Solar Irradiance Variations Research Articles

Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model

As the global energy demand continues to produce, photovoltaic (PV) solar energy has emerged as a key Renewable Energy Source (RES) due to its sustainability and potential to reduce dependence on fossil fuels. However, accurate forecasting of Solar Energy (SE) output remains a significant challenge due to the inherent variability and intermittency of solar irradiance (SI), which is affected by factors such as weather conditions, geographic location, and seasonal patterns. Reliable prediction models are crucial for optimizing energy management, ensuring grid stability, and minimizing operational costs. To address these challenges, this research introduces an innovative method that integrates Robust Seasonal-Trend Decomposition (RSTL) with an Adaptive Seagull Optimisation Algorithm (ASOA)-optimized Long Short-Term Memory (LSTM) neural network. Using RSTL to differentiate between time series data into development, seasonal in nature, and residual factors, this methodology addresses SI’s unpredictable nature and intermittent operation and provides the basis for accurate predictions. ASOA improves LSTM features by constantly finding and exploiting resources and adopting motivation from seagulls’ collecting and migration behaviours. Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. The study of performance measurements (RMSE, MAE, and R2) demonstrates significant improvements in the accuracy of predictions. The research results highlight significant implications regarding subsequent studies and real-world uses in SE prediction, accentuating the positive impacts of incorporating accurate data decomposition and adaptive optimized performance.

PAStar: A model for stellar surface from the Sun to active stars

Context. The characterisation of exoplanets requires a good description of the host star. Stellar activity acts as a source of noise, which can alter planet radii as derived from the transit depth or atmospheric characterisation. Aims. Here, we propose PAStar, a model to describe photospheric activity in the form of spots and faculae, which could be applied to a wide range of stellar observations, from photometric to spectroscopic time series, making it possible to correctly extract planetary and stellar properties. Methods. The adopted stellar atmosphere is a combination of three components: the quiet photosphere, spots, and faculae. The model takes into account the effects of star inclination and Doppler shifts due to stellar rotation and limb darkening, which is independent for each component. Several synthetic products have been presented to show the capabilities of the model. Results. The model is able to retrieve the input surface-inhomogeneity configuration through photometric or spectroscopic observations. The model has been validated against optical solar data and compared to alternative stellar surface activity models; for example SOAP code. The Sun is a unique laboratory to test stellar models because of the possibility to unambiguously relate flux variations to surface inhomogeneities’ configuration. This validation has been done by analysing a photometric time series from the Variability of Solar Irradiance and Gravity Oscillations (VIRGO) photometer on-board Solar and Heliospheric Observatory (SOHO) mission. Results have been compared to real solar images from the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) to confirm the goodness of the results in terms of surface inhomogeneities’ positions and dimensions. Conclusions. The description of stellar activity is a fundamental step in several astrophysical contexts and it is covered by the method we present. Our model offers a flexible and valuable tool to describe the activity of stars when it is dominated by spots and faculae.

A novel metric for quantifying solar irradiance stability: Mapping solar irradiance variability to photovoltaic power generation

A novel metric for quantifying solar irradiance stability: Mapping solar irradiance variability to photovoltaic power generation

Regional modeling of surface solar radiation, aerosol, and cloud cover spatial variability and projections over northern France and Benelux

Abstract. Investigating the current and future evolution of surface solar radiation (SSR) is essential in the context of climate change and associated environmental issues. We focus on the influence of atmospheric aerosols, along with cloud cover and water vapor content, on northern France and Benelux in spring and summer. Our analysis relies on the National Centre for Meteorological Research–Limited Area Adaptation Dynamic International Development v6.4 (CNRM-ALADIN64) regional climate model at 12.5 km resolution, which includes an interactive aerosol scheme. A regional evaluation of 2010–2020 ALADIN hindcast simulations of clear-sky and all-sky SSR, clear-sky frequency, and aerosols, through comparison to coincident multi-site ground-based measurements, shows reasonable agreement. In addition, these hindcast simulations emphasize how elevated aerosol loads over Benelux and high cloud cover over southwestern England reduce the SSR. Additional ALADIN climate simulations for 2050 and 2100 under Coupled Model Intercomparison Project phase 6 (CMIP6) Shared Socioeconomic Pathway (SSP) 1-1.9 predict a significant reduction in aerosol loads compared to 2005–2014, especially over Benelux, associated with future increases in clear-sky SSR but geographically limited all-sky SSR evolution. In contrast, under SSP3-7.0, clear-sky and all-sky SSR is projected to decline significantly over the domain. This decline is greatest in spring over Benelux due to combined increases in cloud cover and nitrate aerosols projected from 2050 onwards. In summer, projected decreases in cloud cover largely attenuate the reduction in SSR due to aerosols in 2050, while by 2100 rising water vapor contents counteract this attenuation. Thus, our results highlight seasonally and spatially variable impacts of future anthropogenic aerosol emissions on SSR evolution due to cloud cover and water vapor modifications that will likely largely contribute to the modulation of forthcoming aerosol influences.

