Photovoltaic Research Articles

Machine Learning Approaches in Advancing Perovskite Solar Cells Research

AbstractThe integration of machine learning (ML) with perovskite solar cells (PSCs) signifies a groundbreaking era in photovoltaic (PV) technology. The traditional iterative approaches in PSC research are often time‐consuming and resource‐intensive. In contrast, ML leverages available data and sophisticated algorithms to quickly identify properties and optimize parameters for novel materials and devices. This review explores how ML‐driven approaches are improving various facets of PSCs research, including the rapid screening of novel compositions, enhancing stability, refining device architectures, and deepening the understanding of underlying physics. The paper is structured to gradually familiarize readers with essential terminologies and concepts, ensuring a solid foundation before delving into more intricate topics. A concise workflow and various introductory toolkits for ML are also briefly discussed. Through a detailed analysis of compelling case studies, a basic research framework within ML‐PSC‐integrated research is provided. This comprehensive review can serve as a valuable reference for researchers aiming to understand and leverage ML‐driven approaches in PSCs research, advancing the path for more efficient and sustainable PV technologies.

PV Array Reconfiguration for Global Maximum Power Optimizing under Partial Shading Conditions based on PV Switched System

Background: Due to the partial shading effects on different photovoltaic (PV) modules in a PV array, PV module operating conditions are inconsistent, and PV array output power is significantly reduced. Although the maximum power point (MPP) of a non-uniform irradiance PV array can be observed through global maximum power point tracking (GMPPT), no evaluation of the array's energy potential has been done. Objective: One of the most effective solutions to overcome the negative effects of partial shading in PV systems is the PV array reconfiguration process. To optimize the electrical structure of the PV array as the PV modules are partially shaded in a non-uniform manner, this study proposes a promising technique for dynamic reconfiguring a PV array in order to improve the extracted maximum power from a PV array under partial shading conditions (PSC). Methods: PV modules are rearranged by iteratively sorting them to allow the PV array with nonuniform irradiance to produce as much power as possible. This is conducted by applying a switching matrix to implement a PV-switched system approach. The proposed system with different PV array dimensions (e.g., 3×4, 4×6, and 5×8) is assessed in order to validate the proposed algorithm. A MATLAB/Simulink PV array developed model is used to find the global maximum power point for the different PV array dimensions in both pre-reconfiguration and post-reconfiguration states. Results: A detailed numerical comparison of the extracted power from the proposed system has been provided. Conclusion: The results show that the proposed system has the potential to extract the exact global maximum power for a PV-switched system under PSC, irrespective of array dimensions, according to simulation results.

A distributed photovoltaic short-term power forecasting model based on lightweight AI for edge computing

Abstract In recent years, an immense amount of distributed photovoltaics are integrated into low-voltage distribution network, producing a substantial volume of operational data. The centralized cloud data center cannot process massive amounts of data precisely and promptly. Therefore, the operational status of distributed photovoltaic systems in low-voltage distribution network becomes difficult to predict. However, edge computing in the distribution network enables local data processing to improve the forecasting service’s real-time reliability. In this regard, this paper proposes a distributed photovoltaic short-term power forecasting model based on lightweight AI algorithms. Firstly, based on the Pearson correlation coefficient method, an analysis is conducted on the historical operational data in the network to extract important meteorological features correlated with the photovoltaic power output. Secondly, a distributed photovoltaic power forecasting model for the distribution network is constructed based on the Xception and attention mechanism. Finally, the model is trained using pruning, which removes redundant parts of the model, resulting in a compact and efficient forecasting model. By validating real-world datasets, the results demonstrate that the model presented in this article has a smaller size and higher forecasting accuracy than other state-of-the-art forecasting models.

Broad-Scale Electroluminescence Analysis of 5 Million+ Photovoltaic Cells for Defect Detection and Degradation Assessment

This paper presents a comprehensive study on the detection, classification, and impact of defects in photovoltaic (PV) modules, using Electroluminescence (EL) imaging as the primary diagnostic tool. The study inspects 85,000 PV modules across 167 installations over a nine-year period, with defects categorized into line cracks, complex cracks, edge-ribbon cracks, and Potential-Induced Degradation (PID). A key finding reveals an increasing prevalence of complex and edge-ribbon cracks in recent installations, highlighting potential shifts in manufacturing or installation processes. Despite these observed defects, the degradation rates of the PV systems remain below 1% per year, with long-term projections indicating that most systems will retain over 80% of their original capacity after 25 years. This study underscores the importance of early defect detection for maintaining system efficiency and longevity, and its findings are crucial for refining manufacturing and installation standards. Novel insights include the integration of EL imaging for defect classification and the suggestion of semantic segmentation techniques to further improve automated defect detection. These advancements hold the potential to optimize the predictive maintenance of PV systems, ensuring enhanced performance and reduced operational losses over time.

