Photovoltaic Production Research Articles

Distributed and explainable GHSOM for anomaly detection in sensor networks

The identification of anomalous activities is a challenging and crucially important task in sensor networks. This task is becoming increasingly complex with the increasing volume of data generated in real-world domains, and greatly benefits from the use of predictive models to identify anomalies in real time. A key use case for this task is the identification of misbehavior that may be caused by involuntary faults or deliberate actions. However, currently adopted anomaly detection methods are often affected by limitations such as the inability to analyze large-scale data, a reduced effectiveness when data presents multiple densities, a strong dependence on user-defined threshold configurations, and a lack of explainability in the extracted predictions. In this paper, we propose a distributed deep learning method that extends growing hierarchical self-organizing maps, originally designed for clustering tasks, to address anomaly detection tasks. The SOM-based modeling capabilities of the method enable the analysis of data with multiple densities, by exploiting multiple SOMs organized as a hierarchy. Our map-reduce implementation under Apache Spark allows the method to process and analyze large-scale sensor network data. An automatic threshold-tuning strategy reduces user efforts and increases the robustness of the method with respect to noisy instances. Moreover, an explainability component resorting to instance-based feature ranking emphasizes the most salient features influencing the decisions of the anomaly detection model, supporting users in their understanding of raised alerts. Experiments are conducted on five real-world sensor network datasets, including wind and photovoltaic energy production, vehicular traffic, and pedestrian flows. Our results show that the proposed method outperforms state-of-the-art anomaly detection competitors. Furthermore, a scalability analysis reveals that the method is able to scale linearly as the data volume presented increases, leveraging multiple worker nodes in a distributed computing setting. Qualitative analyses on the level of anomalous pollen in the air further emphasize the effectiveness of our proposed method, and its potential in determining the level of danger in raised alerts.

Photovoltaic hotspots: A mitigation technique and its thermal cycle

In the rapidly evolving field of solar energy, Photovoltaic (PV) manufacturers are constantly challenged by the degradation of PV modules due to localized overheating, commonly known as hotspots. This issue not only reduce the efficiency of solar panels but, in severe cases, can lead to irreversible damage, malfunctioning, and even fire hazards. Addressing this critical challenge, our research introduces an innovative electronic device designed to effectively mitigate PV hotspots. This pioneering solution consists of a novel combination of a current comparator and a current mirror circuit. These components are uniquely integrated with an automatic switching mechanism, notably eliminating the need for traditional bypass diodes. We rigorously tested and validated this device on PV modules exhibiting both adjacent and non-adjacent hotspots. Our findings are groundbreaking: the hotspot temperatures were significantly reduced from a dangerous 55 °C to a safer 35 °C. Moreover, this intervention remarkably enhanced the output power of the modules by up to 5.3%. This research not only contributes a practical solution to a longstanding problem in solar panel efficiency but also opens new pathways for enhancing the safety and longevity of solar PV systems.

Effect of Saharan dust episodes on the accuracy of photovoltaic energy production forecast in Hungary (Central Europe)

In order to meet global sustainability goals, in particular, the rapid decarbonisation of the energy sector in combination with geopolitical energy security issues, as well as further improvement in regional air quality-the so-called renewable energy sources are becoming increasingly critical and important. Photovoltaic power (PV) generation is clearly the most widely deployed (non-hydro) renewable energy source globally, which still has a significant growth potential in many countries, including Hungary.However, due to the intermittent nature and the strong dependency of photovoltaic power production on meteorological factors, continuous adjustment of electric power in the electric grid is needed. This, in turn, requires the most accurate forecast of the photovoltaic energy to be produced in the ultra-short term (15 min) and short term (1 day) in order to minimise last-minute and high-price energy acquisition or unplanned kicks-in of gas-fired auxiliary power plants.In this paper, we evaluate the impacts of large-scale dust transport episodes on the accuracy of forecasts of PV power generation. The numbers and intensities of Saharan dust storm events reaching Central Europe have increased over the last decade, hitting a new record in 2022 with 16 (!) African dust episodes observed over Hungary. We have shown that the semi-direct effect of atmospheric dust particles on high-level cloud formation rather than their direct irradiance-reducing effect is responsible for the reduced accuracies of e short-term (24-h) PV energy production forecasts during these events.

Volatile Perovskite Precursor Ink Enables Window Printing of Phase-Pure FAPbI3 Perovskite Solar Cells and Modules in Ambient Atmosphere.