The Use of Atmospheric Reanalysis Data for the Estimation of Solar Irradiation Considering the Effect of Atmospheric Aerosols over Brazil

In recent years, several studies have evaluated the potential of renewable energy sources in response to climate change and high energy demand. Due to its equatorial location and significant solar and wind potential, Brazil has incorporated alternative sources into its energy matrix, driven by more efficient and economical technologies for solar energy. However, the availability of observed data is still limited, and many studies rely on satellite estimates or extrapolations of in situ observations from other regions, compromising the efficiency of new technologies. This study uses NASA MERRA-2 reanalysis data to evaluate the influence of aerosols and cloudiness on the estimate of solar irradiance in Brazil. INMET stations were chosen in regions representative of the Brazilian climate and geography, with more than 12 years of observational data. MERRA-2 includes aerosol fields that interact with the model’s radiation fields, with a spatial resolution of 0.5° and hourly temporal resolution. Variables used include shortwave radiation fluxes and aerosol optical depth. Statistical indices used in performance analysis include mean bias, mean squared error, and Pearson correlation coefficient. The stations’ diurnal solar irradiance cycles were compared with MERRA-2 reanalysis data, considering different scenarios of aerosol and cloudiness effects. The reanalysis data represented the Bauru and Santa Maria stations well, while others, such as Barreiras and Goiânia, showed underestimation. Monthly cycling was also analyzed, highlighting seasonality, with greater amplitude in Santa Maria and lower in Caicó. In some locations, such as Campo Grande, the influence of aerosols is more significant, especially during the dry months, when forest fires, mainly in the Amazon region, increase the aerosol optical depth. The results show that reanalysis estimates can be used to evaluate the temporal variability of solar irradiation in regions without observational data. In conclusion, the study was able to evaluate the temporal variability of solar irradiation in Brazil using MERRA-2 atmospheric reanalysis data, demonstrating that, although there are differences with observational data, reanalysis estimates are useful in areas without observed data, with values correlation values above 0.8 and reaching values close to 0.95. However, although small, the differences observed between measured and estimated solar irradiation are generally caused by the inability of models to adequately represent the fraction of clouds and aerosols in the atmosphere.

Solar parking lot capacity: an abundant dual-use alternative to meet demand for the renewable energy transition

Abstract Most utility-scale solar facilities (USSFs) in the United States (US) are planned or installed in grasslands, pastures, agriculture land, and timberland, with aggressive future expansion plans. Investor-owned electricity providers have lobbied aggressively to remove obstacles to approval of USSFs on agricultural lands and for limits on regulations that support distributed solar installation. Rural USSF expansion may have potentially significant local, state, and regional impacts on agricultural production and the economy, property values and affordability, and conservation of habitats, connectivity, and endangered species. The potential use of parking lots in urban and suburban areas for solar development to meet demands is poorly studied and parking lot solar has not gained much traction in the US. The objective of this research was to estimate whether solar PV systems installed in parking lots can meet electricity demands across multiple urban and suburban metropolitan areas within the US. Parking lot data across four municipalities with variation in latitude, solar radiation, and population density was obtained, along with modeled annual electricity demand by sector. Parking lot area and potential generating capacity was calculated for canopy and carport designs using standard and premium efficiency panels, and annual electricity production was estimated using the National Renewable Energy Laboratory’s PVWatts Calculator. Premium efficiency canopy designs can meet ~100% of demand in some large car dependent low to medium density municipalities. High density urban areas may receive marginal direct benefit from parking lot solar development within their boundaries. Dual-use of lands without any ecosystem or productive value can substantially contribute to renewable energy mixes to meet demands.

