This study explores microgrids as small, independent electrical systems that reduce emissions, lower costs, and improve the efficiency and reliability of renewable energy sources (RES). It focuses on solving the optimal power flow (OPF) problem in isolated microgrids to minimize production costs, integrating variable RES like solar photovoltaic systems and wind turbines with a stable small hydropower plant. A novel physics-based Flow Direction Algorithm (FDA), inspired by the D8 hydrological model, is introduced, offering superior precision compared to bio-inspired metaheuristics. The FDA balances global and local search for accurate solutions. Monte Carlo simulation models uncertainties in wind speeds and solar irradiance. Validated on IEEE 33-bus, 69-bus, and 15-bus systems, the FDA outperforms algorithms like Whale Optimization, Ant Lion, Dung Beetle, and Walrus Optimization in efficiency and reliability, advancing microgrid performance.

Abstract - In this paper presenting power enhancement of grid-connected solar photovoltaic and wind energy (PV-WE) system integrated with an energy storage system (ESS) and electric vehicles (EVs). The research works available in the literature emphasize only on PV, PV-ESS, WE, and WE-ESS. The enhancement techniques such as Unified Power Flow Controller (UPFC), Generalized UPFC (GUPFC), and Static Var Compensator (SVC) and Artificial Intelligence (AI)- based techniques including Fuzzy Logic Controller (FLC)- UPFC, and Unified Power Quality Conditioner (UPQC)-FLC have been perceived in the existing literature for power enhancement. Further, the EVs are emerging as an integral domain of the power grid but because of the uncertainties and limitations involved in renewable energy sources (RESs) and ESS, the EVs preference towards the RES is shifted away. The EVA designed is proposed for the PV-WE-ESS-EV system to obtain the benefits such as uninterruptible power supply, effective the load demand satisfaction, and efficient utilization of the electrical power. The reduced power quality at the load side is observed as a result of varying loads in the random fashion and this issue is sorted out by using UPQC in this proposed study. From the results, it can be observed that the maximum power is achieved in the case of PV and WE systems with the help of the FLC-based maximum power point tracking (MPPT) technique. Furthermore, the artificial neural network (ANN)-based technique is utilized for the development of the MPPT algorithm which in turn is employed for the validation of the proposed technique. The outputs of both the techniques are compared to selecting the best-performing technique. A key observation from the results and analysis indicates that the power output from FLC-based MPPT is better than that of ANN-based MPPT. Key Words: Renewable Energy Sources, Energy storage systems, Unified Power Quality Conditioner, Fuzzy Logic Controller, Artificial Neural Network and Maximum Power Point Tracking.

Abstract Interstellar neutral (ISN) atoms enable studies of the physical conditions in the local interstellar medium surrounding the heliosphere. ISN helium, which is the most abundant species at 1 au, is directly observed by space missions, such as Interstellar Boundary Explorer (IBEX). However, some of these atoms are ionized by solar UV radiation before reaching 1 au, producing pickup ions (PUIs). A recent analysis of IBEX data suggests that the helium photoionization rates predicted by models are underestimated by up to 40%. The Solar Wind Around Pluto (SWAP) instrument on board New Horizons enables the study of PUIs, giving complementary insight into the other side of the ionization process. Our goal is to verify this increased helium ionization by determining the ionization rate of ISN helium in the heliosphere based on SWAP observations of helium PUIs. For this purpose, we analyze SWAP data collected between 2012 and 2022, at distances 22–54 au from the Sun. We develop a new method for fitting model distribution functions to the observational data using the maximum likelihood method. Our approach accounts for the spacecraft’s rotation and the SWAP response function, which depends on both energy and inflow direction. We estimate SWAP’s efficiency for helium relative to that for hydrogen, and determine the ISN helium ionization rate. We find that the photoionization rate obtained from the SWAP observations is ∼43% larger than the rates predicted by models, confirming the IBEX results.

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO2 emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions.