Despite the great success of perovskite photovoltaics in terms of device efficiency and stability using laboratory-scale spin-coating methods, the demand for high-throughput and cost-effective solutions remains unresolved and rarely reported because of the complicated nature of perovskite crystallization. In this work, we propose a stable precursor ink design strategy to control the solvent volatilization and perovskite crystallization to enable the wide speed window printing (0.3 to 18.0 m/min) of phase-pure FAPbI3 perovskite solar cells (pero-SCs) in ambient atmosphere. The FAPbI3 perovskite precursor ink uses volatile acetonitrile (ACN) as the main solvent with DMF and DMSO as coordination additives is beneficial to improve the ink stability, inhibit the coffee rings, and the complicated intermediate FAPbI3 phases, delivering high-quality pin-hole free and phase-pure FAPbI3 perovskite films with large-scale uniformity. Ultimately, small-area FAPbI3 pero-SCs (0.062 cm2 ) and large-area modules (15.64 cm2 ) achieved remarkable efficiencies of 24.32 % and 21.90 %, respectively, whereas the PCE of the devices can be maintained at 23.76 % when the printing speed increases to 18.0 m/min. Specifically, the unencapsulated device exhibits superior operational stability with T90 >1350 h. This work represents a step towards the scalable, cost-effective manufacturing of perovskite photovoltaics with both high performance and high throughput.

Volatile Perovskite Precursor Ink Enables Window Printing of Phase‐Pure FAPbI3 Perovskite Solar Cells and Modules in Ambient Atmosphere

AbstractDespite the great success of perovskite photovoltaics in terms of device efficiency and stability using laboratory‐scale spin‐coating methods, the demand for high‐throughput and cost‐effective solutions remains unresolved and rarely reported because of the complicated nature of perovskite crystallization. In this work, we propose a stable precursor ink design strategy to control the solvent volatilization and perovskite crystallization to enable the wide speed window printing (0.3 to 18.0 m/min) of phase‐pure FAPbI3 perovskite solar cells (pero‐SCs) in ambient atmosphere. The FAPbI3 perovskite precursor ink uses volatile acetonitrile (ACN) as the main solvent with DMF and DMSO as coordination additives is beneficial to improve the ink stability, inhibit the coffee rings, and the complicated intermediate FAPbI3 phases, delivering high‐quality pin‐hole free and phase‐pure FAPbI3 perovskite films with large‐scale uniformity. Ultimately, small‐area FAPbI3 pero‐SCs (0.062 cm2) and large‐area modules (15.64 cm2) achieved remarkable efficiencies of 24.32 % and 21.90 %, respectively, whereas the PCE of the devices can be maintained at 23.76 % when the printing speed increases to 18.0 m/min. Specifically, the unencapsulated device exhibits superior operational stability with T90>1350 h. This work represents a step towards the scalable, cost‐effective manufacturing of perovskite photovoltaics with both high performance and high throughput.

Empirical study on optimization methods of building energy operation for the sustainability of buildings with integrated renewable energy

Owing to the challenges caused by climate change, the spread of green buildings is increasing. Moreover, it is becoming necessary to secure sustainability through the management of new and renewable energy operations. Accordingly, this paper describes the proposal and empirical demonstration of an energy operation optimization method for renewable energy-integrated buildings utilizing photovoltaics (PVs) and energy storage systems (ESSs). The proposed optimization method begins with the calculation of a weather index (WI) based on environmental and climatic data, with particular emphasis on its relevance to PV production. Additionally, we develop the weight-prox algorithm (W-PA) for controlling and optimizing daily building energy operations. The goal of this optimization method is to enhance the performance of three sustainability indicators of green buildings. Simulations are then conducted in a digital transformation environment using real green buildings as targets. The results demonstrate the improved performance of sustainability indicators, including renewable energy efficiency, economic resilience, and eco-friendliness. The output of this study can serve as a set of guidelines that considers the convenience of implementation for building operators. This study is also significant by raising awareness of the need for climate change response and contributing to the use of renewable energy for the sustainability of green buildings.

