Solar

Decentralized solar water disinfection systems (DSODIS) in continuous flow systems are alternatives for large-scale improved water access in rural contexts. However, DSODIS in rural Ghana are limited. An exploratory sequential mixed-methods design was used to explore the enablers of and barriers to, as well as reported barrier perceptions to, the effective implementation of DSODIS in the Sawla-Tuna-Kalba (STK) District of Ghana. The qualitative data (26 respondents) were analyzed thematically, and the quantitative data (1155 household heads) were subjected to Poisson regression analyses. Enablers were categorized into themes such as willingness to pay for DSODIS, household and community participation, and willingness to use water from DSODIS. Similarly, the barriers include environmental barriers, technological barriers, economic barriers, and political and legal barriers. Household characteristics such as main water source and income, age group, education, marital status, household size, being born in the community, and years living in the community are statistically associated with reported barrier perceptions. Households with unimproved water sources and high income (IRR = 1.432, p = 0.000) and improved water sources and high income (IRR = 1.295, p = 0.000) are 43% and 30% more likely, respectively, to report more barrier perceptions compared with households with unimproved water sources and low income. Females (IRR = 1.070, p = 0.032) are marginally more likely to report more barrier perceptions compared with males. The model output also indicates that household heads with higher educational attainment (IRR = 1.152, p = 0.001) are 15% more likely to report more barrier perceptions compared with those with no formal education. These findings provide valuable information for policymakers and stakeholders aiming to provide quality water in rural Ghana where centralized systems cannot be installed. Full article

Solar photovoltaic (PV) panels are the best solution to reduce greenhouse gas emissions by fossil fuel combustion, with global capability now exceeding 714 GW due to rapid technological advances in solar panels (SPs). However, SPs’ efficiency and lifespan remain limited due to the absence of advanced fault-detection systems, and they are prone to short circuits (SC), open circuits (OC), and power degradation. Therefore, this large-scale production requires reliable, real-time fault diagnosis to maintain panel performance. However, traditional diagnostic methods implemented using MPPT, neural networks, or microcontroller-based systems often rely on complex computational algorithms and are not cost-effective. So, this paper proposes a diagnostic system composed of six functional blocks to address this issue. The proposed system was initially verified using an Intel DE-10 Lite FPGA board. Once its functionality was confirmed, an ASIC design was proposed for mass production, offering a significantly lower implementation cost and reduced hardware complexity than prior methods. Different circuit designs were developed for each of the six blocks. All designs were created using Cadence software and TSMC 180 nm technology files. The basic components used in these designs include PMOS transistors with 300 nm channel length and 2 µm width, NMOS transistors with 350 nm channel length and 2 µm width, as well as resistors and capacitors. Differential amplifiers with a gain of 40 dB were used for voltage and current sensing from the SP. The chip activation signal generator circuit was designed with an adjustable frequency and generated 120 MHz and 100 MHz signals in this work. The decision-making block, Logic Driver Circuit, was innovatively implemented using a reduced number of transistors. A custom memory block with a reset switch was also implemented to store the fault value detected at the SP. Finally, the proposed ASIC was implemented for fabrication, which is highly cost-effective in mass production and does not require complex computational stages. Full article

As photovoltaics’ (PVs) capacity will probably rapidly expand to tens of terawatts globally, the diversification of the PV technology portfolio becomes essential. Perovskite technology proffers promise for expanding solar energy market segments like building-integrated PVs and flexible PVs for the residential and industrial sectors. In this perspective, we calculate that under reasonably attainable values for the module cost, conversion efficiency, and degradation rate, a levelized cost of electricity (LCOE) of 10 EURct/kWh can be reached for perovskite PV in 2035. Furthermore, if, in 2035, the conversion efficiency can be increased to 25% and the degradation rate falls to below 1%, with a module cost of 50 EUR/m², the LCOE for perovskite PV could become around 8 EURct/kWh. For lower module costs, the LCOE would drop further, by which cost competitiveness with c-Si PV is in sight. We point out that even if the LCOE of perovskite solar modules may remain relatively high, they could still occupy an important role, particularly in the residential sector, thanks to their flexibility and lightweight properties, enabling a large suite of new applications. Overall, to push perovskite PVs towards successful commercialization, policy support will be indispensable. Full article