Abstract In adiabatic processes, such as in many space plasmas, particles flow with no heat entering or exiting their system, while, more generally, the difference between polytropic and adiabatic indices provides a measure of any heating. In this paper, we investigate the thermodynamics of polytropic processes and the relationship between heating and the difference between polytropic and adiabatic indices, extended to account for variable kinetic degrees of freedom. Then, we examine the stability of fluctuating adiabatic process. We show that fluctuations of the adiabatic index cannot be effectively damped to restore equality between polytropic and adiabatic indices, unless the variability of degrees of freedom introduces another source of entropy and heating. We find that the convergence of the stability of the pickup ion (PUI) adiabatic index is very slow, and thus PUIs are rarely adiabatic; in contrast, solar wind protons at ∼1 au are nearly adiabatic. Moreover, we develop the relationship between heat transfer and variable dimensionality. We use this to characterize the evolution of the PUI distribution in the heliosphere. While the initial pickup and final thermalization phases are instantaneous and asymptotically reached, we characterize their duration by the time needed for the dimensionality to change by ∼0.1, and derive the time scales of the phases of PUI evolution: (i) pickup phase is the shorter, ∼1 hr; (ii) isotropization phase takes ∼1 day, (iii) cooling phase is much longer, ∼10 days; and (iv) thermalization phase, ∼1.5 yr. Finally, using observations from Voyager, we verify this model of the evolution of the PUI dimensionality.

Abstract In this work, we propose the use of the Most Extreme Space Weather Events (MESWE) dataset, which combines 12 solar corona and solar wind parameters to predict the disturbance storm time (Dst) index 4 or 8 hr ahead by deep learning models. The dataset focuses on periods of extreme solar activity in the past 30 yr that triggered great and severe geomagnetic storms, as measured by geomagnetic indices. Using the MESWE dataset and leveraging the gated recurrent units type of recurrent neural network, we obtain a significantly reduced persistence effect compared to models trained with conventional methodologies in the Dst index forecasting community. The performance of the models is compared quantitatively by using the dynamic time warping method. In addition, we introduce a methodology to reduce the risk of the split of the validation test set that can lead to misleading performance assessments. The methodology of combining imagery and in situ data and the results presented in this work are directly aligned with the objectives of the European Space Agency Vigil mission.

ABSTRACT The research considers an hourly residential load demand with a daily average of 988 kWh/day and investigates possible standalone systems, including solar panels (photovoltaic [PV]), wind turbines (WTs), diesel generator (DG), biogenerator (BG), and battery bank (Bat), to provide the load demand, for a case study located in Tabuk, Saudi Arabia, where the monthly solar radiation and wind speed are 5.74 kWh/m 2 /day and 5.33 m/s, respectively. In this study, enviroeconomic factors, including inflation and discount rates, capacity shortage and load demand, CO 2 and SO 2 penalties, diesel and biomass prices are considered, while they were not considered in the previous studies in Saudi Arabia. The results show that the net present cost and cost of energy of the optimized system are $1.03 M and 0.178 $/kWh, respectively. Additionally, the prices of diesel fuel and biomass have a significant impact on the CO 2 emissions of the system, even with a 10% increase in the renewable fraction. The results of sensitivity analyses show that increasing the CO 2 emission penalty from 20 to 80 $/ton leads to a decrease in CO 2 emissions by 50%. The effect of the initial cost of WT on the configuration of the optimal system is higher than that of PV, and increasing both prices significantly leads to an increase in CO 2 emissions.