Optimal capacity allocation and scheduling strategy for CSP+PV hybrid standalone power plants

Solar photovoltaic (PV) power generation, as an important clean technology, has been widely adopted globally, especially in remote island areas where access to the main power grid is unavailable. PV power can serve as the primary energy source for standalone island grids. However, due to its dependence on sunlight, PV power output fluctuates, particularly during nighttime and under poor lighting conditions, necessitating the integration of energy storage technology or alternative power generation methods to ensure continuous power supply. Concentrating solar power (CSP) generation, as an emerging technology, can provide efficient power output when solar radiation is abundant and ensure continuous power supply through thermal energy storage systems during adverse weather conditions or nighttime. Although CSP offers advantages such as dispatchability, its high construction and maintenance costs may pose challenges in commercial deployment. Hybrid solar power plants combining both PV and CSP technologies leverage the strengths of both, ensuring more stable and economically viable power output. This study establishes a model for hybrid solar power plants, considering the impact of PV and CSP component capacities and proportions on performance and costs, i.e., capacity allocation. To maximize the overall benefits of standalone microgrids while ensuring the stability of the power station, a capacity allocation method guided by economic dispatch is proposed. Through iterative analysis, the optimal configuration is determined to minimize the system's equivalent annual costs. Simulation experiments validate the financial feasibility of hybrid solar power plants and the reliability of the proposed configuration method.

An agent-based modeling approach for simulating solar PV adoption: A case study of Irish dairy farms

The agricultural sector faces increasing pressure to enhance energy efficiency in light of escalating electricity costs. This study aims to simulate the adoption of photovoltaic (PV) systems in Ireland’s dairy sector using an agent-based modeling (ABM) approach to facilitate PV uptake among dairy farmers. The model incorporates factors such as grid energy costs, annual electricity consumption, annual solar generation, PV cost, and maintenance expenses to predict PV adoption likelihood. Findings reveal that by 2022, about 2.41% of Irish dairy farmers had adopted PV systems, a figure only 0.41 pp higher than the actual observed rate, validating the ABM’s accuracy. Additionally, the research forecasts future adoption rates of PV systems among dairy farmers, demonstrating the efficacy of ABM in understanding and predicting renewable energy uptake in the dairy sector. These insights can inform policy suggestions to promote renewable energy adoption, ultimately enhancing energy efficiency and sustainability in dairy sector.

Innovative materials integrated with PCM for enhancing photovoltaic panel efficiency: An experimental investigation

The electrical power generated by a photovoltaic panel is crucial due to the high demand for electricity. Operating temperatures above the Standard Test Conditions (STC) negatively impact the output power, which is a significant drawback of this technology. Therefore, it is essential to keep the front and back surface temperatures of the photovoltaic panel as low as possible. Phase Change Material (PCM) is a good passive cooling method, but it has low thermal conductivity. This paper introduces a novel material to address this issue. Iron Filling Waste (IFW) is integrated with PCM to cool the photovoltaic panel. This investigation is presented experimentally with three modules. The highest surface temperature of the reference panel, 78 °C, is recorded for the uncooled module, while with PCM- IFW is 70.5 °C. The comparative analysis shows that the PV module cooled by PCM-IFW achieved an efficiency enhancement and power output increase of 39 % and 33 % respectively, compared to the reference panel without cooling. IFW-PCM achieves an average enhancement in efficiency and output power by 23 % and 11 %, respectively, compared with panels that use PCM only. Also, IFW enhances thermal conductivity without any additional economic cost for the cooling systems.

