Browsing by Author "Kosmopoulos, P."

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Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles
(MDPI, 2023) Sidharthan, V.P.; Kashyap, Y.; Kosmopoulos, P.
The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1%, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively. © 2023 by the authors.
Airborne Kite Tether Force Estimation and Experimental Validation Using Analytical and Machine Learning Models for Coastal Regions
(MDPI, 2022) Castelino, R.V.; Kashyap, Y.; Kosmopoulos, P.
Wind power can significantly contribute to the transition from fossil fuels to renewable energies. Airborne Wind Energy (AWE) technology is one of the approaches to tapping the power of high-altitude wind. The main purpose of a ground-based kite power system is to estimate the tether force for autonomous operations. The tether force of a particular kite depends on the wind velocity and the kite’s orientation to the wind vector in the figure-eight trajectory. In this paper, we present an experimental measurement of the pulling force of an Airush Lithium 12 (Formula presented.) kite with a constant tether length of 24 m in a coastal region. We obtain the position and orientation data of the kite from the sensors mounted on the kite. The flight dynamics of the kite are studied using multiple field tests under steady and turbulent wind conditions. We propose a physical model (PM) using Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) deep neural network algorithms to estimate the tether force in the experimental validation. The performance study using the root mean square error (RMSE) method shows that the LSTM model performs better, with overall error values of 126 N and 168 N under steady and turbulent wind conditions. © 2022 by the authors.
Algorithms for Hyperparameter Tuning of LSTMs for Time Series Forecasting
(MDPI, 2023) Dhake, H.; Kashyap, Y.; Kosmopoulos, P.
The rapid growth in the use of Solar Energy for sustaining energy demand around the world requires accurate forecasts of Solar Irradiance to estimate the contribution of solar power to the power grid. Accurate forecasts for higher time horizons help to balance the power grid effectively and efficiently. Traditional forecasting techniques rely on physical weather parameters and complex mathematical models. However, these techniques are time-consuming and produce accurate results only for short forecast horizons. Deep Learning Techniques like Long Short Term Memory (LSTM) networks are employed to learn and predict complex varying time series data. However, LSTM networks are susceptible to poor performance due to improper configuration of hyperparameters. This work introduces two new algorithms for hyperparameter tuning of LSTM networks and a Fast Fourier Transform (FFT) based data decomposition technique. This work also proposes an optimised workflow for training LSTM networks based on the above techniques. The results show a significant fitness increase from 81.20% to 95.23% and a 53.42% reduction in RMSE for 90 min ahead forecast after using the optimised training workflow. The results were compared to several other techniques for forecasting solar energy for multiple forecast horizons. © 2023 by the authors.
Climatological Trends and Effects of Aerosols and Clouds on Large Solar Parks: Application Examples in Benban (Egypt) and Al Dhafrah (UAE)
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Dhake, H.; Kosmopoulos, P.; Mantakas, A.; Kashyap, Y.; El-Askary, H.; Elbadawy, O.
Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting impact over the years. The trends were quantified into the actualised drop in energy production (Wh/m2/year) in order to obtain the impact of each factor. Aerosols displayed a falling trend of ?17.78 Wh/m2/year for Benban and ?44.88 Wh/m2/year for Al Dhafrah. Similarly, clouds also portrayed a largely falling trend for both stations, ?36.29 Wh/m2/year (Benban) and ?70.27 Wh/m2/year (Al Dhafrah). The aerosol and cloud trends were also observed on a monthly basis to study their seasonal variation. The trends were further translated into net increases/decreases in the energy produced and the resulting emissions released. The analysis was extended to quantify the economic impact of the trends. Owing to the falling aerosol and cloud trends, the annual production was foreseen to increase by nearly 1 GWh/year (Benban) and 1.65 GWh/year (Al Dhafrah). These increases in annual production estimated reductions in emission released of 705.2 tonne/year (Benban) and 1153.7 tonne/year (Al Dhafrah). Following these estimations, the projected revenue was foreseen to increase by 62,000 USD/year (Benban) and 100,000 USD/year (Al Dhafrah). Considering the geographical location of both stations, aerosols evidently imparted a larger impact compared with clouds. Severe dust storm events were also analysed at both stations to examine the worst-case scenario of aerosol impact. The results show that the realized losses during these events amounted to 2.86 GWh for Benban and 5.91 GWh for Al Dhafrah. Thus, this study showcases the benefits of Earth observation technology and offers key insights into climatological trends for solar energy planning purposes. © 2024 by the authors.