Abstract While Jupiter’s magnetosphere is primarily governed by internal processes, variations in solar wind conditions can significantly modulate magnetospheric energy. Assessing solar wind conditions at Jupiter has been a long-standing challenge in the community. In this study, we investigate multiple datasets, including auroral images captured by the Hubble Space Telescope and in situ measurements obtained by the Juno spacecraft, to characterize Jupiter’s magnetosphere during solar wind compression and quiet conditions. The distinct responses demonstrate that auroral morphologies, lobe magnetic fields, ultra-low-frequency waves, and broadband kilometric radio emissions could serve as effective indicators for magnetospheric states. In particular, we statistically analyze the distribution of lobe magnetic fields, which represent the intensities of the cross-field electrical currents in the magnetodisc, and propose the applicability of the lobe magnetic field as a proxy for solar wind conditions. The results from the four individual methods of determining magnetospheric states are generally consistent, providing further evidence of their robustness. These observational proxies for solar wind conditions could therefore be widely applied in both existing spacecraft datasets and those from future missions to Jupiter.

The rapid transition toward cleaner energy requires microgrid models that are not only technically feasible but also economically and environmentally compelling for local contexts. This work develops and evaluates a hybrid renewable microgrid tailored for a residential building in Nazipur, Patnitala Thana, Naogaon District, Bangladesh. Using HOMER Pro (v3.14.2), the system integrates solar photovoltaic (PV), wind turbines (WT), an electrolyzer-hydrogen tank-fuel cell chain ("power-to-gas-to-power"), and a grid connection. The optimized design achieves a remarkably low cost of energy (COE) $0.0396/kWh and a net present cost (NPC) of $145,664 with minimal annual operating expenses ($1,100). The total carbon footprint is limited to 11,158kg/yr, reflecting a 95.8% reduction compared with conventional supply, while hydrogen is generated at $3.32/kg, reinforcing its role as a viable long-term storage medium. Beyond techno-economics, the study examines system stability through dynamic voltage and frequency response modelling in MATLAB, and explores resilience under uncertainty via sensitivity analysis of solar radiation, wind speed, hub height, temperature, and financial variables. The findings highlight that integrating hydrogen into renewable-based microgrids offers a scalable pathway for decarbonizing residential sectors in Bangladesh and similar developing regions. This research thus advances the discourse on hydrogen-augmented microgrids, underscoring their potential to bridge the gap between sustainability targets and local energy security. This study uniquely integrates a correlation-based sensitivity analysis with MATLAB dynamic validation to establish the reliability and feasibility of a hydrogen-augmented hybrid microgrid for residential applications.

Abstract We analyze the solar wind (SW) properties associated with relativistic electron precipitation (REP) observed at low‐Earth orbit. A statistical analysis is performed on the SW associated with ∼7,000 REP events likely driven by wave‐particle interactions. We analyze OMNI data to quantify the SW properties prior to REP and reveal temporal patterns that are favorable for electron precipitation. Compared to typical values, SW associated with REP typically exhibits a stronger North‐South interplanetary magnetic field B z component and higher plasma density, indicating dayside reconnection and compression. REP events observed at low L shell ( L ≲ 4), particularly from noon to post‐midnight, are triggered by enhanced B z and density. Dayside REP is associated with slightly faster SW, whereas dawnside REP coincides with high plasma density without strong B z . A typical SW trend leading to REP exhibits enhanced density, magnetic field increase, and a B z minimum before the REP observation. Through k ‐means clustering, we further identify three distinct SW temporal profiles. Dayside REP at high L shells is associated with ∼500 km/s speeds, duskside REP is associated with enhanced SW density prior to REP, and REP over noon‐to‐dusk at low L shells is triggered by strong dayside reconnection. Furthermore, we found that REP events are more frequent during the declining phase of the solar cycle, with a 6‐month periodicity in occurrence rate. The findings in this study are critical to establishing a relationship between SW and REP, enabling future modeling efforts to predict REP from SW observations.