Optimization of PV benefits for Electric Vehicles Charging Station Using a Deterministic Method and Fuzzy Inference System Method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of -863.4 c€/day, whereas the FIS method delivers greater savings of -898.7 c€/day. For fast charging, the deterministic approach yields savings of -524.6 c€/day, while the FIS method results in even higher savings of -540.7 c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20%, compared to 81.60% with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Sustainable management of end of life crystalline silicon solar panels in Australia: Advancing circular economy practices

The worldwide adaptation of Photovoltaic (PV) technology as a sustainable alternative to fossil fuels, has experienced exponential growth in recent years. However, the lack of effective waste management policies has hindered efficient PV panel disposal and recycling practices. In the absence of effective policies, the worldwide End-of-Life (EoL) PV module accumulation is predicted to reach a critical stage in the early 2030s. This study examines the environmental impacts of different EoL management practices using Life Cycle Assessment (LCA), based on a comprehensive database generated from both literature and industrial data. Five different EoL scenarios were considered for 1000 kg of Crystalline Silicon (c-Si) PV modules with a focus on Australia as a case study, while considering the energy recovery options and emphasizing the economic benefits. From a comprehensive LCA study, it is found that upcycling options reduce the environmental impacts significantly compared to downcycling, while chemical treatment emerges as a preferred option, demonstrating a reduced environmental burden. Nevertheless, the utilization of toxic chemicals during chemical treatment-based PV recycling needs to be reconsidered. Additionally, the usage of chemicals during the metal recovery process of PV module recycling caused the highest environmental burden, necessitating the importance of moving to greener treatment practices. This research study will enable the identification of optimum EoL c-Si module recycling options, contributing to sustainable waste management.

Modeling the co-adoption dynamics of PV and heat pumps in Swiss residential buildings: Implications for policy and sustainability goals

The Swiss energy system is facing a paradigm shift as it strives to achieve net-zero greenhouse gas emissions, as well as phase out nuclear electricity production. This entails significant changes in the country’s energy landscape, including increased adoption of renewable energy technologies in the residential sector. To facilitate informed decision-making and planning by policymakers, this study introduces a system-dynamics model for the long-term adoption of solar photovoltaic (PV) and heat pump (HP) technologies in Swiss residential buildings. Unlike conventional approaches, this framework considers the feedback loops and correlations between PV and HP adoption, while also accounting for building heterogeneity. Through scenario analyses, the model evaluates the impact of regulatory and financial policy interventions on the transition of the residential energy system. The results highlight the significant influence of policy measures on technology deployment rates, energy demand, and greenhouse gas emissions. They demonstrate that slight adjustments in current policy and regulatory framework could allow to safely reach PV deployment targets, but strong modifications are necessary to completely decarbonize the residential sector. This study contributes to advancing our understanding of the complex dynamics shaping residential energy transitions and offers valuable insights to support the formulation and implementation of effective energy strategies.

Boosting electricity generation associated with Saudi Arabi buildings using PCM and PV cells on walls and roof leading to a sustainable building

In Saudi Arabia, where the majority of regions receive a minimum incident shortwave solar energy exceeding 4 kWh/(m2.year), there is a high potential for electricity generation and simultaneously, a challenge in building cooling. In this study, by adding photovoltaic (PV) on the roof and wall, as well as using phase change material (PCM) inside the walls, electricity production and energy consumption of Saudi residential buildings were investigated. Taking into account the effects of radiation on vertical and horizontal envelopes as well as phase change in PCM, the energy equation was solved using DesignBuilder to specify hourly energy consumption and electricity generation. When installing PV cells at the optimal tilt, incoming radiation to the cells increases. However, creating shadows on subsequent rows of cells diminishes the effective PV surface area. Surprisingly, calculations revealed that if PV cells are installed at zero angle, owing to installing more PV cells, up to 31% extra electricity is produced than when installed at the optimal tilt. As a side effect, the cooling load decreases by 5.1% due to reduced radiation intensity on the roof. The orientation of the walls significantly impacts both phase change material (PCM) and PV-associated electricity generation. Placing PCM on the east wall optimizes its performance, while walls containing PV panels perform best when facing south. To further enhance cooling and reduce electricity demand, phase change material was incorporated into both the roof and wall, resulting in a 2% reduction in overall electricity demand. Notably, in eight major populated areas across Saudi Arabia, under the constraint of the constant PV cell area, installing PV cells on the roof proves three times more advantageous than placing them on the walls.