This study analyzes the impact of powder-blasted surface modification on the performance of non-imaging solar concentrators and evaluates a ray-tracing simulation approach to virtual solar power measurements. Powder blasting was applied to poly(methyl methacrylate) (PMMA) sheets to create a rough, Lambertian-like scattering surface, enhancing light trapping and total internal reflection. The effects of this modification were systematically assessed using optical transmission spectroscopy, angular scattering measurements, and solar cell efficiency characterization under standard AM1.5 illumination. The results show that surface roughening significantly improves light redirection toward the concentrator’s edge, enhancing solar cell performance. OptisWorks ray-tracing simulations were employed to model the concentrator’s optical behavior, demonstrating strong agreement (within 5–10% deviation) with experimental data. These findings confirm that surface modification is crucial in optimizing concentrator efficiency and establishing ray tracing as a reliable tool for virtual performance evaluation in photovoltaic applications. Full article

This paper discusses the thermal and exergy efficiency analysis of the hydrothermal liquefaction (HTL) process, which converts sewage sludge into biocrude oil in a continuous plug–flow reactor using a linear Fresnel solar collector. The investigation focuses on the influence of key operational parameters, including slurry flow rate, temperature, pressure, residence time, and the external heat transfer coefficient, on the overall efficiency of biocrude oil production. A detailed thermodynamic evaluation was conducted using process simulation principles and a kinetic model to assess mass and energy balances within the HTL reaction, considering heat and mass momentum exchange in a multiphase system using UDF. The reactor’s receiver, a copper absorber tube, has a total length of 20 m and is designed in a coiled configuration from the base to enhance heat absorption efficiency. To optimize the thermal performance of biomass conversion in the HTL process, a Computational Fluid Dynamics–Discrete Element Method (CFD-DEM) coupling numerical method approach was employed to investigate improved thermal performance by obtaining a heat source solely through solar energy. This numerical modeling approach allows for an in-depth assessment of heat transfer mechanisms and fluid-particle interactions, ensuring efficient energy utilization and sustainable process development. The findings contribute to advancing solar-driven HTL technologies by maximizing thermal efficiency and minimizing external energy requirements. Full article

The residential sector is energy-consuming and one of the biggest contributors to climate change. In France, the adoption of photovoltaics (PV) in that sector is accelerating, which contributes to both increasing energy efficiency and reducing greenhouse gas (GHG) emissions, even though the technology faces several issues. One issue that slows down the adoption of the technology is the “duck curve” effect, which is defined as the daily variation of net load derived from a mismatch between power consumption and PV power generation periods. As a possible solution for addressing this issue, electric water heaters (EWHs) can be used in residential building as a means of storing the PV power generation surplus in the form of heat in a context where users’ comfort—the availability of domestic hot water (DHW)—has to be guaranteed. Thus, the present work deals with developing model-based predictive control (MPC) strategies—nonlinear/linear MPC (MPC/LMPC) strategies are proposed—to the management of EWHs in individual dwellings equipped with grid-connected PV systems. The aim behind developing such strategies is to improve both the PV power generation self-consumption rate and the economic gain, in comparison with rule-based (RB) control strategies. Inasmuch as DHW and power demand profiles are needed, data were collected from a panel of users, allowing the development of profiles based on a quantile regression (QR) approach. The simulation results (over 6 days) highlight that the MPC/LMPC strategies outperform the RB strategies, while guaranteeing users’ comfort (i.e., the availability of DHW). The MPC/LMPC strategies allow for a significant increase in both the economic gain (up to 2.70 EUR) and the PV power generation self-consumption rate (up to 14.30%ps), which in turn allows the ${\mathrm{CO}}_2$ emissions to be reduced (up to 3.92 $\mathrm{k}\mathrm{g}{\mathrm{CO}}_{2.\mathrm{eq}})$. In addition, these results clearly demonstrate the benefits of using EWHs to store the PV power generation surplus, in the context of producing DHW in residential buildings. Full article

