scholarly journals Solar Power System Planning and Design

2020 ◽  
Vol 10 (1) ◽  
pp. 367
Author(s):  
Yosoon Choi
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Greenhouse Gas ◽  
Energy Production ◽  
Energy Systems ◽  
Environmental Safety ◽  
System Planning ◽  
Renewable Energy Systems ◽  
Planning And Design ◽  
Production Techniques

With growing concerns about greenhouse gas emissions, the security of conventional energy supplies, and the environmental safety of conventional energy production techniques, renewable energy systems are becoming increasingly important and are receiving much political attention [...]

scholarly journals Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 499
Author(s):  
Sebastian Klaudiusz Tomczak ◽  
Anna Skowrońska-Szmer ◽  
Jan Jakub Szczygielski
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Manufacturing Sector ◽  
Energy Systems ◽  
Product Portfolio ◽  
New Approach ◽  
Renewable Energy Systems ◽  
Pv Systems ◽  
Depth Analysis ◽  
A Company

In an era of increasing energy production from renewable sources, the demand for components for renewable energy systems has dramatically increased. Consequently, managers and investors are interested in knowing whether a company associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is a money-making business. We apply a new approach that extends prior research by applying decision trees (DTs) to identify ratios (i.e., indicators), which discriminate between companies within the sector that do (designated as “green”) and do not (“red”) produce elements of PV systems. Our results indicate that on the basis of selected ratios, green companies can be distinguished from the red companies without an in-depth analysis of the product portfolio. We also find that green companies, especially operating in China are characterized by lower financial performance, thus providing a negative (and unexpected) answer to the question posed in the title.

Spatio-temporal ensemble predictions for wind and solar energy combining dispersion modelling methods and machine learning

2021 ◽  
Author(s):  
Irene Schicker ◽  
Petrina Papazek ◽  
Elisa Perrone ◽  
Delia Arnold
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Renewable Energy ◽  
Random Forest ◽  
Energy Production ◽  
Energy Systems ◽  
Processing Algorithm ◽  
Post Processing ◽  
Processing Methods ◽  
Renewable Energy Systems

<p>With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production,  maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area.</p><p>These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. </p><p>In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.</p>

Electronic Structure of h-WO3 and CuWO4 Nanocrystals, Harvesting Materials for Renewable Energy Systems and Functional Devices

2011 ◽  
Vol 110-116 ◽  
pp. 2188-2193 ◽  
Author(s):  
V.V. Atuchin ◽  
I.B. Troitskaia ◽  
O.Yu. Khyzhun ◽  
V.L. Bekenev ◽  
Yu.M. Solonin
Keyword(s):  
Electronic Structure ◽  
Renewable Energy ◽  
Valence Band ◽  
Photoelectron Spectroscopy ◽  
Emission Spectroscopy ◽  
Energy Production ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
X Ray ◽  
Valence Band Region

— The electronic structure of hexagonal WO3 and triclinic CuWO4 nanocrystals, prospective materials for renewable energy production and functional devices, has been studied using the X-ray photoelectron spectroscopy (XPS) and X-ray emission spectroscopy (XES) methods. The present XPS and XES results render that the W 5d-and O 2p-like states contribute throughout the whole valence-band region of the h-WO3 and CuWO4 nanocrystalline materialls, however maximum contributions of the O 2p-like states occur in the upper, whilst the W 5d-like states in the lower portions of the valence band, respectively.

scholarly journals Selection of the Most Sustainable Renewable Energy System for Bozcaada Island: Wind vs. Photovoltaic

2019 ◽  
Vol 11 (15) ◽  
pp. 4098 ◽  
Author(s):  
Elif Oğuz ◽  
Ayşe Eylül Şentürk
Keyword(s):  
Renewable Energy ◽  
Life Cycle ◽  
Production Process ◽  
Life Cycle Cost ◽  
Energy Production ◽  
Economic Impacts ◽  
Unit Cost ◽  
Energy Systems ◽  
Renewable Energy Systems ◽  
Photovoltaic Power

Energy production without destroying the environment has been one of the most crucial issues for people living in today’s world. In order to analyze whole environmental and/or economic impacts of the energy production process, life cycle assessment (LCA) and life cycle cost (LCC) are widely used. In this study, two distinct renewable energy systems are assessed. First, a land-based wind farm, which has been operating in Bozcaada Island since 2000, is compared to a proposed solar photovoltaic power plant in terms of Energy Pay-Back Time (EPBT) periods and greenhouse gas (GHG) emissions and life cycle cost. The energy production process including the recycling phase evaluated “from cradle to grave” using GaBi software for both cases. All scenarios are compared by considering different impact categories such as global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP). Following this, levelized unit cost to produce 1 MWh electricity (LUCE) is calculated for both systems. This study revealed that LCA and LCCA are useful and practical tools that help to determine drawbacks and benefits of different renewable energy systems considering their long-term environmental and economic impacts. Our findings show that onshore wind farms have a number of benefits than proposed photovoltaic power plants in terms of environmental and cost aspects.

scholarly journals Synthesis, structural and electrochemical properties of sodium nickel phosphate for energy storage devices

Nanoscale ◽  
2016 ◽  
Vol 8 (21) ◽  
pp. 11291-11305 ◽  
Author(s):  
Manickam Minakshi ◽  
David Mitchell ◽  
Rob Jones ◽  
Feraih Alenazey ◽  
Teeraphat Watcharatharapong ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Large Scale ◽  
Low Cost ◽  
Energy Systems ◽  
Energy Storage Devices ◽  
Renewable Energy Systems ◽  
Storage Devices ◽  
Nickel Phosphate ◽  
And Storage

Electrochemical energy production and storage at large scale and low cost, is a critical bottleneck in renewable energy systems.

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