Performance evaluation of optimal photovoltaic-electrolyzer system with the purpose of maximum Hydrogen storage

Power electronics-based electrolyzer systems are prevalently in current use. This paper proposes the more recently employed directly coupled photovoltaic (PV) electrolyzer systems. Equipped with accurate electrical models of the advanced alkaline electrolyzer, PV system and Hydrogen storage tank simulated using MATLAB, the system\u27s performance for a full week is analyzed using Miami, Florida\u27s meteorological data. A multi-level Genetic Algorithm (GA)-based optimization facilitates maximum hydrogen production, minimum excess power generation, and minimum energy transfer loss. The crucial effect of temperature on the overall system performance is also accounted for by optimizing this parameter using GA, maintaining operating conditions close to the Maximum Power Point (MPP) of the PV array. The results of the analysis have been documented to show that the optimal system for a 10 kW electrolyzer can produce, on an average, Hydrogen of 0.0176 mol/s, when the system is operating with 6.3% power loss and 2.4% power transfer loss

International Collaboration Framework for the Calculation of Performance Loss Rates: Data Quality, Benchmarks, and Trends (Towards a Uniform Methodology)

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

In this study, the design and simulation of a combination of a photovoltaic (PV) array with an alkaline electrolyzer is performed to maximize the production of hydrogen as a reliable power resource. Detailed electrical model of PV system, as long as thermal and electrochemical model of electrolyzer is used. Since an electrolyzer is a non-linear load, its coupling with PV systems to get the best power transfer is very important. Solar irradiation calculations were done for the region of Miami (FL, USA), giving an optimal surface slope of 25.7° for the PV array. The size of the PV array is optimized, considering maximum hydrogen production and minimum excess power production in a diurnal operation of a system using the imperialistic competitive algorithm (ICA). The results show that for a 10 kW alkaline electrolyzer, a PV array with a nominal power of 12.3 kW The results show that 12.3 kW photvoltaic system can be utilized for supplying a 10 kW electrolyzer. Hydrogen production and Faraday efficiency of the system are 697.21 mol and 0.3905 mol, respectively

Optimal design of hybrid wind/photovoltaic electrolyzer for maximum hydrogen production using imperialist competitive algorithm

Abstract The rising demand for high-density power storage systems such as hydrogen, combined with renewable power production systems, has led to the design of optimal power production and storage systems. In this study, a wind and photovoltaic (PV) hybrid electrolyzer system, which maximizes the hydrogen production for a diurnal operation of the system, is designed and simulated. The operation of the system is optimized using imperialist competitive algorithm (ICA). The objective of this optimization is to combine the PV array and wind turbine (WT) in a way that, for minimized average excess power generation, maximum hydrogen would be produced. Actual meteorological data of Miami is used for simulations. A framework of the advanced alkaline electrolyzer with the detailed electrochemical model is used. This optimal system comprises a PV module with a power of 7.9 kW and a WT module with a power of 11 kW. The rate of hydrogen production is 0.0192 mol/s; an average Faraday efficiency of 86.9 percent. The electrolyzer works with 53.7 percent of its nominal power. The availability of the wind for longer periods of time reflects the greater contribution of WT in comparison with PV towards the overall throughput of the system

Optimal Operation of Combined Photovoltaic Electrolyzer Systems

In this study, the design and simulation of a combination of a photovoltaic (PV) array with an alkaline electrolyzer is performed to maximize the production of hydrogen as a reliable power resource. Detailed electrical model of PV system, as long as thermal and electrochemical model of electrolyzer is used. Since an electrolyzer is a non-linear load, its coupling with PV systems to get the best power transfer is very important. Solar irradiation calculations were done for the region of Miami (FL, USA), giving an optimal surface slope of 25.7° for the PV array. The size of the PV array is optimized, considering maximum hydrogen production and minimum excess power production in a diurnal operation of a system using the imperialistic competitive algorithm (ICA). The results show that for a 10 kW alkaline electrolyzer, a PV array with a nominal power of 12.3 kW The results show that 12.3 kW photvoltaic system can be utilized for supplying a 10 kW electrolyzer. Hydrogen production and Faraday efficiency of the system are 697.21 mol and 0.3905 mol, respectively

Time-Series Energy Consumption Dataset of an Educational Building

This is an example of dataset used in the paper titled "Automated pipeline framework for processing of large-scale building energy time series data" authored by "Arash Khalilnejad, Ahmad M. Karimi, Shreyas Kamath, Rojiar Haddadian, Roger H. French, Alexis R. Abramson"

Proactive Identification of Patients with Diabetes at Risk of Uncontrolled Outcomes during a Diabetes Management Program: Conceptualization and Development Study Using Machine Learning

BackgroundThe growth in the capabilities of telehealth have made it possible to identify individuals with a higher risk of uncontrolled diabetes and provide them with targeted support and resources to help them manage their condition. Thus, predictive modeling has emerged as a valuable tool for the advancement of diabetes management. ObjectiveThis study aimed to conceptualize and develop a novel machine learning (ML) approach to proactively identify participants enrolled in a remote diabetes monitoring program (RDMP) who were at risk of uncontrolled diabetes at 12 months in the program. MethodsRegistry data from the Livongo for Diabetes RDMP were used to design separate dynamic predictive ML models to predict participant outcomes at each monthly checkpoint of the participants’ program journey (month-n models) from the first day of onboarding (month-0 model) up to the 11th month (month-11 model). A participant’s program journey began upon onboarding into the RDMP and monitoring their own blood glucose (BG) levels through the RDMP-provided BG meter. Each participant passed through 12 predicative models through their first year enrolled in the RDMP. Four categories of participant attributes (ie, survey data, BG data, medication fills, and health signals) were used for feature construction. The models were trained using the light gradient boosting machine and underwent hyperparameter tuning. The performance of the models was evaluated using standard metrics, including precision, recall, specificity, the area under the curve, the F1-score, and accuracy. ResultsThe ML models exhibited strong performance, accurately identifying observable at-risk participants, with recall ranging from 70% to 94% and precision from 40% to 88% across the 12-month program journey. Unobservable at-risk participants also showed promising performance, with recall ranging from 61% to 82% and precision from 42% to 61%. Overall, model performance improved as participants progressed through their program journey, demonstrating the importance of engagement data in predicting long-term clinical outcomes. ConclusionsThis study explored the Livongo for Diabetes RDMP participants’ temporal and static attributes, identification of diabetes management patterns and characteristics, and their relationship to predict diabetes management outcomes. Proactive targeting ML models accurately identified participants at risk of uncontrolled diabetes with a high level of precision that was generalizable through future years within the RDMP. The ability to identify participants who are at risk at various time points throughout the program journey allows for personalized interventions to improve outcomes. This approach offers significant advancements in the feasibility of large-scale implementation in remote monitoring programs and can help prevent uncontrolled glycemic levels and diabetes-related complications. Future research should include the impact of significant changes that can affect a participant’s diabetes management

IEA PVPS Task 13-ST2.5: PLR Determination Benchmark Study

Datasets (quantity indicated) provided by following power plants for use in PLR determination benchmark study: EURAC (1), FOSS (1), RSE (2), Pfaffstaetten (3), US DOE (8), NREL (4), and EDF (4)