Photovoltaic (PV) systems are at the heart of the energy transition, providing an essential source of clean, renewable energy for applications such as solar pumping, which is essential for irrigation and rural water supply. However, their efficiency depends directly on the quality and performance of the modules, which are often affected by defects or unfavorable environmental conditions. This article presents the development of an innovative automated tool designed for advanced characterization of PV modules by analyzing key parameters such as voltage and current. The system integrates measurement sensors (voltage, current, temperature, etc.), an Arduino Mega board and an SD card, enabling real-time data collection, processing, and recording under various environmental conditions. The results of the experimental tests demonstrate a significant improvement in the PV panel selection process, ensuring optimized choices at the time of purchase and rigorous monitoring during operation. This innovation contributes to maximizing energy performance and extending panel longevity, reinforcing their role in the transition to a sustainable energy model. Full article

Upscaling perovskite solar cells and modules requires precise laser patterning for series interconnection and spatial characterization of cell parameters to understand laser–material interactions and their impact on performance. This study investigates the use of nanosecond (ns) and picosecond (ps) laser pulses at varying fluences for the P3 patterning step of perovskite solar cells. Hyperspectral photoluminescence (PL) imaging was employed to map key parameters such as optical bandgap energy, Urbach energy, and shunt resistance. The mappings were correlated with electrical measurements, revealing that both ns and ps lasers can be utilized for effective series interconnections with minimal performance losses at optimized fluences. Our findings provide a deeper understanding of fluence-dependent effects in P3 patterning. Moreover, the results demonstrate that the process window is robust, allowing for reasonable cell performance even with deviations from optimal parameters. This robustness, coupled with the scalability of the laser patterning process, emphasize its suitability for industrial module production. Full article

Solar thermal energy is one of the most interesting sustainable solutions for decarbonizing the energy sector. Integrating solar collectors with other energy sources is common, as seen in domestic heating, where solar collectors are combined with common heaters to reduce fuel consumption (gasoline, electricity, gas, and biomass) and therefore, the energy cost. Similarly, this concept can be applied to nuclear energy, where the reduction in nuclear fuel consumption is very strategic for decreasing not only its cost but also the risk in handling, transportation, and storage (both the fuel and the nuclear waste as well). Nuclear energy, on the other hand, seems to be very useful in reducing the land occupation of concentrated solar power plants (CSPs) and helping a more constant production of electricity, both points being two important bottlenecks of CSP technologies. CSP and nuclear reactors, on the other hand, share common heating technologies and both can produce energy without CO₂ emissions. Solar and nuclear energy, especially with the advent of the fourth generation of small modular reactors (SMRs), present a compelling opportunity for sustainable electricity generation. In this work, we present a brief review of CSP technology, a brief review of SMR concepts and development, and a brief overview of the combination of these two technologies. The review shows that in general, combined SMR + CSP technologies offer several advantages in terms of a strong reduction in the solar field extension areas, improved dispatchability of energy, improved efficiency of the SMRs, and, in particular, lower nuclear fuel consumption (hence, e.g., with a lowered refueling frequency). Full article

Urban soiling, consisting of dust, industrial byproducts, and other pollutants, presents a significant risk to the effectiveness and safety of solar energy systems. To achieve the goal of net zero, having renewable energy systems such as solar panels in urban environments can help. This review will examine the composition and variety of urban soiling and evaluate its impact on PV installation. The study will analyze the efficiency loss attributable to soiling, focusing on its impact on small-scale installations such as rooftops, building integrated photovoltaics (BIPVs), and large-scale urban solar installations. Furthermore, this study will also investigate various developing technologies and strategies to reduce the effects of urban soiling. This encompasses the examination of automated cleaning systems and robotic maintenance, with a specific focus on their potential effectiveness. This review aims to underline the importance of addressing urban soiling within the framework of sustainable urban development and the expansion of solar energy, with further research into the development of soiling mitigation technologies. Finally, soil management and further research gaps will be discussed. Full article

In the decarbonization process, the solar energy sector will play a crucial role, representing one of the key technologies for reducing greenhouse gas emissions. In Italy, photovoltaics stands out as the fastest-growing energy sector, thanks to the combination of favorable climatic conditions, supportive policies, and a growing interest in renewable energy sources. In this context, renewable energy communities (RECs) emerge as potential strategic tools for promoting the development of photovoltaics nationally and at the European level. Therefore, this study aims to examine the policy and regulatory frameworks governing RECs in Europe and Italy, highlighting their impact on the establishment, operation, and evolution of these communities. Through a critical analysis of legislative documents at both the European and national levels, this research identifies the key factors shaping the growth and functionality of RECs, such as governance structures, economic incentives, and social inclusivity. This study underscores the dual influence of comprehensive regulation and a certain degree of flexibility in fostering RECs’ adaptability to diverse contexts. Additionally, it identifies existing challenges, including regional implementation disparities, legal ambiguities, and potential conflicts with other renewable energy policies. The findings contribute to the ongoing discourse on decentralized energy systems, providing insights for policymakers to refine frameworks and maximize RECs’ contributions to sustainable energy transitions. Full article