Solar parks can enhance bird diversity in agricultural landscape

Solar photovoltaic power parks are a relatively new anthropogenic habitat that will become more widespread in the future. The greatest potential for solar photovoltaic power production is on arable land and grassland. Knowledge on the impacts of solar parks on biodiversity is scarce and spatially limited. We investigated the impact of ground-mounted solar parks on species richness, abundance, Shannon diversity and composition of bird communities in Slovakia (Central Europe), taking into account pre-construction land cover, elevation and landscape context. We recorded breeding, foraging or perching birds on 32 solar park plots and 32 adjacent control plots (two hectares each) during single breeding season. We found that solar parks supported higher total bird species richness and diversity, and richness and abundance of invertebrate-eaters, and that the abundance of ground-foragers was higher in solar parks developed on grassland than in grassland control plots. Ordination analysis showed that solar parks had a different composition of bird communities and thus increased overall species diversity and beta diversity in the agricultural landscapes studied. Plot type and landscape context accounted for most of the variation in bird community composition. Black redstart, European stonechat, white wagtail and Eurasian tree sparrow were identified as indicator species for solar parks. The observed pattern could be due to the higher structural diversity of solar parks. The solar parks studied were designed and managed exclusively for electricity production. It can therefore be assumed that solar parks designed and managed in synergy with a stronger focus on wildlife would have an even greater positive impact on bird diversity in an agricultural landscape.

Comparative life cycle assessment of photovoltaic systems: An evaluation of environmental impacts over time

The production of photovoltaic (PV) systems is subject to continuous technological development and geographical shifts in manufacturing, leading to changes in economic aspects and environmental impacts. This work focuses on evaluating changes in environmental impacts over time using life cycle assessment (LCA). Several existing LCA studies and life-cycle inventory datasets for multi-crystalline silicon (multi-Si), single-crystalline silicon (single-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIS) were compared, and the reasons for deviations were analyzed.The comparison was carried out at the plant level for panel slanted roof systems, and the impact assessment results were compared to their counterparts from the ecoinvent v3.3 life cycle inventory cut-off database for individual datasets.The study found that technological advances in PV manufacturing have reduced environmental impacts. The differences between cell types converged over time, with CdTe having the lowest environmental impact during production. However, this may be relativized when considering the lower life time of CdTe cells in a cradle-to-grave assessment. Single-Si has a higher environmental impact during manufacturing, but it is just about compensated for compared to multi-Si. If a dataset for PV must be selected without cell-type knowledge, multi-Si plants are recommended as a proxy. The analysis provides insights into the environmental impacts of different PV systems over time and highlights the importance of regular updates of life-cycle inventories and consistent modeling.

Ongoing Waste Issue Towards the Development of Photovoltaic Module Recycling Regulations

In this paper, solar photovoltaic (PV) systems, that play an essential role in reducing fossil energy resources, are considered from a sustainability perspective. Solar PV systems, accounting for a significant capacity share among renewable energy sources, will have environmental and economic repercussions in the future. Despite photovoltaic (PV) modules contributing to the production of renewable energy for an average of 25 to 30 years, they pose a threat to the global environment. Policies should support the significance of initiatives. This will ensure that PV manufacturers, users, governments, and R&D researchers assume responsibility for PV module waste, which is expected to be encountered more frequently and rapidly in the near future. Prioritizing global awareness and policy development in the field of solar PV recycling will inspire future work. Furthermore, the prospect of other solar PV technologies consolidating their market position in the future will necessitate a solar PV recycling industry. This industry should encompass all developing PV technologies.

PV generation forecasting utilizing a classification-only approach

The increasing use of photovoltaic (PV) systems in electricity infrastructure poses new reliability challenges, as the supply of solar energy is primarily dependent on weather conditions. Consequently, to mitigate the issue, enhanced day-ahead PV production forecasts can be obtained by employing advanced machine learning techniques and reducing the uncertainty of solar irradiance predictions through statistical processing. The objective of this study was to present a methodology for accurately forecasting day-ahead PV production using novel machine learning techniques and a classification-only forecasting approach. Specifically, the central component of the proposed method is a classifier model based on an Extreme Gradient Boosting (XGBoost) ensemble algorithm that classifies the respective daily 30-min profiles of the forecasted global horizontal irradiance (GHI), the measured incident irradiance (Gi), and the AC power (PAC) into a predetermined number of classes. The formed classifier model was used as a dictionary to designate the newly arrived forecasted GHI to a particular class and ultimately identify the corresponding forecasted PAC. The results demonstrated that the proposed forecasting solution provided forecasts with a daily normalised root mean square error (nRMSE) of 8.20% and a mean absolute percentage error (MAPE) of 6.91% over the test set period of one year, while the model's reproducibility was also evaluated and confirmed. Additionally, a comprehensive evaluation based on clear-sky index categories revealed that the model's performance was notably accurate on clear-sky days, while maintaining acceptable accuracy levels on moderate and overcast days. These findings underscore the versatility and robustness of the proposed methodology in handling diverse weather conditions and hold promise for improved PV production forecasts.