Experimental and Modeling Study for the Solar-Driven CO2 Electrochemical Reduction to CO

With the rising levels of atmospheric CO2, electrochemistry shows great promise in decarbonizing industrial processes by converting CO2 into valuable products through scalable and sustainable technologies. In this framework, the present study investigates the solar-driven CO2 reduction toward carbon monoxide, achieved by the integration between the electrochemical reactor and dye-sensitized solar cells (DSSCs), both in experimental and modeling perspectives. COMSOL® Multiphysics 6.3 was used to develop a detailed finite element method model of the electrochemical cell integrated with a photovoltaic module, validated with the experimental results that demonstrated a strong correlation. A 2D model was designed, incorporating cathode and anode regions divided by an ion-exchange membrane. The model includes platinum foil and silver nanoparticles as catalysts for the oxygen evolution reaction and CO2 reduction reaction, respectively. Integration with the fundamental equations of the DSSCs was simulated to analyze the solar-driven CO2 reduction behavior under solar irradiance variations, offering a valuable tool for optimizing operating conditions and predicting the device performance under different environmental conditions. The integrated device successfully produces CO with a faradaic efficiency of 73.85% at a current density of J = 3.35 mA/cm2 under 1 sun illumination, with the result validated and reproduced by the mathematical model. Under reduced illumination conditions of 0.8 and 0.6 suns, faradaic efficiencies of 68.5% and 64.1% were achieved, respectively.

Variability of solar radiation and cloud cover in the Antarctic Peninsula region

Variability of solar radiation and cloud cover in the Antarctic Peninsula region

The capability of 20th‐century reanalyses to reproduce the spatiotemporal variation in surface incident solar radiation over Japan for 1931–2010

AbstractUnderstanding the spatiotemporal variations in surface incident solar radiation (Rs), especially over longer time spans, is crucial for climate modeling and environmental assessment. This study, referring to the century‐long homogenized Rs measurements, comprehensively quantifies the ability of five 20th‐century reanalyses phenomena to estimate Rs over Japan for 1931–2010, and furthermore assesses the contributions of total cloud cover and vapor pressure to the biases in Rs trends. Dominant features revealed that Rs signals boost from the north to the south, which could be captured but is overestimated by all reanalyses. The dominant time evolution of Rs was well captured by CERA20C, followed by ERA20C and 20CRv3, corresponding to their relatively better performance cloud cover. Unfortunately, ERA20CM totally fails to reproduce the spatiotemporal variation in Rs as its procedure does not include an assimilation step. 20CRv2c fails in estimating Rs for 1931–1960, but improves much for 1961–2010 as the quality of assimilated observational variables becomes better. A dimming of −2.76 W·m−2·decade−1 for 1931–1960 was detected in the observed Rs, which could be reproduced by CERA20C and ERA20C. Afterwards, a brightening of 0.88 W·m−2·decade−1 for 1961–2010 was shown in observations, and all reanalyses fail. The trend bias of Rs in CERA20C attributes more to total cloud cover before 1960, and to aerosols or other factors after 1960. Cloud cover contributed more than vapor pressure to the trend bias of Rs for 1931–1960; vapor pressure became more important in regulating Rs for 1961–2010 than for 1931–1960. The combined effects of both factors on Rs are insignificant for 20CRv3 during the entire period, for 20CRv2c for 1931–1960 and for CERA20C for 1961–2010, suggesting other factors, such as aerosols, cloud types, cloud optical depth and their interactions may impact Rs simulations more and this calls for further investigation.

Modelling Analysis and Characteristics of Multi-Level Inverter for Integrating Solar PV System to Grid

This paper investigates the implementation of a cascaded H-bridge multi-level inverter in a single-phase grid-connected solar photovoltaic (PV) system to address harmonic distortion challenges in DC-to-AC power conversion. The primary objective is to enhance power quality and system efficiency by leveraging the inverter's ability to reduce harmonics through power sharing and a lower switching frequency. A Simulink model is developed to evaluate the proposed system's performance, incorporating a unipolar Pulse Width Modulation (PWM) control strategy and an LCL filter for effective harmonic mitigation. The system employs Maximum Power Point Tracking (MPPT) based on the Perturb and Observe (P&O) algorithm, combined with Synchronous Reference Frame Theory-Phase Disposition PWM (SRFT-PDPWM) for optimal control. The proposed inverter ensures precise synchronization between the grid and inverter frequencies, achieving a Total Harmonic Distortion (THD) below 5%. Simulation results demonstrate the system's robust responsiveness to variations in solar irradiance, with increased irradiance leading to further reductions in THD and improved power quality. These findings validate the proposed inverter's capability to deliver high-quality, efficient power under varying environmental conditions, aligning with global objectives for integrating clean and sustainable energy into utility networks.

