscillation, can be overcome by adopting the TD3 algorithm. The TD3 algorithm provides improved learning capabilities and performance by mitigating overestimation by using dual critic networks. A single TD3-based controller is implemented to simultaneously control the Rotor Side Converter (RSC), Grid Side Converter (GSC) and solar PV system integrated at the DC link. OPAL-RT real-time hardware-in-the-loop (HIL) simulation results demonstrate that the TD3 controller achieves a 10.3% reduction in power overshoot, 8% improvement in DC link voltage regulation, 15.3% faster response time, and 16.9% faster settling time compared to conventional PI control, and also outperforms the DDPG-based controller across all metrics.

ABSTRACT This study presents an adaptive control framework for a grid-connected solar photovoltaic (PV) system with battery energy storage. An enhanced hybrid artificial neural network – incremental conductance (ANN – INC)-based maximum power point tracking (MPPT) algorithm is developed to provide fast and oscillation-free tracking under standard test conditions (STC) and varying irradiance conditions. By merging the learning capability of the ANN with the structural robustness of the INC method, the approach optimizes both convergence speed and steady-state performance. In addition, a source switching mechanism (SSM) manages power flow between the PV array, battery, and grid based on irradiance and battery state of charge (SoC), ensuring a continuous supply and efficient energy utilization. The proposed method is evaluated against various MPPT techniques. MATLAB/Simulink results demonstrate superior tracking efficiency, faster settling time, and reduced steady-state power fluctuations under steady-state and dynamic irradiance varying conditions. The system further exhibits enhanced stability and effective energy management, confirming its suitability for grid-connected PV applications.