Energy storage via synthetic fuel gases in electrical systems

This article looks at energy storage in the form of gas (hydrogen or methane) to compensate for the variability of intermittent wind and photovoltaic production in electricity systems. (This method of storage and retrieval is known as “power-to-gas-to-power”). Energy storage for other applications, such as mobility, is not covered here.

Stress tolerance of lightweight glass-free PV modules for vehicle integration

Electric vehicles (EVs) currently dominate the sales in the automotive market. A big leap in this market can be made by developing a photovoltaic product that can be integrated to an EV, as it can boost the driving range of the EV while reducing the charging frequency. Such vehicle-integrated photovoltaic (VIPV) products are already successfully demonstrated, but they are usually made with glass as a front sheet – making them bulky and limiting their use to the car roofs due to safety reasons. The contemporary focus of the research in the field of VIPV is on developing a product that is lightweight (LW) and easily integrable into the complex shapes of an EV. Therefore, in this work, we present our initial findings on a novel architecture for LW VIPV modules employing polycarbonate (PC) as a front sheet. The mechanical behaviour of the LW module under bending is successfully simulated using finite elements (FE) modelling to predict the fracture of the solar cells, which can then be used as a predictive tool to check the maximal load on the PV body of an EV before cracking the c-Si solar cells. We demonstrate that a change in the temperature of the PC-based LW modules can modify the interspacing between the cells and thus create stress on the connectors. The dog-bone connectors are found to allow almost unconstrained movement of the cells in the module when subjected to variation of temperature. The cell movements may result in mechanical fatigue of the interconnection, which can ultimately result in disconnection of the cells. Initial performance of the dog-bone connectors is investigated by applying mechanical fatigue experiments, which demonstrate that the special geometry of the dog-bone connector could endure a greater number of thermal cycles than a simple prismatic shape would.

Prioritizing circular economy strategies for sustainable PV deployment at the TW scale

Global decarbonization requires an unprecedented scale-up of photovoltaic (PV) manufacturing and deployment. The material demand and eventual end of life management associated with multi-TW scale deployment poses many challenges. Circular Economy (CE) and it's associated R-Actions (Reduce, Reuse, Recycle) have been proposed to mitigate end of life management and material sourcing concerns. However, CE metrics typically focus on a single product and only consider mass, excluding energy flows. This work leverages the PV in Circular Economy (PV ICE) tool to quantify the deployment, mass, and energy impacts of R-Actions and proposed sustainable PV designs in the context of achieving energy transition deployment goals (75 TW in 2050). 13 module scenarios are established and evaluated across 6 capacity, mass and energy metrics to identify tradeoffs and priorities. We find that increasing module efficiency can reduce near-term material demands up to 30% and improve energy metrics by up to 9%. Material circularity (recycling) can minimize lifecycle wastes and reduce material demands at the cost of higher energy demands. Increasing module lifetime, including reliability improvements and reuse strategies, is effective at reducing both material (>10%) and energy demands (24%). Uniquely, lifetime improvements maximize benefits and minimize the harms across all six metrics while achieving multi-TW scale deployment.

A Current Ripple Suppression Strategy of TAB for Photovoltaic Hydrogen Production

A Current Ripple Suppression Strategy of TAB for Photovoltaic Hydrogen Production

Comparative Analysis of Photovoltaic Simulation Software with Actual Conditions of 93.6 kWp On-Grid Photovoltaic System

The Indonesian government encourages an increase in the energy mix, especially renewable energy such as on-grid rooftop PV, which has an intermittent nature and high investment costs, so the development of rooftop PV must be carefully calculated by considering system reliability. Thus, PV systems can be optimised in terms of tilt angle, number of PV modules, module type, module support structure, and inverter. Many simulation tools are currently used to design PV systems using climate data. These simulation tools estimate energy production and provide corresponding economic data. However, the reliability of these data varies because the system productivity of the photovoltaic system productivity depends on the climatic conditions of the photovoltaic system depends on the climatic conditions. Therefore, in this study, two simulation tools, namely helioscope and RETScreen, will be evaluated in comparison with the actual conditions in the field of 93.6 kWP on-grid PV system. Based on the comparison results, Helioscope and RETScreen are reliable simulation tools because the results obtained are closest to the actual energy value.