Enhancing Solar Energy Forecast Using Multi-Column Convolutional Neural Network and Multipoint Time Series Approach
(MDPI, 2023) Kumar, A.; Kashyap, Y.; Kosmopoulos, P.
The rapid expansion of solar industries presents unknown technological challenges. A dedicated and suitable energy forecast is an effective solution for the daily dispatching and production of the electricity grid. The traditional forecast technique uses weather and plant parameters as the model information. Nevertheless, these are insufficient to consider problematic weather variability and the various plant characteristics in the actual field. Considering the above facts and inspired by the excellent implementation of the multi-column convolutional neural network (MCNN) in image processing, we developed a novel approach for forecasting solar energy by transforming multipoint time series (MT) into images for the MCNN to examine. We first processed the data to convert the time series solar energy into image matrices. We observed that the MCNN showed a preeminent response under a ground-based high-resolution spatial–temporal image matrix with a 0.2826% and 0.5826% RMSE for 15 min-ahead forecast under clear (CR) and cloudy (CD) conditions, respectively. Our process was performed on the MATLAB deep learning platform and tested on CR and CD solar energy conditions. The excellent execution of the suggested technique was compared with state-of-the-art deep neural network solar forecasting techniques. © 2022 by the authors.
Exploring the Potential of Kite-Based Wind Power Generation: An Emulation-Based Approach
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Castelino, R.V.; Kumar, P.; Kashyap, Y.; Karthikeyan, A.; Sharma K, M.; Karmakar, D.; Kosmopoulos, P.
A Kite-based Airborne Wind Energy Conversion System (KAWECS) works by harnessing the kinetic energy from the wind and converting it into electric power. The study of the dynamics of KAWECS is fundamental in researching and developing a commercial-scale KAWECS. Testing an actual KAWECS in a location with suitable wind conditions is only sometimes a trusted method for conducting research. A KAWECS emulator was developed based on a Permanent Magnet Synchronous Machine (PMSM) drive coupled with a generator to mimic the kite’s behaviour in wind conditions. Using MATLAB-SIMULINK, three different power ratings of 1 kW, 10 kW, and 100 kW systems were designed with a kite surface area of 2.5 m (Formula presented.), 14 m (Formula presented.), and 60 m (Formula presented.), respectively. The reel-out speed of the tether, tether force, traction power, drum speed, and drum torque were analysed for a wind speed range of 2 m/s to 12.25 m/s. The satellite wind speed data at 10 m and 50 m above ground with field data of the kite’s figure-of-eight trajectories were used to emulate the kite’s characteristics. The results of this study will promote the use of KAWECS, which can provide reliable and seamless energy flow, enriching wind energy exploitation under various installation environments. © 2023 by the authors.
Laboratory-Scale Airborne Wind Energy Conversion Emulator Using OPAL-RT Real-Time Simulator
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kumar, P.; Kashyap, Y.; Castelino, R.V.; Karthikeyan, A.; Sharma K, M.; Karmakar, D.; Kosmopoulos, P.
Airborne wind energy systems (AWES) are more efficient than traditional wind turbines because they can capture higher wind speeds at higher altitudes using connected kite generators. Securing a real wind turbine or a site with favorable wind conditions is not always an assured opportunity for conducting research. Hence, the Research and Development of the Laboratory Scale Airborne Wind Energy Conversion System (LAWECS) require a better understanding of airborne wind turbine dynamics and emulation. Therefore, an airborne wind turbine emulation system was designed, implemented, simulated, and experimentally tested with ground data for the real time simulation. The speed and torque of a permanent magnet synchronous motor (PMSM) connected to a kite are regulated to maximize wind energy harvesting. A field-oriented control technique is then used to control the PMSM’s torque, while a three-phase power inverter is utilized to drive the PMSM with PI controllers in a closed loop. The proposed framework was tested, and the emulated airborne wind energy conversion system results were proven experimentally for different wind speeds and generator loads. Further, the LAWECS emulator simulated a 2 kW, 20 kW, and 60 kW designed with a projected kite area of 5, 25, and 70 square meters, respectively. This system was simulated using the Matlab/Simulink software and tested with the experimental data. Furthermore, the evaluation of the proposed framework is validated using a real-time hardware-in-the-loop environment, which uses the FPGA-based OPAL-RT Simulator. © 2023 by the authors.
Multi-Layer Cloud Motion Vector Forecasting for Solar Energy Applications
(Elsevier Ltd, 2024) Kosmopoulos, P.; Dhake, H.; Melita, N.; Tagarakis, K.; Georgakis, A.; Stefas, A.; Vaggelis, O.; Korre, V.; Kashyap, Y.