This paper presents findings from the LEOPARD project, part of the LEAP-RE program, a joint European Union (EU) and African Union initiative to advance renewable energy solutions. The study employs a simulation-based approach to optimize solar-integrated microgrid configurations for rural electrification. The project deployed a solar-integrated pilot microgrid at the Songhai agroecological center in Benin to address key challenges, including load profile estimation, energy balancing, and diesel dependency reduction. A hybrid methodology integrating predictive modeling, real-time solar and weather data analysis, and performance simulations was employed, leading to a 65% reduction in diesel reliance and an LCOE of EUR 0.47/kWh. Quality control measures, including compliance with IEC 61215 and IEC 62485-2 standards, ensured system reliability under extreme conditions. Over 150 days, the system consistently supplied energy, preventing 10.16 tons of ${CO}_2$ emissions. Beyond the Benin pilot, the project conducted feasibility assessments in Senegal to evaluate microgrid replicability across different socio-economic and environmental conditions. These analyses highlight the scalability potential and the economic viability of expanding solar microgrids in rural areas. Additionally, this research explores innovative business models and real-time diagnostics to enhance microgrid sustainability. By providing a replicable framework, it promotes long-term energy access and regional adaptability. With a focus on community involvement and capacity building, this study supports efforts to reduce energy poverty, strengthen European–African collaboration, and advance the global clean energy agenda. Full article

The global energy landscape is experiencing growing challenges, with energy crises in regions such as South Africa underscoring the drive to accelerate the shift toward renewable energy solutions. This paper presents an approach for improving solar energy planning, specifically focusing on leveraging the capabilities of the ATlite software in conjunction with custom data. Using mathematical models, ATlite (which was initially developed by the Renewable Energy Group at the Frankfurt Institute for Advances Studies) is a Python software package that converts historical weather data into power generation potentials and time series for renewable energy technologies such as solar photovoltaic (PV) panels and wind turbines. The software efficiently combines atmospheric and terrain data from large regions using user-defined weights based on land use or energy yield. In this study, European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5) data was modified using Kriging to enhance the resolution of each data field. This refined data was applied in ATlite, instead of utilizing the standard built-in data download and processing tools, to generate solar capacity factor maps and solar generation time series. This was utilized to identify specific PV technologies as well as optimal sites for solar power. Thereafter, a simulated power generation time series was compared with measured solar generation data, resulting in a root mean square error (RMSE) of 19.6 kW for a 250 kWp installation. This approach’s flexibility and versatility in the inclusion of custom data, led to the conclusion that it could be a suitable option for renewable energy planning and decision making in South Africa and globally, providing value to solar installers and planners. Full article

The rapid acceptance of solar photovoltaic (PV) energy across various countries has created a pressing need for more coordinated approaches to the sustainable monitoring and maintenance of these widely distributed installations. To address this challenge, several digitization architectures have been proposed, with one of the most recently applied being the digital twin (DT) system architecture. DTs have proven effective in predictive maintenance, rapid prototyping, efficient manufacturing, and reliable system monitoring. However, while the DT concept is well established in fields like wind energy conversion and monitoring, its scope of implementation in PV remains quite limited. Additionally, the recent increased adoption of autonomous platforms, particularly robotics, has expanded the scope of PV management and revealed gaps in real-time monitoring needs. DT platforms can be redesigned to ease such applications and enable integration into the broader energy network. This work provides a system-level overview of current trends, challenges, and future opportunities for DTs within renewable energy systems, focusing on PV systems. It also highlights how advances in artificial intelligence (AI), the internet-of-Things (IoT), and autonomous systems can be leveraged to create a digitally connected energy infrastructure that supports sustainable energy supply and maintenance. Full article