This paper proposes a high-efficiency maximum power point tracking (MPPT) algorithm based on a variable step size control technique for standalone hybrid solar-wind energy systems. Unlike conventional approaches that utilize separate MPPT controllers for photovoltaic (PV) and wind systems, the proposed method integrates a single adaptive control strategy that simultaneously optimizes power extraction from both renewable sources. The algorithm dynamically adjusts the step size according to environmental variations, improving convergence speed and tracking accuracy. The system is modeled in MATLAB/Simulink, incorporating a 500 W solar PV system and a 560 W wind turbine, both interfaced through traditional boost converters. To validate the performance, simulations are conducted under varying solar irradiance levels (600 W/m², 800 W/m², and 1000 W/m²) and wind speeds (8 m/s, 10 m/s, and 12 m/s). Results indicate that the PV output power increases from 288.8 W to 513 W with rising irradiance, while the wind output improves from 301.4 W to 439.3 W with increasing wind speed. The combined hybrid system achieves total output powers of 557.35 W, 691.74 W, and 807.12 W across three operating intervals. These findings confirm that the proposed variable step size MPPT algorithm significantly enhances energy harvesting efficiency and system performance under dynamic environmental conditions.

Abstract In recent years, we have learned that foreshock transients play an influential role in solar wind coupling with Earth's magnetosphere. These transients include spontaneous hot flow anomalies (SHFAs), which are characterized by dips in magnetic field magnitude and ion density, enhanced temperature, decelerated and deflected solar wind flow, and are surrounded by ultra low frequency (ULF) waves. SHFAs share some characteristics with hot flow anomalies, but their formation mechanism does not require a discontinuity in the solar wind. SHFAs evolve close to the shock from caviton interaction with backstreaming ions. We use MMS magnetic field and plasma data to study SHFAs' internal structure and their evolution. In addition we analyze the properties of velocity distribution functions inside the events and in their surroundings. Our findings show that the morphology of the SHFAs is diverse depending on where they are located. They can appear deep in the foreshock or close to the ULF wave foreshock boundary, where they separate regions with different types of waves. Those SHFAs observed closer to the bow shock can experience more compression at their rear edge. Overall, our results show that the internal structure of SHFAs is more complex and diverse than previously thought.

This study examines the analytical investigation of nonlinear wave structures governed by a Schamel-type Korteweg-de Vries (S-KdV) equation, essential in plasma physics for modelling ion-acoustic waves. This work is motivated by the necessity to enhance understanding of the impact of electron trapping effects on solitary wave dynamics. We employ the Extended Direct Algebraic (EDA) method to derive precise wave solutions. The applied method offers a systematic approach to derive various soliton structures and improves our comprehension of their physical properties. In plasma physics, the S-KdV equation is utilised to examine dust ion acoustic waves. Furthermore, it is employed to examine shallow water waves distinguished by steepening and breaking. It is applied in studying the Earth's magnetosphere, the solar wind, the nonlinear plasma turbulence, and the dusty space plasma. Graphical and comparative analyses are presented to validate the results and demonstrate the robustness of the method. The obtained solutions for the S-KdV equation have Kink type, anti-kink type, and multisoliton and solitary wave structures. The properties of the wave structures are demonstrated through the two-dimensional, three-dimensional, and contour plots. Additionally, the impact of the nonlinear term as well as the dispersion term on some of the obtained solutions are discussed through the two dimensional plots.

Spatial optimization of solar PV and wind power capacity in Finland and correlation analysis

<p><span lang="EN-US">This paper presents a hybrid one-step voltage-adjustable transformerless inverter designed to efficiently integrate both solar photovoltaic (PV) and wind energy sources into a single-phase grid. The primary objective is to enhance power conversion efficiency while minimizing system complexity and cost. The proposed architecture combines a buck-boost DC-DC converter with a full-bridge inverter in a compact and modular design, enabling voltage regulation across a wide input range typical of hybrid renewable systems. By grounding the PV negative terminal, the system effectively eliminates leakage currents and ensures compliance with IEEE harmonic standards. The inverter operates with reduced switching losses and supports multiple operational modes tailored for variable solar and wind conditions. Simulation of a 300 W prototype demonstrates reliable performance, achieving a total harmonic distortion (THD) below 1%, validating its compatibility with grid requirements. Key contributions include the development of a unified topology for hybrid energy sources, in-depth analysis of energy storage components, and implementation of efficient modulation strategies. This work addresses significant challenges in renewable energy integration and provides a scalable solution for next-generation grid-connected hybrid power systems</span><span lang="EN-US">.</span></p>