Enhancing solar efficiency in steppe regions: a comprehensive performance evaluation of photovoltaic systems in M’sila, Algeria

This study assesses a 10 kW grid-connected solar PV system in Algeria's harsh steppe climate in M'Sila, characterised by intense solar radiation and considerable temperature variations. High temperatures reduce photovoltaic conversion efficiency and increase thermal stresses, prompting a detailed investigation into PV systems' challenges in such environments. The research reveals that M'Sila's system generates 7,794.8 kWh annually, with an efficiency range of 16%–19.2%, peaking slightly. Unique thermal management techniques are necessary to sustain efficiency, as extreme heat causes efficiency losses of up to 20%. The levelized cost of energy (LCOE) of $0.0138 per kWh makes the system economically viable in these conditions. The investment proves worthwhile, yielding annual savings of $304. The M'Sila solar system also reduces CO₂ emissions by 4.07 tonnes per year. Algeria’s shift toward renewable energy and carbon reduction gains an additional $59.11 annually in “carbon credit” from these emissions reductions. This research supports Algeria’s renewable energy goals by guiding energy companies and decision-makers to improve efficiency and minimise environmental impact in steppe regions like M'Sila.

A Data Storage, Analysis, and Project Administration Engine (TMFdw) for Small- to Medium-Size Interdisciplinary Ecological Research Programs with Full Raster Data Capabilities

Over almost 20 years, a data storage, analysis, and project administration engine (TMFdw) has been continuously developed in a series of several consecutive interdisciplinary research projects on functional biodiversity of the southern Andes of Ecuador. Starting as a “working database”, the system now includes program management modules and literature databases, which are all accessible via a web interface. Originally designed to manage data in the ecological Research Unit 816 (SE Ecuador), the open software is now being used in several other environmental research programs, demonstrating its broad applicability. While the system was mainly developed for abiotic and biotic tabular data in the beginning, the new research program demands full capabilities to work with area-wide and high-resolution big models and remote sensing raster data. Thus, a raster engine was recently implemented based on the Geo Engine technology. The great variety of pre-implemented desktop GIS-like analysis options for raster point and vector data is an important incentive for researchers to use the system. A second incentive is to implement use cases prioritized by the researchers. As an example, we present machine learning models to generate high-resolution (30 m) microclimate raster layers for the study area in different temporal aggregation levels for the most important variables of air temperature, humidity, precipitation, and solar radiation. The models implemented as use cases outperform similar models developed in other research programs.

Implementation and investigation of a solar and wind energy-based grid-connected hybrid system with nine switch converter

In this paper, a hybrid, comprising of solar-PV and wind energy sources, grid-connected system with nine-switch converter (NSC) instead of a back-to-back (BtB) converter (comprising 12 switches) is proposed and a control scheme is also proposed for NSC. NSC is the potential substitute for the current BtB converter, which uses 12 switches. The NSC uses nine switches, that is, three less active switches than BtB converter therefore the system efficiency is increased by lowering component costs, counts of components, and switching losses. Vector control concept is considered to design the proposed control scheme for NSC to provide both supply power generation to the utility grid at unity power factor and variable speed constant frequency operation under fluctuating wind velocity and solar irradiance. In addition, 120° discontinuous modulation concept is integrated with the control scheme of NSC for reducing the switching losses of the system. Moreover, maximum power point trackers are used for capturing maximum power from wind and solar-PV, and blade pitch angle algorithm is also considered to restrict the generation from wind to its rated power level while speed is more than its rated level. The proposed system along with its control scheme is implemented in OPAL-RT lab to investigate its performance thoroughly under real-life scenarios (i) constant solar irradiance and variable wind velocity and (ii) variable solar irradiance and wind velocity. Results show the effectiveness of the proposed scheme on the system and it has been observed that the proposed scheme provides good dynamic responses in terms of less oscillation during transient, almost negligible (i.e. 0.0001 approx.) steady-state error etc. to the variation of input sources.