Real-time forecasting of solar radiation posses several benefits and has huge potential for industrial applications. However, the intermittent nature of solar radiation makes it difficult to forecast accurately. Cloud cover is one of the major influencing factors of solar radiation. Thus, forecasting cloud motion effectively can help to improve the accuracy of short-term solar radiation forecasts. In this study, a novel Multi-Layer Cloud Motion Vector (referred as 3D-CMV) forecasting technique was introduced, which combined with the fast radiative transfer model (FRTM) produces forecasts up to 3 h ahead at 15 min intervals over 5km × 5km grids across Europe and North Africa. The cloud microphysics obtained from the Support to Nowcasting and Very Short Range Forecasting (SAFNWC) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) was used as input to the forecasting system. The results obtained improvements in forecasts as compared to the conventional cloud motion vector techniques, across all seasons and sky conditions. Comparisons against ground-based measurements from the Baseline Surface Radiation Network (BSRN) revealed an overall maximum percentage difference of less than 12%, bias under -20 Wm−2 and a root mean square error (RMSE) under 80 Wm−2. Performance evaluations of Multi-Layer Cloud Motion Vector has been performed against several state-of-the-art techniques and presented in this study. Short-term solar energy forecasting has an established market and rising demand. Accurate forecasts from Multi-Layer CMV hence pose a high potential for real world applications. © 2023 The Authors
Ray-Tracing modeling for urban photovoltaic energy planning and management
(Elsevier Ltd, 2024) Kosmopoulos, P.; Dhake, H.; Kartoudi, D.; Tsavalos, A.; Koutsantoni, P.; Katranitsas, A.; Lavdakis, N.; Mengou, E.; Kashyap, Y.
The traditional Radiative Transfer Modelling solutions for Solar Energy monitoring and forecasting often provide outputs for a single point location or an area location. However, for high resolution representation of areas these solutions suffer due to low simulation speeds. This approach makes it difficult for decision-makers to accurately estimate the solar potential of the administrative area and plan installations accordingly. In this direction, the study introduces three-dimensional Ray-Tracing based radiative modeling which is a high-speed area-based solution for solar energy monitoring. The three-dimensional ray-tracing was simulated by using advanced graphic creation platforms and cloud computing in conjunction with satellite data of the clouds, aerosols, building shadows effects and three-dimensional representations of the city using Cesium 3D tiles and Unreal Engine ®. The entire system was developed in a hybrid model to be exploited by urban planners for solar PV installations and by electricity distribution system operators for energy management and efficient incorporation of the produced energy into the regular and smart grids. This study implements and analyses this Ray-Tracing model for solar photovoltaic energy potential estimation at a rooftop level for the city of Athens, Greece. The total rooftop exploitable area in Athens was found to be close to 34 km2, which is able to massively host distributed PVs followed by almost 4.3 TWh of annually produced energy, whilst Penteli (a Municipality in Athens) possessed a potential of 96.8 GWh with an exploitable area of just 0.8 km2. This amount of energy, in a hypothetical full coverage scenario, is able to provide for 48.7% of Athens's total energy requirement. Similar year-long simulations were conducted using the EU's largest rooftop solar installation at Stavros Niarchos Foundation Cultural Center and randomly selected rooftops having solar installations in different municipalities in Athens. With these estimated solar potential values, the gross savings in natural gas consumption and hence the CO2 equivalent emissions can be computed. With the current estimated solar potential of Athens, the analyzed savings accounted of nearly 2.43 billion euros and 18 MT CO2 equivalent emissions. These computed annual savings are capable of covering installation costs for nearly 100,000 new solar installations. The end-product of this study is the development of a solar cadastre web tool which will support the decision-makers in the energy transition policies and the solar PV penetration into the urban environment and eventually drive the effort into renewable energy transition across the globe. © 2024 The Author(s)
Rooftop Photovoltaic Energy Production Estimations in India Using Remotely Sensed Data and Methods
(MDPI, 2023) Kumar, A.; Kosmopoulos, P.; Kashyap, Y.; Gautam, R.