Tabu Search Algorithm for Optimal Electric Water Heating Energy Management

The aim of this paper is to develop an optimal energy management strategy for electric water heaters (EWHs), based on operational data such as photovoltaic (PV) production, ambient temperature (AT), water demand (WD), and fixed appliance consumption (FAC), electricity tariffs and user preferences. The proposed strategy uses a multi-objective Tabu Search (TS) algorithm to determine the optimal domestic hot water output over the next 24 hours based on this prior data. The aim is to minimize the electricity bill while maintaining a desired level of comfort, by keeping the water temperature within a range compatible with the user’s thermal comfort. In addition, the temperature setpoint was varied according to operating conditions. A comparison with particle swarm optimization (PSO)-based management, which uses a similar strategy but employs the PSO algorithm for optimization, reveals that the proposed strategy achieves a significant 33.2% reduction in electricity costs and a 3.8% reduction in carbon emissions.

Using a ledger to facilitate autonomous peer-to-peer virtual net metering of solar photovoltaic distributed generation

To assist electric utilities to overcome limitations of centralized billing and encourage distributed production of solar photovoltaic (PV) electricity, this study designs and assesses a novel open-source autonomous virtual utility to monitor users and enable peer-to-peer trading. This study provides system design and software implementation of the concept using blockchain technology written in Solidity and Truffle. A set of smart contracts adds users to a system and monitors their demand, PV generation, and facilitates transactions between users on an hourly basis when one user has PV-generated excess electricity, and another has demand. Unit tests for each of the contracts’ methods are developed in Solidity, and data on gas usage and costs is collected. Once the contracts have been written and evaluated, a JavaScript simulation is developed to use the contracts on real load and PV generation data for one year on an hourly basis. The results of two case studies are quantified: 1) true peers, where all houses are prosumers with rooftop PV, and 2) intermittent transition case, where PV deployment and demand are more varied. The results found that with ten users in the system, the true peers case study resulted in an uneconomic number of exchanges, but the intermittent transition case study resulted in more than a factor of twenty increases in exchanges and net cost savings. The savings more than doubles for both cases when time of use pricing is in effect. The system utility increases with more variability of PV production across participating users and is recommended for utilities targeting increases in distributed generation during the energy transition.

Optimizing Electric Vehicle Charging Infrastructures in the Smart Grid with a Multi-Objective Approach

This paper introduces a tool designed to optimize electric vehicle (EV) charging infrastructures within the smart grid framework. The tool utilizes a multi-objective approach and is programmed in Python. It enables dynamic management of energy distribution among different EV charging infrastructures, addressing scenarios where surplus photovoltaic (PV) power generation exceeds charging demands but faces challenges due to storage costs and electric energy transmission rates to alternative infrastructures. In instances of low PV production relative to charging demand, the algorithm strategically selects the optimal procurement strategy, either purchasing electric energy from neighboring infrastructures or utilizing surplus PV energy for direct charging. The tool empowers stakeholders to make informed decisions by facilitating comparisons between the cost of storing electric energy locally and the expense of procuring it from external sources, thereby enhancing the efficiency and cost-effectiveness of EV charging infrastructures in the smart grid ecosystem. Extensive simulations and case studies demonstrate the efficacy of the proposed approach, showcasing its potential to optimize energy distribution and promote sustainable practices within the EV charging domain.

Solar photovoltaic panel production in Mexico: A novel machine learning approach

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10−4 m3/kWh) and the emissions generated (1.12 × 10−6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.

From shadows to light: The role of latent networks in mainstreaming solar PV practices

Practice theories have generated interest in sustainability transitions research as a means to study shared expectations, conventions and routines that maintain unsustainable consumption within the dominant regime. However, collective action to change practices, which is crucial for innovation, is seldom examined in practice theory research. This article applies practice theories to conceptualize the nature of collective action in shaping energy practices, using insights from a literature review on residential photovoltaic (PV) production by German citizens. We demonstrate the significance of latent networks, which refer to individuals involved in similar parallel practices, in promoting the adoption of solar power in homes. Our results show that both multi-actor and latent networks contributed to mainstreaming residential solar PV practices. Multi-actor networks encompassed households and various service providers, each integrated within larger latent networks.