A minute-resolution downscaling algorithm for high-resolution global irradiance time series

Numerous studies have demonstrated the significant impact of the resolution of solar irradiation data on the outcomes of hourly production models. Accurate integration of photovoltaic (PV) systems sometimes demands a high-resolution global horizontal irradiance (GHI) time series to capture the rapid fluctuations in PV power output induced by swift irradiance changes. Most of the available databases provide data at hourly resolution, leading to a lack of accuracy in PV simulations. Those existing hourly averages of global horizontal irradiance in open sources fail to represent this volatility adequately, especially when PV systems are coupled with fast ramp rate technologies. In the present work, an easy-to-use algorithm is implemented to synthesize high-resolution GHI time series from hourly averaged and clear sky irradiance datasets. By employing Linear interpolation, a technique that helps to achieve the desired time resolution and afterward computing critical factors, the algorithm identifies periods characterized by short-term weather phenomena, thus creating a high-resolution time series that accurately represents these dynamics. Avoiding the probabilistic components and machine learning techniques conserves computational power and reduces calculation time, but this comes at the cost of reduced fidelity in reproducing the results. Improving accuracy in PV simulations is not always directly related to reproducing real phenomena, but enhancing the amount of information contained in the data is sufficient. This study’s approach enhances user-friendliness and facilitates seamless integration into existing energy modeling frameworks, aiming for representation with sub-hourly time steps. The algorithm’s effectiveness is demonstrated by applying the model to hourly averaged data to revert them to a one-minute time step, and finally comparing the synthetically produced one-minute GHI data to the original measured data. The comparative analysis between synthesized and measured data demonstrated a strong agreement, with normalized mean bias error (MBE) values ranging between 1.8% and 9.6% and normalized root mean square error (NRMSE) values between 2.7% and 16.1%, depending on weather conditions. Additionally, the coefficient of determination (R2) consistently remained above 0.64. Successful algorithm validation makes our algorithm suitable for use in meteorological datasets and locations, with similar climatic characteristics.

Soft Switching Technique in a Modified SEPIC Converter with MPPT using Cuckoo Search Algorithm

: MPPT refers to the process of continuously tracking and adjusting the operating point of a photovoltaic (PV) system to maximize the power output from the solar panels. The operating point at which a PV system produces the most power under a specific set of environmental circumstances is known as the maximum power point (MPP). The Maximum power point tracking (MPPT) technique is used to overcome the challenges of PV arrays with variable irradiance levels, which collects the greatest power from the PV array. Standalone PV systems, as well as gridconnected systems, can benefit from a DC-DC converter with MPPT. This study compares the standard perturbation and observation (P&O) technique with the soft-switching method used in the SEPIC converter utilizing the cuckoo search (CS) algorithm using MATLAB/Simulink. Reviewing the simulation results and assessing the SEPIC converter's performance. Background: SEPIC soft-switching converter, the soft-switching technique is applied to the SEPIC converter topology. It typically uses additional components, such as inductor and capacitors, to enable soft-switching operation. These additional components help to control the voltage and current waveforms across the main switching devices, reducing switching losses. Methods: The proposed method uses a soft switching technique to reduce switching losses and to improve efficiency. Soft switching is used in SEPIC (Single Ended Primary Inductor Converter) converters, a type of DC-DC converter, to enhance their performance. The projected solution further addresses the maximum power point tracking (MPPT) issue in PV systems using the Cuckoo Search (CS) method. Results: Soft switching technique is implemented in SEPIC converters to reduce these switching losses. In this system, Zero Voltage Switching (ZVS) involves turning on the switch when the voltage across it is zero. This allows the current to flow through the switch without creating any significant switching losses. CS algorithm can track the MPP quickly and accurately, it exhibits less prone to oscillations, easily monitor the MPP under a variety of weather circumstances. However, it exhibits negligible oscillation in a steady state, which results in significant power savings. Conclusion: The modified SEPIC converter with a soft-switching MPPT cuckoo algorithm is used with a solar-powered system. A prototype comprising a solar panel, a SEPIC converter, a driver, and a controller circuit was created. A soft-switching SEPIC converter with an MPPT cuckoo algorithm for the PV system is implemented, as are the converter operation, converter analysis, and theoretical analysis, and a controller circuit is analysed. The PV system, MPPT controller with tracking algorithm, and PMSM load were used in the system simulation. A 60-watt SEPIC converter prototype is built, and the outcomes are also tested experimentally.