We investigate the possibility of estimating global horizontal irradiance (GHI) in parallel to photovoltaic (PV) power production in India using a radiative transfer model (RTM) called libRadtran fed with satellite information on the cloud and aerosol conditions. For the assessment of PV energy production, we exploited one year’s (January–December 2018) ground-based real-time measurements of solar irradiation GHI via silicon irradiance sensors (Si sensor), along with cloud optical thickness (COT). The data used in this method was taken from two different sources, which are EUMETSAT’s Meteosat Second Generation (MSG) and aerosol optical depth (AOD) from Copernicus Atmospheric Monitoring Services (CAMS). The COT and AOD are used as the main input parameters to the RTM along with other ones (such as solar zenith angle, Ångström exponent, single scattering albedo, etc.) in order to simulate the GHI under all sky, clear (no clouds), and clear-clean (no clouds and no aerosols) conditions. This enabled us to quantify the cloud modification factor (CMF) and aerosol modification factor (AMF), respectively. Subsequently, the whole simulation is compared with the actual recorded data at four solar power plants, i.e., Kazaria Thanagazi, Kazaria Ceramics, Chopanki, and Bhiwadi in the Alwar district of Rajasthan state, India. The maximum monthly average attenuation due to the clouds and aerosols are 24.4% and 11.3%, respectively. The energy and economic impact of clouds and aerosols are presented in terms of energy loss (EL) and financial loss (FL). We found that the maximum EL in the year 2018 due to clouds and aerosols were 458 kWh m−2 and 230 kWh m−2, respectively, observed at Thanagazi location. The results of this study highlight the capabilities of Earth observations (EO), in terms not only of accuracy but also resolution, in precise quantification of atmospheric effect parameters. Simulations of PV energy production using EO data and techniques are therefore useful for real-time estimates of PV energy outputs and can improve energy management and production inspection. Success in such important venture, energy management, and production inspections will become much easier and more effective. © 2023 by the authors.
Rooftop photovoltaic energy production management in india using earth-observation data and modeling techniques
(MDPI AG rasetti@mdpi.com Postfach Basel CH-4005, 2020) Masoom, A.; Kosmopoulos, P.; Kashyap, Y.; Kumar, S.; Bansal, A.
This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations. © 2020 by the authors.
Solar Photovoltaic Hotspot Inspection Using Unmanned Aerial Vehicle Thermal Images at a Solar Field in South India
(MDPI, 2023) Umesh, P.; Kashyap, Y.; Baxevanaki, E.; Kosmopoulos, P.
The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each other, create solar panels, which in their turn, are connected in a solar string, and they create solar farms. These structures are extremely efficient in electricity production, but also, cells are fragile in nature and delicate to environmental conditions, which is the reason why some of them show discrepancies and are called defective. In this research, a thermal camera mounted on a drone has been used for the first time in the solar farm operating conditions of India in order to capture images of the solar field and investigate solar panels for defective cells and create an orthomosaic image of the entire area. This procedure next year will be established on an international scale as a best practice example for commercialization, providing effortless photovoltaic monitoring and maintenance planning. For this process, an open source software WebODM has been used, and the entire field was digitized so as to identify the location of defective panels in the field. This software was the base in order to provide and analyze a digital twin of the studied area and the included photovoltaic panels. The defects on solar cells were identified with the use of thermal bands, which record and point out their temperature of them, whereas anomalies in the detected temperature in defective solar cells were captured using thermal electromagnetic waves, and these areas are mentioned as hotspots. In this research, a total number of 232.934 solar panels were identified, and 2481 defective solar panels were automatically indicated. The majority of the defects were due to manufacturing failure and normal aging, but also due to persistent shadowing and soiling from aerosols and especially dust transport, as well as from extreme weather conditions, including hail. The originality of this study relies on the application of the proposed under development technology to the specific conditions of India, including high photovoltaic panels wear rates due to extreme aerosol loads (India presents one of the highest aerosol levels worldwide) and the monsoon effects. The ability to autonomously monitor solar farms in such conditions has a strong energy and economic benefit for production management and for long-term optimization purposes. © 2023 by the authors.
Tether Force Estimation Airborne Kite Using Machine Learning Methods
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Gupta, A.; Kashyap, Y.; Kosmopoulos, P.
This paper explores the potential of Airborne Wind Energy Systems to revolutionize wind energy generation, demonstrating advancements over current methods. Through a series of controlled field experiments and the application of classical machine learning techniques, we achieved significant improvements in tether force estimation. Our XGBoost model, for example, demonstrated a notable reduction in error in predicting the tether force that can be extracted at a particular location, with a root mean square error of 52.3 Newtons and a mean absolute error of 32.1 Newtons, coupled with a (Formula presented.) error, which measures the proportion of variance explained by the model, achieved an impressive value of 0.93. These findings not only validate the effectiveness of our proposed methods but also illustrate their potential to optimize the deployment of Airborne Wind Energy Systems, thereby maximizing energy output and contributing to a sustainable, low-carbon energy future. By analyzing key input features such as wind speed and kite dynamics, our model predicts optimal locations for Airborne Wind Energy System installation, offering a promising alternative to traditional wind turbines. © 2025 by the authors.