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Category Archives: Data
A fuel cell converts the chemical energy from a fuel into electricity, heat and water through a chemical reaction with oxygen. Hydrogen is the most common fuel and is produced from the steam methane reforming of natural gas, but for greater efficiency hydrocarbons can be used directly, be it natural gas, gasoline, or alcohols like methanol.
Since fuel cells rely on an electrochemical process and not combustion, emissions from fuel cells are significantly lower than emissions from even the cleanest fuel combustion processes.
Energy Content Explained
The energy content of any organic fuel is defined as the fuel’s primary energy. Primary energy is measured given the fuels calorific value or the heat generation from the complete combustion of one unit of fuel under well-defined conditions. The calorific value can be a gross or net number, depending on whether the combustible heat released takes into account the vapor condensation of water. Power production efficiency is typically calculated using Net Calorific Value (NCV) after water vaporization.
Solar radiation data is best obtained direct from sensors, be it ground stations, satellite observation networks, or modeled solutions that combine both. To this end, solar data is available from various databases, modeling projects, and commercial venders….all with varying degrees of quality.
Introduction to Renewable Generation
Renewable generation includes sources of energy that renew themselves constantly through natural processes and will never run out during normal human time-scales. Renewable energies come from three primary sources: the Sun, heat from the inner Earth, and tidal power. The Sun, in turn fuels the wind and, indirectly, most biomass resources.
Thermal Generation Efficiencies
Thermal generation relies on fossil fuels and renewable fuel sources like biomass, biogass, waste=to-energy and geothermal. The following section indicates thermal efficiency ranges for converting primary energy into electricity based on standard market and state-of-the-art equipment. The efficiency values reported do not include losses attributed to plant availability such as planned maintenance, unforced outages, and grid curtailments.
Weather data sources are presented that were collated to support wind and solar resource assessment, engineering design, and power system monitoring. Data sources include ground stations, satellite observation networks, reanalysis data, forecasts systems, and aerosol models.
All links and content have been extracted from data source websites to facilitate ease of access to data servers. Please contribute if you find links have changed or data product definitions should be updated.
Intro to Aerosol Data
Aerosol data can be obtained from several sources. It is important to point out that aerosol data has many uncertainties. Measuring atmospheric aerosols is a challenge due to the large range in particle size, shape, concentration and composition. No single instrument can measure all the aerosol properties, and detection methods of the smallest sub-3 nm particles are still developing.
Intro to Reanalysis Data
Reanalysis is a scientific method for developing a comprehensive and consistent record of how the weather and climate are changing over time. Reanalysis data relies on the assimalation of sensor data from multiple sources and numerical weather prediction models to produce a continually updated, gridded data set that describes the state of the Earth’s atmosphere at difference points in time and space. Gridded reanalysis data is available from different sources. The different data sources vary with respect to
Weather and atmospheric forecast data can be obtained from the following forecast systems:
Introduction to Satellite Observation Networks
Satellite observation networks provide invaluable data on the climate and the layered atmosphere. Space satellite data is a key input to assess the feasibility and operational integrity of renewable energy power systems.
Ground station sensors for weather and climate observation are listed below. The list is limited to station networks that that provide verification of wind and solar resource data.
For the first time, U.S. wind and solar production in March exceeded 10% of total electricity generation, based on March data in EIA’s Electric Power Monthly.
The record contribution for non-hydro renewables comes amid surging installations of both wind and solar in the US, with 14.8GW of solar and 8.2GW of wind added in 2016. On an annual basis, wind and solar made up 7% of total U.S. electric generation.
A new method to extract data tables from PDF files is introduced. Most of the data scraping tools available are browser-based. The common tools are also manual in nature and limited to one table at a time. A solution is outlined to extract multiple tables at once. The solution combines the R programming language with the open-source Java program Tabula. The result is a convenient method that transforms documents into databases.
The ability to train a machine to extract data tables from PDF files has several benefits:
A common task in spatial data analysis is extracting SpatialPoints inside a set of polygons or buffer zones. Analysts can use standard GIS or map tools to extract a set of points within an area of interest using manual “point-and-click” routines. This method is easy, but will probably prove impractical, especially in cases involving big data. The alternative is to train a machine to automatically extract the points in a polygon or buffer zone. This post achieves that task and presents a case-study with R code.
Wind and solar capacity are expanding rapidly and each is well on track to pass nuclear power capacity. Advocates of nuclear energy have long been predicting its renaissance, but the installation of nuclear capacity appears to be stalled with little to no change in recent years. Wind energy, by contrast, will have more capacity installed than nuclear by 2015 and solar energy capacity is likely to pass nuclear prior to 2020.