Current-Day and Future Dunkelflaute Risks for Belgium

Abstract Dunkelflautes, events of low renewable production and potentially high demand, are the most extreme examples of the susceptibility of a renewable energy system with respect to weather variability. Yet, these events currently lack a precise definition in the literature. Based on 41 years of weather data, we explore the resilience of the Belgian energy system with respect to the duration and extremeness of Dunkelflautes or energy drought. Although the hourly average load cannot be covered 89% of the time in our analysis, we find that Dunkelflaute occurrences are generally independent of the energy mix between photovoltaic (PV) panels and wind energy (WE) production (for fractions between 10% and 60% of PV). However, to cope with Dunkelflautes, a system with full PV or full WE provision is very ineffective. While the optimal mix is found to be independent of Dunkelflaute extremeness, it strongly depends on the Dunkelflaute duration: For subdaily or longer than two-daily events, a high WE fraction is optimal, while a high PV fraction is needed for durations of the order of days. More than 56% of Dunkelflautes over Belgium coincide with those in the surrounding countries. Finally, as opposed to weak changes in the climatological average, future climate projections reveal an increase in Dunkelflaute severity, mainly due to a reduction in WE production.

Performance evaluation of different photovoltaic array configurations under partial shading

In Partial Shading Conditions (PSCs), Photo Voltaic (PV) systems often experience notable output power and efficiency reductions due to weather variations. This study is dedicated to determining the most effective PV array configuration under partial shading. Various configurations, including Series Parallel (SP), Total Cross Tied (TCT), Bridge Linked (BL), Honey Comb (HC), Double Tied (DT), and hybrid connections, are simulated and evaluated under PSCs. Nine shading patterns, such as vertical, horizontal, centre-wise, upper triangular, cross-wise, expansive, arbitrary, L-shaped, and diagonal, are examined using a 6 × 6 array of PV Configuration. Performance analysis is based on parameters such as open circuit voltage (Voc), short circuit current (Isc), Global Maximum Power Point (GMPP), maximum voltage (Vm), maximum current (Im), Fill Factor (FF), Mismatch Loss (ML)/Power Loss (PL), and efficiency (η). Additionally, hybrid configurations like Alternate- Total Cross Tied -Bridge Linked (ALT-TCT-BL), Alternate -Total Cross Tied -Double Tied (ALT-TCT-DT), and Alternate -Total Cross Tied -Triple Tied (ALT-TCT-TT) are investigated and compared with existing configurations. Hybrid configurations with fewer cross-ties are recommended to simplify circuit complexity. MATLAB/Simulink software is employed for simulation, using a Sunpower SPR-E18-295-COM panel. Comparative analysis confirms that hybrid configurations exhibit higher efficiency for various shading patterns than existing configurations, equal or at par with the TCT connection. For eight connections and nine shading scenarios along with zero (no) shading conditions, eighty distinct combinations have been analysed, and on comparative analysis with contemporary work, the optimal output is obtained from the connections considered under PSCs. For all the shading patterns considered, the proposed work fetches maximum efficiency compared to the contemporary work, and it ranges between 13.61 % and 17.84 % for different shading patterns. The maximum value of efficiencies for horizontal, vertical, diagonal, centre-wise, expansive, arbitrary, upper triangular and L-shaped shading patterns is 13.77 %, 17.24 %, 17.84 %, 15.66 %, 13.61 %, 17.21 %,17.35 % and 15.11 % respectively. The hybrid configuration achieves higher efficiency than current methods for all shading patterns and configurations, with improvements in efficiency ranging from 2.1 % to 42.22 % across all cases.

Hybrid renewable energy systems for sustainable power supply in remote location: Techno-economic and environmental assessment

Electricity supply is inconsistent and unreliable in many remote areas of India, where depending solely on a single renewable energy source is impractical. In this context, this study investigates the potential of off-grid hybrid renewable energy systems (HRES) to meet the energy needs of a village community in India. Techno-economic analysis and life cycle assessment have been employed to compare eleven HRES combinations which combine photovoltaic (PV), wind turbine (WT), battery (BAT), diesel generator (DG), biogas generator (BG), converter (CONV), and electrolyser (ELEC). By optimising the size and capacity of each component in HRES, this study aims to identify the combination with the lowest levelised cost of electricity (LCOE). This research aligns with United Nations Sustainable Development Goal No. 7 to seek “Affordable and Clean Energy”. The findings highlight that HRES comprising PV/WT/BAT/CONV/DG exhibits the lowest LCOE (0.319 $/kWh) and net present cost (6.81 M$) among all combinations. In systems with partial reliance on diesel, integrating both PV and WT could reduce diesel consumption and increase the renewable fraction to 87.4 %. For HRES involving PV, a significant contribution to greenhouse gas emissions occurs during the construction stage. The WT/DG combination, with its high diesel dependency, has the largest global warming potential. The efforts from this study provide valuable insights into determining the optimal HRES for remote communities by considering their economic and environmental factors.

