Sustainable strategies for crystalline solar cell recycling: A review on recycling techniques, companies, and environmental impact analysis

Solar PV is gaining increasing importance in the worldwide energy industry. Consequently, the global expansion of crystalline photovoltaic power plants has resulted in a rise in PV waste generation. However, disposing of PV waste is challenging and can pose harmful chemical effects on the environment. Therefore, developing technologies for recycling crystalline silicon solar modules is imperative to improve process efficiency, economics, recovery, and recycling rates. This review offers a comprehensive analysis of PV waste management, specifically focusing on crystalline solar cell recycling. The classification of PV recycling companies based on various components, including solar panels, PV glass, aluminum frames, silicon solar cells, junction boxes, plastic, back sheets, and cables, is explored. Additionally, the survey includes an in-depth literature review concentrating on chemical treatment for crystalline solar cell recycling. Furthermore, this study provides constructive suggestions for PV power plants on how to promote solar cell recycling at the end of their life cycles, thereby reducing their environmental impact. Moreover, the techno-economic and environmental dimensions of solar cell recycling techniques are investigated in detail. Overall, this review offers valuable insights into the challenges and opportunities associated with crystalline solar cell recycling, emphasizing the importance of economically feasible and environmentally sustainable PV waste management solutions in the constantly evolving solar energy market

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA’s operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis

Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques

This paper discuss virtual power plant (VPP) and Microgrid controller for energy management system (EMS) based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA) and particle swarm optimization algorithm (PSO). The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion

Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution

Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency

Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

This paper aims to investigate a hybrid photovoltaic (PV) biogas on-grid energy system in Al-Ghabawi territory, Amman, Jordan. The system is accomplished by assessing the system’s reliability and economic viability. Realistic hourly measurements of solar irradiance, ambient temperature, municipal solid waste, and load demand in 2020 were obtained from Jordanian governmental entities. This helps in investigating the proposed system on a real megawatt-scale retrofitting power system. Three case scenarios were performed: loss of power supply probability (LPSP) with total net present cost (TNPC), LPSP with an annualized cost of the system (ACS), and TNPC with the index of reliability (IR). Pareto frontiers were obtained using multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm. The system’s decision variables were the number of PV panels (Npv) and the number of biogas plant working hours per day (tbiogas). Moreover, three non-dominant Pareto frontier solutions are discussed, including reliable, affordable, and best solutions obtained by fuzzy logic. Double-diode (DD) solar PV model was implemented to obtain an accurate sizing of the proposed system. For instance, the best solution of the third case is held at TNPC of 64.504 million USD/yr and IR of 96.048%. These findings were revealed at 33,459 panels and 12.498 h/day. Further, system emissions for each scenario have been tested. Finally, decision makers are invited to adopt to the findings and energy management strategy of this paper to find reliable and cost-effective best solutions

Active Power Control to Mitigate Frequency Deviations in Large-Scale Grid-Connected PV System Using Grid-Forming Single-Stage Inverters

Over the last few years, the number of grid-connected photovoltaic systems (GCPVS) has expanded substantially. The increase in GCPVS integration may lead to operational issues for the grid. Thus, modern GCPVS control mechanisms should be used to improve grid efficiency, reliability, and stability. In terms of frequency stability, conventional generating units usually have a governor control that regulates the primary load frequency in cases of imbalance situations. This control should be activated immediately to avoid a significant frequency variation. Recently, renewable distribution generators such as PV power plants (PVPPs) are steadily replacing conventional generators. However, these generators do not contribute to system inertia or frequency stability. This paper proposes a control strategy for a GCPVS with active power control (APC) to support the grid and frequency stability. The APC enables the PVPP to withstand grid disturbances and maintain frequency within a normal range. As a result, PVPP is forced to behave similar to traditional power plants to achieve frequency steadiness stability. Frequency stability can be achieved by reducing the active power output fed into the grid as the frequency increases. Additionally, to maintain power balance on both sides of the inverter, the PV system will produce the maximum amount of active power achievable based on the frequency deviation and the grid inverter’s rating by working in two modes: normal and APC (disturbance). In this study, a large-scale PVPP linked to the utility grid at the MV level was modeled in MATLAB/Simulink with a nominal rated peak output of 2000 kW. Analyses of the suggested PVPP’s dynamic response under various frequency disturbances were performed. In this context, the developed control reduced active power by 4%, 24%, and 44% when the frequency climbed to 50.3 Hz, 50.8 Hz, and 51.3 Hz, respectively, and so stabilized the frequency in the normal range, according to grid-code requirements. However, if the frequency exceeds 51.5 Hz or falls below 47.5 Hz, the PVPP disconnects from the grid for safety reasons. Additionally, the APC forced the PVPP to feed the grid with its full capacity generated (2000 kW) at normal frequency. In sum, the large-scale PVPP is connected to the electrical grid provided with APC capability has been built. The system’s capability to safely ride through frequency deviations during grid disturbances and resume initial conditions was achieved and improved. The simulation results show that the given APC is effective, dependable, and suitable for deployment in GCPVS