Development of a novel multifunctional superconducting device for performance enhancement of DC microgrids

DC microgrids have become an important infrastructure for increasing the integration of renewable energy sources in power grids. However, these DC microgrids suffer from inherent fluctuations from renewable energy sources as well as high fault currents during grid disturbances. Addressing these challenges simultaneously with a single device is a significant technical issue. This paper proposes a novel Multifunctional Superconducting Device (MSCD) with dual functionality to overcome these challenges. The MSCD can operate as a Superconducting Magnetic Energy Storage (SMES) system during normal operation, providing mitigation of renewable energy fluctuations through its shunt connection. During grid faults, the MSCD transits to operate as a Superconducting Fault Current Limiter (SFCL) in series with the grid, limiting the fault current. The proposed MSCD has only an additional controlled switch to the conventional power electronic converter of SMES. The dual functionality of the proposed MSCD has been evaluated using the PSCAD/EMTCD computer package under various operating conditions, validating its ability to seamlessly transition between SMES and SFCL modes. The proposed MSCD could effectively smooth DC bus voltage fluctuations caused by variations in wind speed and solar radiation and could significantly limit the fault current contribution from the DC microgrid under fault conditions. Furthermore, a superconducting coil with a pancake structure has been built and integrated with a control circuit to experimentally validate the functionality of the MSCD. Two experimental case studies were conducted - the first testing the charging and discharging behavior, and the second evaluating the current limiting capability of the MSCD prototype.

Intelligent fuzzy-PSO based grid connected solar EV charging station for electric buses to enhance stability and energy management

Abstract Modern power networks are relying more and more on Distributed Generation (DG) and solar energy-powered Electric Bus (EB) charging stations; nevertheless, controlling the charging process under situations of peak EB load and variable solar irradiation presents considerable hurdles. This study presents a coordinated control approach that maximizes energy distribution for solar (PV)-based EB charging stations by using an intelligent energy management system (IEMS). Through the analysis of load demand and meteorological data in real time, the IEMS optimizes PV generation and grid power consumption, thereby halving peak power demand, minimizing system losses, and easing distribution grid stress. In order to enable dynamic modifications to the energy flow between PV power, the grid, and battery storage, the method integrates an adaptive neuro-based fuzzy control system that anticipates solar energy generation and predicts EB load demands. Furthermore, to balance power distribution between battery and ultracapacitor systems and control energy storage in ultracapacitors, a fuzzy inference system optimized using Particle Swarm Optimization (PSO) is utilized. This prolongs battery life and guarantees effective energy management. The overall performance of the IEMS is improved by the fuzzy logic controller, which produces output signals that specify the best power distribution for any energy storage system. Digital simulations and a real-time hardware-in-loop experimental setup are used to validate the effectiveness of the proposed system, which shows notable gains in energy management, grid stability, and system efficiency. In order to improve intelligent energy management in grid-connected solar-powered systems, this study offers a viable method for incorporating renewable energy sources into EB charging stations.

A Novel EPSO Algorithm Based on Shifted Sigmoid Function Parameters for Maximizing the Energy Yield from Photovoltaic Arrays: An Experimental Investigation

This paper presents a new method for maximizing the energy production of photovoltaic (PV) arrays, utilizing the Enhanced Particle Swarm Optimization (EPSO) algorithm. In contrast with the classical PSO, the proposed EPSO algorithm employs shifted sigmoid functions in order to adapt the acceleration coefficients and the inertia weight instead of using constant coefficients. The EPSO algorithm proves its effectiveness even in challenging either condition, such as abrupt variations in solar irradiance or instances of partial shading conditions (PSCs). The proposed EPSO-based Maximum Power Point Tracking (MPPT) controller has undergone testing and a comparative analysis alongside well-known metaheuristic algorithms, which include the PSO, Bat Algorithm (BAT), Grasshopper Optimization Algorithm (GOA), and Grey Wolf Optimizer (GWO). The results obtained through simulations consistently demonstrate that the EPSO-based MPPT method surpasses other metaheuristic approaches in terms of energy yield and the speed at which it tracks the Maximum Power Point (MPP) under PSCs. In addition, the proposed EPSO algorithm demonstrates robustness, maintaining consistent tracking of the MPP during sudden solar radiation changes. Experimental validation on a real PV system affirms these findings, demonstrating that the EPSO algorithm surpasses its counterparts by achieving a high MPPT efficiency of approximately 99.19% in a fast-tracking time of 2.4 s while maintaining minimal power oscillations of around 2.04 %.