Abstract Standard visualizations of Extreme Ultraviolet (EUV) solar imagery often fail to convey the full complexity of the Sun’s corona, especially in faint off-limb regions. This can leave the misleading impression of the Sun as a bright ball in a dark void, rather than revealing it as the dynamic, structured source of the solar wind and space weather. A variety of enhancement algorithms have been developed to address this challenge, each with its own strengths and tradeoffs. We introduce the Radial Histogram Equalizing Filter (RHEF), a novel hybrid technique that optimizes contrast in high dynamic range solar images. By combining the spatial awareness of radial graded filters with the perceptual benefits of histogram equalization, RHEF reveals faint coronal structures and works out of the box—without requiring careful parameter tuning or prior dataset characterization. RHEF operates independently on each frame, and it enhances on-disk and off-limb features uniformly across the field of view. For additional control, we also present the Upsilon redistribution function—a symmetrized cousin of gamma correction—as an optional post-processing step that provides intuitive programmatic tonal compression. We benchmark RHEF against established methods and offer guidance on filter selection across various applications, with examples from multiple solar instruments provided in an appendix. Implemented and available in both Python and IDL, RHEF enables immediate improvements in solar coronal visualization.

Global complexity signatures of solar cycles: A unified Entropy-Fractal survey of OMNI solar wind data (1964–2025)

Simulations of Structured Upflows from Plumes and Their Connection to the Solar Wind

The mechanical characteristics of polyimide films of the Kapton H type with different thicknesses, both in the initial state and after irradiation separately with corpuscular radiation fluxes from the Earth’s radiation belts (protons or electrons) and electromagnetic radiation from the transatmospheric Sun in the range of vacuum ultraviolet and ultrasoft X-rays, simulated in the laboratory, were investigated. The preservation of the forced elastic state in the irradiated films was revealed. The influence of corpuscular and electromagnetic irradiation on several characteristics (forced elasticity limit, rupture stress, deformation to rupture) is different, and these parameters vary in opposite ways with thickness. After exposure to all types of irradiation, a redistribution of the contributions (reversible deformation upon load removal and delayed forced elastic deformation, as well as irreversible deformation) to the total deformation before rupture was observed. The nature and magnitude of this redistribution also depend on the type of irradiation and the film thickness. The elastic moduli, including Young’s modulus and the modulus at the final stage of forced elasticity, with the exception of the modulus of proton-irradiated films, increase after all types of irradiation.

Abstract We explore the capabilities of time-dependent (TD) magnetohydrodynamic (MHD) solar wind simulations with the coupled Wang–Sheeley–Arge (WSA) model of the solar corona and the Grid Agnostic MHD for Extended Research Applications model of the inner heliosphere. We compare TD with steady-state (SS) simulations and in situ observations from multiple spacecraft (Earth, STEREO-A, Parker Solar Probe). We show that TD predictions, although better than SS predictions, substantially mispredict the solar wind at different heliospheric locations. We identified three reasons for that: (1) the uncalibrated WSA velocity formula used to generate solar wind velocities at the inner boundary of a heliospheric domain, (2) the extraction of the WSA boundary conditions for input into MHD models very high in the corona, and (3) the abrupt and partial emergence of active regions from the solar east limb. Evaluation of 1 year of TD predictions at the Earth and STEREO-A locations shows that tuning accordingly the WSA relationship when used with MHD models and extracting the WSA boundary conditions lower in the corona (at 5 R s instead of 21.5 R s ) can lead to improved predictions. However, the abrupt emergence of active regions from the east limb of the Sun, which can highly disrupt the magnetic field topology in the corona, is a difficult task to deal with since complete knowledge of the conditions on the solar far side is not currently available. Solar Orbiter Polarimetric and Helioseismic Imager data can help mitigate this effect; however, unless we get a 4 π view of the Sun, we will be unable to completely address it.