Evaluation of household electricity consumption in multi-apartment buildings for optimization of rooftop PV systems

Empowering citizen energy communities (CECs) to produce, self-consume and share renewable energy can enhance household energy efficiency, support the adoption of renewable energy sources, reduce energy consumption and supply tariffs, and increase independence from fossil fuels. This study analyzes actual electricity consumption data from 31 dwellings in typical five-story multi-apartment buildings in Riga, Latvia, considering the potential for rooftop solar energy systems within CECs. The novelty of this study lies in the detailed analysis of household electricity consumption data and characteristics (e.g., household size, population, etc.) to predict household load which is essential for the optimal design of a multi-apartment building scale PV system. We evaluate the suitability of typical multi story buildings for rooftop photovoltaic (PV) systems, aiming to determine the potential PV performance relative to final electricity consumption. The study utilizes PolySun computer simulation software. Our findings indicate that a rooftop PV system on a typical 5 story multi-apartment building (60 apartments) can cover up to 77% of annual electricity consumption. This analysis is based on hourly real electricity consumption data. The research outcomes can inform optimization of on-site energy storage to mitigate the impact of PV systems on the grid. The proposed solution is applicable in many Eastern European countries and in some Western European countries that were influenced by the Soviet Union in the latter half of the 20th century.These regions have a similar stock of panel-constructed multi-apartment residential buildings and share comparable climatic conditions.

Attaining a wide photoluminescence Stokes-shift of carbon dots obtained from waste biomass

Owing to the tremendous physicochemical and quantum confinement characteristics of carbon quantum dots (CQDs), they have revealed exciting and essential visions in the field of energy conversion and energy storage. However, due to their low photoluminescence quantum yield (PLQY) and/or short Stokes-shift, limits their application in photovoltaic (PV). In this regard, a novel and cost-effective method was used to synthesize the CQDs using waste biomass. Furthermore, a comprehensive study was made to explore the applicability of the CQDs as photon downconverters/downshifters in PVs. The average size of the CQDs are 5.1, 4.4, and 3.9 nm for the synthesis temperature of 160, 180, and 200 ℃, respectively. The EDS analysis revealed that C, O, Mg, Na, Cl, and K are present in all the CQDs. The PL emission peak position is blue-shifted with the rise in the synthesis temperature. The Stokes-shift is reduced with a rise in the synthesis temperature. The highest Stokes-shift was obtained at excitation wavelength of 300 nm for CQDs synthesized at 160 ℃. The PLQY increased by 3.5 and 7.2-folds for the rise in the temperature from 160 to 180 and 200 ℃, respectively. The investigation revealed that the applicability of CQDs can be enriched via the selection of the synthesis parameters.

Bridging the gap: A comparative analysis of indoor and outdoor performance for photovoltaic-thermal-thermoelectric hybrid systems

This research evaluates the performance of a hybrid thermal-thermoelectric photovoltaic air collector system (PV/T-TE) through experimental investigations. By integrating photovoltaic (PV) panels with thermoelectric (TE) modules, the system aims to enhance efficiency and energy output by converting waste heat into additional electricity. The study examines the impact of radiation intensity, ranging from 455.65 to 795.18 W/m2, on the system's performance, utilizing output temperature (To) and plate temperature (Tp) as key metrics. Managing waste heat in PV technology remains a challenge, impacting efficiency. Traditional PV/T systems generate both electricity and thermal energy but suffer efficiency losses due to inadequate heat management. Integrating TE modules into PV/T systems offers a promising solution, but optimal configurations and performance impacts under varying conditions are underexplored. Limited research focuses on PV/T-TE hybrid systems, with most studies addressing PV-TE systems. The objectives of this research are to experimentally assess the impact of integrating TE modules on the thermal and electrical efficiency of PV/T systems under varying radiation intensities and air mass flow rates. The methodology involves setting up a PV/T-TE hybrid system, conducting experiments under controlled conditions, and analyzing key performance metrics. The study explores optimal TE module configurations and installation techniques to maximize heat transfer and electricity generation. Comparative analysis with conventional PV/T systems establishes the superiority of the hybrid system. Findings indicate significant benefits from incorporating TE modules, enhancing energy output and efficiency by maintaining optimal PV panel temperatures.