Estimation of the Contribution of Size-Fractionated Phytoplankton in the Kara Sea to Primary Production and Chlorophyll a during Different Seasons

Based on data obtained during seven Kara Sea expeditions (2017–2023), seasonal variation in the contribution of phytoplankton size groups to the total values of primary production (PP) and chlorophyll a (chl a) are examined for the first time. Micro- and nanophytoplankton (MPh + NPh) (>3 µm) dominated in the community composition during the entire ice-free period (June–October). Its predominance was especially noticeable during the spring bloom immediately after first-year sea-ice retreat (up to 97% for PP and up to 93% for chl a). The role of picophytoplankton (PPh) (<3 µm) increased in summer (July, August) (up to 50% for PP and up to 44% for chl a) and decreased by the end of the growing season (September, October). Seasonal variation in the size composition of phytoplankton during the growing season was determined mainly by variability in water temperature and incoming solar radiation. The contribution of PPh to the total chl a increased (up to 51%) at depths of the deep chlorophyll maximum in July and August. The assimilation activity of PPh was higher than that of MPh + NPh in July–September, with an increase in its contribution to the total PP and chl a. For the first time, annual PP of Kara Sea phytoplankton size groups was assessed: 8 ТgС (65%) for MPh + NPh and 5 ТgС (35%) for PPh.

Specialization of a new flux-based approach to thermal problems. Numerical study of glass façades of buildings with cast shadows

Uneven temperature distributions in the glass panes of building façades can induce thermal stresses exceeding material strength, a critical condition usually referred to as “thermal shock”. Here, we specialize in architectural glazing a recently-proposed flux-based variational approach to thermal problems, by considering heat exchanges resulting from climatic actions, in particular the variability of environmental temperatures and solar radiation, influenced by the shielding of cast shadows. The finite element implementation is facilitated because the unknown field is the heat displacement, whose time derivative is the heat flux, which is much smoother than the temperature field, on which the classical approach via Fourier’s law is based. A custom in-house-made FEM code is used to determine the effect of size and shape of cast shadows on temperature distribution in monolithic and laminated glass panes, also coupled in insulating glass units. We find that the temperature difference between irradiated and shadowed regions is only mildly influenced by the shape and size of the shadows, but the glass thickness determines the width of the transition zone and, hence, the temperature gradient. Practical rules for the design of glass façades are tentatively proposed.

Clearness index cluster analysis for photovoltaic weather classification based on solar irradiation measurement data: Theory and application

With the rapid expansion of photovoltaic (PV) power generation, accurately predicting and evaluating PV power output remains a critical challenge due to the inherent variability of solar irradiation. This paper proposes a novel weather classification approach based on the clearness index (CI), derived from solar irradiation data collected in Zhangbei, China, a region abundant in solar resources. Three clustering methods, which are Gaussian Mixture Model, K-Means, and Fuzzy C-Means (FCM), were applied to classify weather into three types: sunny, semi-cloudy, and cloudy. Our findings indicate that FCM consistently outperformed the other methods, particularly under variable conditions. By employing the classified data, short-term PV power forecasting is significantly enhanced. A hybrid CNN-LSTM model trained on the weather-clustered data achieved superior prediction accuracy compared to models trained without weather classification. Integrating weather classification into PV power prediction can notably reduce prediction errors and improve grid management. The proposed approach is recommended for future evaluations of solar applications in Zhangbei and other regions with similar climatic characteristics.

Techno-economic analysis and experimental validation of solar-assisted low-temperature water electrolysis for green hydrogen production: Insights from Afyonkarahisar

This study explores green hydrogen production via low-temperature water electrolysis powered by regional solar energy. A comprehensive techno-economic analysis was conducted to evaluate hydrogen production flow rate and unit costs using computational models developed in the Engineering Equation Solver (EES) and Aspen Plus software. Experimental setups at Afyon Kocatepe University’s energy laboratories were designed to validate these models, showing close alignment between computational and experimental results. Key findings demonstrate the feasibility of solar-assisted hydrogen production, with energy requirements reduced to 52.12 kWh/kg H2 by preheating water to 360 K. The small-scale prototype achieved a maximum hydrogen production rate of 1.829 × 10−6 kg/s, with energy and exergy efficiencies of 64.0 % and 62.46 %, respectively. The calculated levelized cost of hydrogen (LCOH) was 5.84 $/kg H2, with a payback period of 6.9 years, indicating long-term economic viability. This research also introduced novel electrode configurations and fluid variations, optimizing system efficiency under an average solar irradiance of 600 W/m2. This study supports the technical and economic feasibility of solar-driven hydrogen production in the Afyon region by assessing energy, exergy, and techno-economic parameters. It underscores its potential to reduce energy costs and contribute to Turkey’s energy security. The study highlights limitations, including the challenges of scaling up and solar irradiance variability, and suggests avenues for future research to optimize further and expand this technology.